This guide explores some of the most common AI and privacy concerns that businesses face. Additionally, it identifies potential solutions and best practices organizations can pursue to achieve better outcomes for their customers, their reputation, and their bottom line.

Major Issues with AI and Privacy

Given that the role of artificial intelligence has grown so rapidly, it’s not surprising that issues like unauthorized incorporation of user data, unclear data policies, and limited regulatory safeguards have created significant issues with AI and privacy.

Unauthorized Incorporation of User Data

When users of AI models input their own data in the form of queries, there’s the possibility that this data will become part of the model’s future training dataset. When this happens, this data can show up as outputs for other users’ queries, which is a particularly big issue if users have input sensitive data into the system.

In a now-famous example, three different Samsung employees leaked sensitive company information to ChatGPT that could now possibly be part of ChatGPT’s training data. Many vendors, including OpenAI, are cracking down on how user inputs are incorporated into future training. But there’s still no guarantee that sensitive data will remain secure and outside of future training sets.

Unregulated Usage of Biometric Data

A growing number of personal devices use facial recognition, fingerprints, voice recognition, and other biometric data security instead of more traditional forms of identity verification. Public surveillance devices are also beginning to use AI to scan for biometric data so individuals can be identified quickly.

While these new biometric security tools are incredibly convenient, there’s limited regulation focused on how AI companies can use this data once it’s collected. In many cases, individuals don’t even know that their biometric data has been collected, much less that it is being stored and used for other purposes.

Covert Metadata Collection Practices

When a user interacts with an ad, a TikTok or other social media video, or pretty much any web property, metadata from that interaction—as well as the person’s search history and interests—can be stored for more precise content targeting in the future.

This method of metadata collection has been going on for years, but with the help of AI, more of that data can be collected and interpreted at scale, making it possible for tech companies to further target their messages at users without their knowledge of how it works. While most user sites have policies that mention these data collection practices and/or require users to opt in, it’s mentioned only briefly and in the midst of other policy text, so most users don’t realize what they’ve agreed to. This veiled metadata collection agreement subjects users and everything on their mobile devices to scrutiny.

Limited Built-In Security Features for AI Models

While some AI vendors may choose to build baseline cybersecurity features and protections into their models, many AI models do not have native cybersecurity safeguards in place. Even the AI technologies that do have basic safeguards rarely come with comprehensive cybersecurity protections. This is because taking the time to create a safer and more secure model can cost AI developers significantly, both in time to market and overall development budget.

Whatever the reason, AI developers’ limited focus on security and data protection makes it much easier for unauthorized users and bad-faith actors to access and use other users’ data, including personally identifiable information (PII).

Extended and Unclear Data Storage Policies

Few AI vendors are transparent about how long, where, and why they store user data. The vendors who are often store data for lengthy periods of time, or use it in ways that clearly do not prioritize privacy.

For example, OpenAI’s privacy policy says it can “provide Personal Information to vendors and service providers, including providers of hosting services, customer service vendors, cloud services, email communication software, web analytics services, and other information technology providers, among others. Pursuant to our instructions, these parties will access, process, or store Personal Information only in the course of performing their duties to us.”

In this case, several types of companies can gain access to your ChatGPT data for various reasons as determined by OpenAI. It’s especially concerning that “among others” is a category of vendors that can collect and store your data, as there’s no information about what these vendors do or how they might choose to use or store your data.

OpenAI’s policy provides additional information about what data is typically stored and what your privacy rights are as a consumer. You can access your data and review some information about how it’s processed, delete your data from OpenAI records, restrict or withdraw information processing consent, and/or submit a formal complaint to OpenAI or local data protection authorities.

This more comprehensive approach to data privacy is a step in the right direction, but the policy still contains certain opacities and concerning elements, especially for Free and Plus plan users who have limited control over or visibility into how their data is used.

Little Regard for Copyright and IP Laws

AI models pull training data from all corners of the web. Unfortunately, many AI vendors either don’t realize or don’t care when they use someone else’s copyrighted artwork, content, or other intellectual property without their consent.

Major legal battles have focused on AI image generation vendors like Stability AI, Midjourney, DeviantArt, and Runway AI. It is alleged that several of these tools scraped artists’ copyrighted images from the internet without permission. Some of the vendors defended their action by citing a lack of laws that prevent them from following this process for AI training.

The problems of using unauthorized copyrighted products and IP grow much worse as AI models are trained, retrained, and fine-tuned with this data over time. Many of today’s AI models are so complex that even their builders can’t confidently say what data is being used, where it came from, and who has access to it.

Limited Regulatory Safeguards

Some countries and regulatory bodies are working on AI regulations and safe use policies, but no overarching standards are officially in place to hold AI vendors accountable for how they build and use artificial intelligence tools. The proposed regulation closest to becoming law is the EU AI Act, expected to be published in the Official Journal of the European Union in summer of 2024. Some aspects of the law will take as long as three years to become enforceable.

With such limited regulation, a number of AI vendors have come under fire for IP violations and opaque training and data collection processes, but little has come from these allegations. In most cases, AI vendors decide their own data storage, cybersecurity, and user rules without interference.

How Data Collection Creates AI Privacy Issues

Unfortunately, the total number and variety of ways that data is collected all but ensures that this data will find its way into some irresponsible uses. From Web scraping to biometric technology to IoT sensors, modern life is essentially lived in service of data collection efforts.

Web Scraping Harvests a Wide Net

Because web scraping and crawling require no special permissions and enable vendors to collect massive amounts of varied data, AI tools often rely on these practices to quickly build training datasets at scale. Content is scraped from publicly available sources on the internet, including third-party websites, wikis, digital libraries, and more. In recent years, user metadata is also increasingly pulled from marketing and advertising datasets and websites with data about targeted audiences and what they engage with most.

User Queries in AI Models Retain Data

When a user inputs a question or other data into an AI model, most AI models store that data for at least a few days. While that data may never be used for anything else, many artificial intelligence tools collect that data and hold onto it for future training purposes.

Biometric Technology Can Be Intrusive

Surveillance equipment—including security cameras, facial and fingerprint scanners, and microphones—can all be used to collect biometric data and identify humans without their knowledge or consent. State by state, rules are getting stricter in the U.S. regarding how transparent companies need to be when using this kind of technology. However, for the most part, they can collect this data, store it, and use it without asking customers for permission.

IoT Sensors and Devices Are Always On

Internet of Things (IoT) sensors and edge computing systems collect massive amounts of moment-by-moment data and process that data nearby to complete larger and quicker computational tasks. AI software often takes advantage of an IoT system’s detailed database and collects its relevant data through methods like data learning, data ingestion, secure IoT protocols and gateways, and APIs.

APIs Interface With Many Applications

APIs give users an interface with different kinds of business software so they can easily collect and integrate different kinds of data for AI analysis and training. With the right API and setup, users can collect data from CRMs, databases, data warehouses, and both cloud-based and on-premises systems. Given how few users pay attention to the data storage and use policies their software platforms follow, it’s likely many users have had their data collected and applied to different AI use cases without their knowledge.

Public Records Are Easy Accessed

Whether records are digitized or not, public records are often collected and incorporated into AI training sets. Information about public companies, current and historical events, criminal and immigration records, and other public information can be collected with no prior authorization required.

User Surveys Drives Personalization

Though this data collection method is more old-fashioned, using surveys and questionnaires are still a tried-and-true way that AI vendors collect data from their users. Users may answer questions about what content they’re most interested in, what they need help with, how their most recent experience with a product or service was, or any other question that gives the AI a better idea about how to personalize interactions.

Emerging Trends in AI and Privacy

Because the AI landscape is evolving so rapidly, the emerging trends shaping AI and privacy issues are also changing at a remarkable pace. Among the leading trends are major advances in AI technology itself, the rise of regulations, and the role of public opinion on AI’s growth.

Advancements in AI Technologies

AI technologies have exploded in terms of technology sophistication, use cases, and public interest and knowledge. This growth has happened with more traditional AI and machine learning technologies but also with generative AI.

Generative AI’s large language models (LLMs) and other massive-scale AI technologies are trained on incredibly large datasets, including internet data and some more private or proprietary datasets. While the data collection and training methodologies have improved, AI vendors and their models often are not transparent in their training or the algorithmic processes they use to generate answers.

To address this issue, many generative AI companies in particular have updated their privacy policies and their data collection and storage standards. Others, such as Anthropic and Google, have worked to develop and release clear research that illustrates how they are working to incorporate more explainable AI practices into their AI models, which improves transparency and ethical data usage across the board.

Impact of AI on Privacy Laws and Regulations

Most privacy laws and regulations do not yet directly address AI and how it can be used or how data can be used in AI models. As a result, AI companies have had a lot of freedom to do what they want. This has led to ethical dilemmas like stolen IP, deepfakes, sensitive data exposed in breaches or training datasets, and AI models that seem to act on hidden biases or malicious intent.

More regulatory bodies—both governmental and industry-specific—are recognizing the threat AI poses and developing privacy laws and regulations that directly address AI issues. Expect more regional, industry-specific, and company-specific regulations to come into play in the coming months and years, with many of them following the EU AI Act as a blueprint for how to protect consumer privacy.

Public Perception and Awareness of AI Privacy Issues

Since ChatGPT was released, the general public has developed a basic knowledge of and interest in AI technologies. Despite the excitement, general public perception of AI technology is fearful—especially as it relates to AI privacy.

Many consumers do not trust the motivations of big AI and tech companies and worry that their personal data and privacy will be compromised by the technology. Frequent mergers, acquisitions, and partnerships in this space can lead to emerging monopolies, and the fear of the power these organizations have.

According to a survey completed by the International Association of Privacy Professionals in 2023, 57 percent of consumers fear that AI is a significant threat to their privacy, while 27 percent felt neutral about AI and privacy issues. Only 12 percent disagreed that AI will significantly harm their personal privacy.

Real-World Examples of AI and Privacy Issues

While there have been several significant and highly publicized security breaches with AI technology and its respective data, many vendors and industries are taking important strides in the direction of better data protections. We cover both failures and success in the following examples.

High-Profile Privacy Issues Involving AI

Here are some of the most major breaches and privacy violations that directly involved AI technology over the past several years:

• Microsoft: Its recent announcement of the Recall feature, which allows business leaders to collect, save, and review user-activity screenshots from their devices, received significant pushback for its lack of privacy design elements, as well as for the company’s recent problems with security breaches. Microsoft will now let users more easily opt in or out of the process, and plans to improve data protection with just-in-time decryption and encrypted search index databases.
• OpenAI: OpenAI experienced its first major outage in March 2023 as a result of a bug that exposed certain users’ chat history data to other users, and even exposed payment and other personal information to unauthorized users for a period of time.
• Google: An ex-Google employee stole AI trade secrets and data to share with the People’s Republic of China. While this does not necessarily impact personal data privacy, the implications of AI and tech companies’ employees being able to get this kind of access are concerning.

Successful Implementations of AI with Strong Privacy Protections

Many AI companies are innovating to create privacy-by-design AI technologies that benefit both businesses and consumers, including the following:

• Anthropic: Especially with its latest Claude 3 model, Anthropic has continued to grow its constitutional AI approach, which enhances model safety and transparency. The company also follows a responsible scaling policy to regularly test and share with the public how its models are performing against biological, cyber, and other important ethicality metrics.
• MOSTLY AI: This is one of several AI vendors that has developed comprehensive technology for synthetic data generation, which protects original data from unnecessary use and exposure. The technology works especially well for responsible AI and ML development, data sharing, and testing and quality assurance.
• Glean: One of the most popular AI enterprise search solutions on the market today, Glean was designed with security and privacy at its core. Its features include zero trust security and a trust layer, user authentication, the principle of least privilege, GDPR compliance, and data encryption at rest and in transit.
• Hippocratic AI: This generative AI product, specifically designed for healthcare services, complies with HIPAA and has been received extensively by nurses, physicians, health systems, and payor partners to ensure data privacy is protected and patient data is used ethically to deliver better care.
• Simplifai: A solution for AI-supported insurance claims and document processing, Simplifai explicitly follows a privacy-by-design approach to protect its customers’ sensitive financial data. Its privacy practices include data masking, limited storage times and regular data deletion; built-in platform, network, and data security components and technology; customer-driven data deletion; data encryption; and the use of regional data centers that comply with regional expectations.

Best Practices for Managing AI and Privacy Issues

While AI presents an array of challenging privacy issues, companies can surmount these concerns by using best practices like focusing on data governance, establishing appropriate use policies and educating all stakeholders.

Invest in Data Governance and Security Tools

Some of the best solutions for protecting AI tools and the rest of your attack surface include extended detection and response (XDR), data loss prevention, and threat intelligence and monitoring software. A number of data-governance-specific tools also exist to help you protect data and ensure all data use remains in compliance with relevant regulations.

Establish an Appropriate Use Policy for AI

Internal business users should know what data they can use and how they should use it when engaging with AI tools. This is particularly important for organizations that work with sensitive customer data, like protected health information (PHI) and payment information.

Read the Fine Print

AI vendors typically offer some kind of documentation or policy that covers how their products work and the basics of how they were trained. Read this documentation carefully to identify any red flags, and if there’s something you’re not sure about or that’s unclear in their policy docs, reach out to a representative for clarification.

Use Only Non-Sensitive Data

As a general rule, do not input your business’s or customers’ most sensitive data in any AI tool, even if it’s a custom-built or fine-tuned solution that feels private. If there’s a particular use case you want to pursue that involves sensitive data, research if there’s a way to safely complete the operation with digital twins, data anonymization, or synthetic data.

Educate Stakeholders and Users on Privacy

Your organization’s stakeholders and employees should receive both general training and role-specific training for how, when, and why they can use AI technologies in their daily work. Training should be an ongoing initiative that focuses on refreshing general knowledge and incorporating information about emerging technologies and best practices.

Enhance Built-In Security Features

When developing and releasing AI models for more general use, you must put in the effort to protect user data at all stages of model lifecycle development and optimization. To improve your model’s security features, focus heavily on data, increasing practices like data masking, data anonymization, and synthetic data usage; also consider investing in more comprehensive and modern cybersecurity tool sets for protection, such as extended detection and response (XDR) software platforms.

Proactively Implement Stricter Regulatory Measures

The EU AI Act and similar overarching regulations are on the horizon, but even before these laws go into effect, AI developers and business leaders should regulate how AI models and data are used. Set and enforce clear data usage policies, provide avenues for users to share feedback and concerns, and consider how AI and its needed training data can be used without compromising industry-specific regulations or consumer expectations.

Improve Transparency in Data Usage

Increasing data usage transparency—which includes being more transparent about data sources, collection methods, and storage methods—is a good business practice all-around. It gives customers greater confidence when using your tools, it provides the necessary blueprints and information AI vendors need to pass a data or compliance audit, and it helps AI developers and vendors to create a clearer picture of what they’re doing with AI and the roadmap they plan to follow in the future.

Reduce Data Storage Periods

The longer data is stored in a third-party repository or AI model (especially one with limited security protections), the more likely that data will fall victim to a breach or bad actor. The simple act of reducing data storage periods to only the exact amount of time that’s necessary for training and quality assurance will help to protect data against unauthorized access and give consumers greater peace of mind when they discover this reduced data storage policy is in place.

Ensure Compliance with Copyright and IP Laws

While current regulations for how AI can incorporate IP and copyrighted assets are murky at best, AI vendors will improve their reputation (and be better prepared for impending regulations) if they vet their sources from the outset. Similarly, business users of AI tools should be diligent in reviewing the documentation AI vendors provide about how their data is sourced; if you have questions or concerns about IP usage, you should contact that vendor immediately or even stop using that tool.

Bottom Line: Addressing AI and Privacy Issues Is Essential

AI tools present businesses and the everyday consumer with all kinds of new conveniences, ranging from task automation and guided Q&A to product design and programming. While these tools can simplify our lives, they also run the risk of violating individual privacy in ways that can damage vendor reputation and consumer trust, cybersecurity, and regulatory compliance.

It takes extra effort to use AI in a responsible way that protects user privacy, yet it’s essential when you consider how privacy violations can impact a company’s public image. Especially as this technology matures and becomes more pervasive in our daily lives, it’s essential to follow AI laws as they’re passed and develop more specific AI use best practices that align with your organization’s culture and customers’ privacy expectations.

For additional tips related to cybersecurity, risk management, and ethical AI use when it comes to generative AI, check out these best practice guides:

• Generative AI Ethics: Concerns and Possible Solutions
• Generative AI and Cybersecurity: Ultimate Guide
• Risks of Generative AI: 6 Risk Management Tips

The post AI and Privacy Issues: Challenges, Solutions, and Best Practices appeared first on eWEEK.

I’ve analyzed various artificial intelligence software solutions for different use cases — primarily focusing on business scenarios — to help you determine the best and most relevant AI applications for your needs. This list considers software from AI companies that serve both technical and non-technical teams. Let’s take a look.

Comparison Chart: Top AI Software

The top AI software solutions on the market today cover a broad spectrum of industries and specific use cases. To provide a point of comparison across these categories, we’ve summed up the key characteristics and strengths of each of our top choices in the table below.

7 Best AI Software for Model Building & Governance

Cloud and other technology companies are racing to develop AI tools and solutions both for their customers and internal use cases. To meet these goals, a growing number of business users are investing in AI software that helps them build their own models, manage their training data, and govern all aspects of the modeling lifecycle for better outcomes. These are the best solutions I’ve found for model building and governance:

Azure Machine Learning Studio

Best for Data Scientists & Azure Users

Azure Machine Learning Studio is an AI solution designed to help ML engineers and data scientists train and deploy models and manage the MLOps lifecycle. With this tool, users can create a model in Azure Machine Learning or use a model built from an open-source platform, such as PyTorch, TensorFlow, or scikit-learn. They can also design and build custom models and algorithms to gain insights from data, deploy them in production, and monitor the models’ analytics results as they evolve.

Azure Machine Learning Studio offers several features that simplify data science and machine learning. These include automated machine learning, model management, and interactive visualizations. The Studio version of the tool is designed to be user-friendly and low-code/no-code, so in some ways, its capabilities are limited; the greater Azure Machine Learning environment offers support for more complex use cases and experienced developers who want to go further with their models in Azure.

Pros & Cons

Pricing

Azure Machine Learning Studio (classic) is available in two tiers: Free and Standard.

The standard plan costs $9.99 per ML studio workspace per month, or $1 per studio experimentation hour.

Production Web API pricing works like this:

• Dev/Test: $0 per month.
• Standard S1: $100.13 per month plus overage rates of $0.50 per 1,000 transactions and $2 per API compute hour.
• Standard S2: $1,000.06 per month plus overage rates of $0.25 per 1,000 transactions and $1.50 per API compute hour.
• Standard S3: $9,999.98 per month plus overage rates of $0.10 per 1,000 transactions and $1 per API compute hour.

Key Features

• Data labeling and preparation capability.
• Automated machine learning with monitoring and analysis.
• Drag-and-drop designer.
• Open-source libraries and frameworks.
• Hybrid and multi-cloud model training and deployment.

Databricks Data Intelligence Platform

Best for Collaborative Data Workflow Management

The Databricks Data Intelligence Platform is an innovative solution that combines the strengths of Databricks’ data lakehouse storage infrastructure with several different AI and data workflow management features, including a comprehensive layer of AI, data, and analytics governance capabilities. Many enterprise teams are opting for this solution to develop and manage all aspects of AI technology for their businesses, as it is one of the best tools on the market for prepping and getting to know your data at all levels.

Built on the Databricks open data lakehouse foundation, the Data Intelligence Platform combines the strengths of a lakehouse with generative AI to create its trademark Data Intelligence Engine, which quickly learns and operates in your organization’s data style and lingo. The platform also includes a range of capabilities to support business intelligence, ETL, data warehousing, data analytics, orchestration, and data science. Most recently, Databricks updated the platform with a new addition: DBRX, an open LLM that is now available to all users and is competitive with tools like GPT-3.5.

Pros & Cons

Pricing

The Databricks platform itself appears to be “free,” but to actually do anything with it, you’ll need to pay for DBU-based product usage. Pricing looks like this, but can be variable, depending on selected cloud, region, volume discount privileges, and other factors:

• Workflows: Starting at $0.15 per DBU.
• Delta Live Tables: Starting at $0.20 per DBU.
• Databricks SQL: Starting at $0.22 per DBU.
• Interactive Workloads: Starting at $0.40 per DBU.
• Mosaic AI Training and Serving: Starting at $0.07 per DBU.

Key Features

• Databricks Unity Catalog for unified governance layer that covers data and AI.
• Mosaic AI for AI lifecycle management.
• Vector search and feature engineering.
• Platform built on data lakehouse.
• Model creation, tuning, and deployment.

Amazon SageMaker

Best for AI & ML Model Deployment

Amazon SageMaker is an ML-focused platform from one of the largest and most successful tech companies in the world. With its impressive network of third-party partners and solutions, as well as the tools and capabilities directly available from Amazon and AWS, users can effectively manage the model lifecycle through deployment in several different formats, including edge device, geospatial-data-driven, and embedded AI deployment.

SageMaker includes a diverse array of tools and features to help users prepare their data, build out their models, train models, deploy models, and govern their models, all with supportive features for end-to-end ML. It is one of the best platforms for dedicated deployment support. The platform’s dedicated deployment capabilities include Model Deployment; Pipelines, for CI/CD-driven workflow management; and Edge, to help users learn how to deploy and manage models that run on edge devices on an ongoing basis.

Pros & Cons

Pricing

Some SageMaker features and capabilities may be available in a limited format through AWS Free Tier. Otherwise, pricing is typically based on per-hour usage and is highly variable, depending on the global region in which you operate and the version of the tool you select. Sample pricing in the United States may look like this for Amazon SageMaker Studio Classic:

• Standard Instances: Between $0.05 and $6.509 per instance per hour used.
• Compute Optimized: Between $0.102 and $3.672 per instance per hour used.
• Memory Optimized: Between $0.151 and $7.258 per instance per hour used.
• Accelerated Computing: Between $0.94 and $28.50 per instance per hour used.

Key Features

• Data preparation, governance, and lifecycle management, including for geospatial data and ML.
• SageMaker Notebooks and Studio Labs to comprehensively test model ideas.
• HyperPod, purpose-built AI/ML training infrastructure.
• End-to-end ML with MLOps and Studio interfaces.
• Ground Truth solution for human-in-the-loop feedback mechanisms.

DataRobot AI Platform

Best for Rapid Model Building & Model Lifecycle Management

DataRobot AI Platform is an automated, end-to-end platform for developing and deploying AI models, helping organizations process and analyze data to derive important business insights. The platform provides tools and resources for data science teams to build, tune, and deploy models, and it allows users to explore models from various algorithms to select the best-fit models for their datasets. The DataRobot AI Platform can be deployed as multi-tenant SaaS, single-tenant SaaS, self-managed VPC, or self-managed on-premise. It is a helpful tool for teams — including inexperienced teams — to get up and running with AI models quickly, as many of its features are automated and unified for both generative and predictive modeling.

The platform includes features for deployment and operations, AI governance and management, and model building and fine-tuning. Additionally, the platform comes with particularly user-friendly visuals that are color-coded and clearly labeled.

Pros & Cons

Pricing

DataRobot doesn’t advertise specific pricing on its page. However, they do mention two plans: Essential 9.0 and Business Critical 9.0. More specific pricing information is available upon request.

Key Features

• Modeling data preparation.
• Model building, validation, performance monitoring, and governance.
• Integration with third-party services like GitHub, Hugging Face, Streamlit, Azure Machine Learning, Amazon SageMaker, mlflow, and Apache Airflow.
• Support for and compatibility with data platforms like Snowflake, Google Cloud Storage, Amazon S3, Amazon Redshift, Google Big Query, Azure Synapse Analytics, SAP, Databricks, and Microsoft Azure Data Lake.
• Supported business apps and intelligence solutions include SAP, Salesforce, Power BI, Tableau, and ThoughtSpot.

IBM watsonx

Best for AI Governance & Explainability

IBM watsonx is a new generation of Watson’s AI products and solutions that span across AI model-building, open data lakehouse storage, AI governance, conversational AI chatbots, task and efficiency management, and code generation and assistance. It is a highly modular platform, with users getting to choose between watsonx.ai, watsonx.data, watsonx.governance, watsonx Assistant, watsonx BI Assistant, watsonx Orchestrate, watsonx Code Assistant, or some combination of these tools and AI assistance services.

AI governance, AI ethics, and regulatory-compliance-focused features include a commitment to only using trusted datasets for training, hands-on data and AI governance tools for users, and a hybrid and governed large-scale data store. With watsonx.governance specifically, users can benefit from a platform-agnostic governance approach that works on platforms like Amazon SageMaker, Amazon Bedrock, Google Vertex, Microsoft Azure, and watsonx.ai. Especially as AI regulations ramp up in the coming years, this is an impressive tool for organizations that want to quickly scale their governance practices.

Pros & Cons

Pricing

Pricing for most watsonx platform features is based on the number of virtual processor cores (VPCs) a user requires. Three SaaS tiers are currently available: Trial, Essentials, and Standard. These tiers include watsonx.ai, watsonx.data, and watsonx.governance:

• Trial: Free, limited trial of watsonx.ai and watsonx.data that includes limited ML functionality and inference tokens per month, as well as 2,000 free watsonx.data Resource Units. It also now includes limited Resource Units and features for watsonx.governance.
• Essentials: A $0 per month tier fee plus RU-based token pricing per 1,000 tokens, ML tools and runtime based on Capacity Unit Hours used per billing month, supporting services at $3 per hour, cache optimized node at $2.80 per hour, and compute optimized node at $6.50 per hour. watsonx.governance usage costs $0.60 per Resource Unit.
• Standard: A $1,050 per month tier fee plus similar component-based pricing to the Essentials plan.

Key Features

• AI and ML model building, training, fine-tuning, and validation.
• Hybrid-cloud-compatible data lakehouse store with shared metadata layer and workload optimization.
• Automated model and workflow documentation.
• Model health, accuracy, drift, and bias management.
• Governance features that include approval workflows, risk scorecards, and model metadata management solutions.

H2O AI Cloud

Best for AutoML

H2O AI Cloud is an enterprise AI and ML platform that comes from H2O.ai, a leading AI cloud company with over 10 years of experience developing AI and ML solutions. The company’s goal is to democratize AI and make it accessible to organizations of all sizes.

In addition to the core H2O AI Cloud platform, H2O.ai also offers the open-source generative AI solution, h2oGPT, which provides tools (H2O LLM Studio, a framework and no-code GUI) for data scientists and developers to build and deploy large language models and chatbot applications. Most recently, the company also released Danube2-18B, an open-source LLM that is both user-friendly and enterprise-quality.

H2O AI Cloud provides comprehensive automated machine learning (autoML) capabilities and no-code deep learning engines through a flexible and scalable cloud platform. The autoML approach in particular makes it possible for users of all different backgrounds to work with the interface’s no-code UI to develop usable workflows and pipelines.

Pros & Cons

Pricing

H2O.ai doesn’t advertise its rates on its website. They encourage interested buyers to request a demo, and quotes will be sent after the demo based on the buyer’s needs. Publicly available pricing information from AWS Marketplace shows that H2O AI Cloud costs $50,000 per unit, with a minimum of four AI units.

Key Features

• Distributed, in-memory processing.
• AutoML capability.
• Various algorithms, including Random Forest, GLM, GBM, XGBoost, GLRM, and Word2Vec, for distributed computing and for both supervised and unsupervised techniques.
• No-code deep learning engines.
• Fully managed and hybrid cloud deployments.

C3 AI Platform

Best for Industry-Specific Model Development

The C3 AI Platform is a unique enterprise AI solution that supports digital transformation projects on an enterprise scale while still remaining accessible to both technical and non-technical team members. C3 AI enables users to ideate, plan, build, deploy, and maintain enterprise AI applications, including for highly specific industries and industrial use cases. For example, one set of C3 AI applications is prebuilt and designed for manufacturing problems and challenges, including inventory parameter management, supply chain variable calculations, and automated recommendations for inventory analysts.

C3 AI Platform customers primarily come from highly regulated sectors with complex workflows, including manufacturing, oil and gas, utilities, financial services, defense and intelligence, government, healthcare, telecommunications, transportation, and retail. The platform includes a democratized AI studio space, open architecture, and a shared data and model ontology to speed up and improve model deployments.

Pros & Cons

Pricing

C3 AI is not transparent with its pricing information or plans. Prospective buyers will need to contact the company directly for more information.

Key Features

• AI feature store and model development tools, including C3 AI Studio.
• Model ops and DevOps workflows.
• Deployment platform services.
• Industry-specific, prebuilt AI application sets.
• Data integration, preparation, visualization, and governance capabilities.

4 Best AI Software for Business Operations & Workflows

Glean

Best for Organizational Knowledge Management & Search

Glean is a generative AI platform that focuses on organizational knowledge management and enterprise search, giving employee users at all levels the tools they need to find, access, save, and share different kinds of business documents and data. All users receive access to the workplace search feature, which is accompanied by an AI assistant that can help users find answers and content that is geared for their specific role, search query, and search history and habits.

From an administrative perspective, the platform also does a compelling job of storing information in such a way that it’s easy to update outdated resources, manage privacy and compliance, and create teams and environments for productive collaboration. Many users also appreciate how Glean’s knowledge graph, plug-ins and connectors, and work hub platform combine the best of both ease of use and customizable workplace search and knowledge management.

Pros & Cons

Pricing

According to a Glean representative, the product is priced per user per month and billed at an annual flat rate. Though the website does not transparently list plans or rates, the Glean customer service representative with whom we chatted shared that different packages are available based on company size and scalable goals. The company typically requires a $40,000 annual contract minimum for teams with fewer than 100 users.

Key Features

• Customizable collections and Go Links for organization-specific knowledge management.
• Vector search with semantic understanding and NLP for plain-language user queries.
• Data governance and compliance features such as DLP reports, GDPR and CCPA compliance, and user access reviews.
• Workplace search, AI assistants, and work hub for UX-focused enterprise knowledge management.
• Slack and other third-party connectors for knowledge management across business applications.

Microsoft Copilot

Best for General Workplace Task Assistance

Microsoft Copilot is a built-in AI assistant that works within several Microsoft products. A free chat interface is available and works similarly to ChatGPT and Gemini, offering several great multimodal capabilities. However, Microsoft’s copilot technology really shines in Microsoft 365 in particular, where tools like Word, Teams, Excel, and others have built in smart assistance, content completion, automations, and workflow support for some of the most common daily and routine business tasks.

Because so many businesses rely on the Microsoft Suite tools for their business operations, this tool has quickly grown in popularity. Microsoft Copilot is also available in a handful of other Microsoft business tools, including PowerApps, Dynamics 365, and Power BI (in preview).

Pros & Cons

Pricing

Pay-as-you-go pricing is a key component of many Microsoft products, including the account users sign up for to use Microsoft Copilot business tier products. The online chat interface of Microsoft Copilot can be freely accessed or upgraded to Copilot Pro for $20 per user per month.

For the most popular business-specific Microsoft Copilot solutions, here’s what pricing looks like:

• Copilot for Microsoft 365: $30 per user per month, billed annually, in addition to Microsoft 365 subscription costs.
• Copilot Studio: $200 for 25,000 messages per month.
• Microsoft Copilot for Sales: $50 per user per month; annual commitment required.

Key Features

• Copilot for Microsoft 365 for built-in AI assistance and workflow automations.
• GPT-4-powered content generation.
• Microsoft Graph grounding.
• Microsoft Copilot Studio for customizable copilots.
• Enterprise data protection and scalability.

Jasper

Best for Digital Marketing Agencies & Teams

Jasper is a suite of AI copilot tools that focuses on supplementing digital and content marketing efforts across social media and web channels. Its tools cover a range of features and functions, including art and image generation for ads and thumbnails, AI content templates, chatbots that provide customer and user assistance, automated end-to-end marketing campaigns, and smart understanding for brand voice and style guides.

Many users select Jasper because the platform can generate content based on the context users provide, including descriptions of what they want content to include, knowledge about specific products or services, instructions on tone of voice and audience, SEO guidance, and style guides.

Many digital marketing teams and agencies also select Jasper for its ease of use and ability to integrate with existing marketing tools. The Jasper API, Chrome browser extension, and a variety of collaboration tools make the Jasper For Business version of the platform a particularly effective tool for holistic marketing strategies and extensibility.

Pros & Cons

Pricing

Jasper is available in three main subscription plans:

• Creator: $39 per month, billed annually, or $49 billed monthly. Includes one seat and one brand voice. A 7-day free trial is also available.
• Pro: $59 per month per seat, billed annually, or $125 per seat, billed monthly. Includes one seat and the ability to add up to five seats; also includes three brand voices, 10 knowledge assets, and three Instant Campaigns. A 7-day free trial is also available.
• Business: Prospective buyers will need to contact Jasper’s sales team for pricing information.

Key Features

• AI-powered content writing, editing, social copywriting, and content strategy.
• Instant and automated marketing campaigns.
• Brand voice guide uploads and content application.
• SEO integrations and content optimization.
• SEO, social media, content management, and other digital-marketing-friendly integrations, as well as Jasper API for custom integrations and embeds.

Fireflies

Best for Notetaking Assistance

Fireflies is an AI-powered note-taking platform that automates the process of capturing meeting information, including audio transcripts, text notes, and highlights. It uses machine learning and natural language processing (NLP) to transcribe meeting recordings and provide users with actionable notes to save them hours of manual post-meeting note-taking and follow-up.

Beyond its basic content summarization features, Fireflies has also continued to expand its conversation analytics and search capabilities. With these two features, users can get a more enterprise-level experience out of an otherwise-simple AI tool, learning more about what was said and how while also quickly pulling important conversational snippets out of longer transcripts. Most recently, Fireflies has come out in a mobile app version.

Pros & Cons

Pricing

Four pricing tiers are available for Fireflies:

• Free: No cost for up to 800 mins of storage.
• Pro: $10 per seat per month, billed annually, or $18 per seat per month, billed monthly.
• Business: $19 per seat per month, billed annually, or $29 per seat per month, billed monthly.
• Enterprise: $39 per seat per month, billed annually; no monthly billing option.

Key Features

• Chrome extension.
• CRM, Zapier, and Slack integrations.
• Team and security management capabilities.
• Meeting clips as sound bytes.
• Topic tracker and analytics.

3 Best AI Software for Customer Service & User Experience

Salesforce Einstein

Best for Unified Sales, Marketing, & Service Support

Salesforce Einstein is a family of AI tools and solutions that are available across the Salesforce ecosystem. Whether you use Salesforce for marketing, sales, service, e-commerce, or some combination of all of these, dedicated Einstein AI capabilities have been designed to automate customer data and experience management in smart ways.

The design of Salesforce itself, with its separate clouds that work seamlessly in a united platform, makes its Einstein AI technology particularly practical and effective. Businesses can easily manage customers throughout their buyer lifecycle, from the time they’re a prospective buyer who requires dedicated marketing campaigns until they become a loyal customer who benefits from frequent nurture campaigns or customer service workflows.

Pros & Cons

Pricing

Certain Saelsforce cloud subscription tiers include access to Einstein capabilities. These are your best options for Einstein access:

• Einstein 1 Sales: $500 per user per month, billed annually.
• Einstein 1 Service: $500 per user per month, billed annually.
• Marketing Cloud Growth Edition: $1,500 per month.

Key Features

• Generative and predictive intelligence through Sales AI features.
• AI for Customer 360.
• Conversational analytics and recommendations.
• Smart chatbot building technology.
• Automated marketing campaign generation.

Chorus

Best for Client-Facing & Sales Teams

Chorus is a conversational intelligence and recording solution that helps users analyze all customer engagements across video conferencing, phone calls, and email channels to learn about their performance in these engagements. With this in-depth data, teams can work to convert prospects into paying customers and forecast future outcomes more accurately.

Chorus comes with features like sales call analysis, deal intelligence, relationship management focused on CRM data, and other advanced AI-driven insights to help teams modify actions for better customer conversations. Since its acquisition by ZoomInfo, Chorus also includes more advanced integrated intelligence about customer contacts and the company, which benefits go-to-market teams even more.

Pros & Cons

Pricing

ZoomInfo, which acquired Chorus.ai, doesn’t publicly advertise its pricing on its website unless you fill out an information request form. However, the company pricing page shows that they offer three plans: Sales, Marketing, and Talent. ZoomInfo usually charges based on your required features, functionality, license number, and credit usage.

Key Features

• Sales call analysis.
• Snippet tool.
• Automatically join Zoom meetings.
• Talk time percentages.
• Supported languages include German, English, French, Italian, Japanese, Dutch, Portuguese, Spanish, and Chinese (Traditional).

Tidio

Best for Customer Success Teams

Tidio is a live chat and chatbot platform for customer support, sales, and marketing. It enables businesses to communicate with customers and visitors in real time and analyze and optimize their conversations.

It offers a suite of useful sales and CX-focused features, including automated chatbot responses, integrated messaging apps, AI-based automated responses, and integrations with third-party software. The Lyroi AI Chatbot and Reply Assistant are two of its most effective AI capabilities, helping businesses create smoother customer service and response workflows.

Pros & Cons

Pricing

Tidio offers various pricing plans, with one free and five paid plans.

• Free: No cost for up to three seats, 50 handled conversations, 100 chatbot triggers, 50 free Lyro conversations, limited workflow and campaign capabilities, and unlimited tickets.
• Starter: $24.17 per month, billed annually, or $29 per month for up to three seats, 100 handled conversations, 500 chatbot triggers, and unlimited tickets.
• Growth: Starting at $49.17 per month, billed annually, or $59 per month for tiered conversation handling options up to 1,000 and several advanced features.
• Tidio+: It’s a personalized plan designed for various enterprise use cases. It starts at $499 per month.
• Lyro AI: Starting at $32.50 per month, billed annually, or $39 billed monthly.
• Flows: Starting at $24.17 per month, billed annually, or $29 billed monthly.
• Campaigns: Starting at $8.33 per month, billed annually, or $10 billed monthly.

Key Features

• Messenger, Instagram, and email integrations.
• Live chat conversations and chatbot triggers.
• Built-in analytics.
• Native Shopify and WordPress actions.
• Lyro AI Chatbot and Reply Assistant.

6 Best AI Software for Enterprise Generative AI

GPT-4 (ChatGPT)

Best Overall Generative AI Platform

Released in November 2022, ChatGPT is a language model developed by OpenAI that interacts with users conversationally. It allows you to have a human-like conversation with the artificial intelligence tool, which has the ability “to answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.”

Business users can use ChatGPT to brainstorm ideas, create first drafts, generate outlines, email ideas, or discuss complex concepts. With the tool’s increasing modality that has come with GPT-4, users can also generate images and videos and benefit from internet connectivity for more up-to-date results.

While ChatGPT offers many benefits, some companies have banned or limited its usage by their employees for compliance reasons and to prevent the release of confidential information. I highly recommend that all business leaders who choose to use ChatGPT and GPT-4 in their businesses also develop a usage policy and train their employees on appropriate use, as proprietary data can easily be misused in the tool.

Pros & Cons

Pricing

ChatGPT and GPT-4 pricing varies based on your chosen model and prompt. Here’s what pricing looks like for GPT-4 models:

• GPT-4o: $5 per 1 million input tokens and $15 per 1 million output tokens. Additional costs may arise based on selected resolution. Limited, free access to this model and its capabilities is available in ChatGPT at all tiers, including the free tier.
• GPT-4 Turbo: $10 per 1 million input tokens and $30 per 1 million output tokens. Additional costs may arise based on selected resolution.
• GPT-4: Between $30 and $60 per 1 million input tokens and between $60 and $120 per 1 million output tokens.

ChatGPT subscriptions are also available for users who simply want to benefit from GPT-4 functionality without working on their own model use cases.

Key Features

• Multimodal content generation.
• Code generation and debugging.
• Internet connectivity in certain subscription levels.
• Free and paid plans, with all plans getting access to at least GPT-4o capabilities.
• Natural language, “chat-like” format with conversational history.

Vertex AI & Gemini

Best for Integrated Generative AI & Internet Experience

Vertex AI is Google’s enterprise AI development platform that includes a range of capabilities and models. Most recently, it has added the highly-praised Gemini models to its collection, including Gemini 1.5 Pro and Gemini 1.5 Flash. In Vertex AI, users can build custom instances of their favorite AI models, training, fine-tuning, and comprehensively testing performance metrics within the platform. The platform is designed to unify data and AI in such a way that users can develop models with little lag time or error.

The Gemini aspect of Vertex AI is one of my personal favorites. Compared to most other generative AI models, I like how well Gemini integrates with Google Search mechanisms, even with widgets that focus on specific types of search (travel, for example). I also appreciate how well this model incorporates user feedback into its performance improvement plans.

Pros & Cons

Pricing

Pricing for Vertex AI is highly variable. First, you’ll need to determine what model type and data format you’re working with (image, video, tabular, or text data). Model options include training, edge on-device training, deployment and online prediction, and batch prediction models. Each of these listings has a different per-node-hour pricing setup. Additional costs are accrued based on Vertex AI Forecast selections, machine types in use, accelerators in use, disks in use, region, and more. To find out exactly how much Vertex AI (and accompanying models like Gemini) will cost for your business, check out the pricing page here.

Key Features

• Vertex AI Studio and Agent Builder.
• 150+ foundation models.
• Access to the latest Gemini models.
• Multimodal content generation.
• Real-time internet connection and widgets.

Claude 3

Best for Ethical Generative AI Strategy

Claude 3 is the latest generation of Anthropic’s Claude, a generative AI model and chat interface that emphasizes ethical outcomes as much as high performance. The newest generation of Claude stands out for a few reasons: It continues to rely on the company’s standard Constitution AI framework and other ethical AI standards, but it has also improved its abilities, adding a larger context window, fewer refusals, and additional responsible design features.

Depending on your requirements, you can work with either Claude 3 Opus, the larger and more intelligent version of the model, or Claude 3 Sonnet, the lightweight and simpler version of Claude 3. There’s also Claude.ai, a free, online version of the tool that works well for users who only need access to simple chat capabilities. My personal favorite thing about the Claude interface is the ability to add attachments to my queries.

Pros & Cons

Pricing

Pricing for Claude 3 is as follows:

• Claude 3 Haiku: $0.25 per 1 million input tokens and $1.25 per 1 million output tokens.
• Claude 3 Sonnet: $3 per 1 million input tokens and $15 per 1 million output tokens.
• Claude 3 Opus: $15 per 1 million input tokens and $75 per 1 million output tokens.

Users also have the option to access legacy models — including Claude 2.1, Claude 2.0, and Claude Instant — as well as claude.ai, a free online version of the tool.

Key Features

• Free Claude access through Claude.ai.
• Constitutional AI review and quality management framework.
• At least 200K context window.
• Red teaming evaluations.
• Multiple versions, including Opus and Sonnet.

GitHub Copilot

Best for Generative Coding & Developer Assistance

GitHub Copilot is a collaboration among GitHub, Microsoft, and OpenAI that supports developers — including student developers with minimal coding skills — with programming, code completion, and code quality assurance tasks. This particular Copilot is unique in that it works effectively for highly technical tasks but allows users to query and improve their code through natural language.

Most recently, an enterprise version of GitHub Copilot was released. This more advanced solution includes a range of chat capabilities, including a beta web search capability powered through Bing; several code completion capabilities; smart actions; multiple IDE; and other supported environments; and comprehensive management and policy features for organizations that need to keep their code aligned with organizational and larger compliance standards.

Pros & Cons

Pricing

Pricing plans are available for both individuals and business teams:

• Copilot Individual: $10 per month or $100 per year for one user.
• Copilot Business: $19 per user per month.
• Copilot Enterprise: $39 per user per month.

Key Features

• Natural language chat interface.
• Real-time AI coding suggestions.
• Vulnerability prevention system.
• Collaborative coding and automated environment setup.
• Personalized answers with inline citations to organizational knowledge base.

Cohere Command

Best for Enterprise API Usability

Cohere Command is one of the latest and greatest collections of generative models from Cohere, considered a pioneer and leader in the generative AI space. Most recently, the company has released its R and R+ series of Command models to the public; these models intend to meet the needs of enterprise users in areas like content summarization, research, and analysis while also offering a range of lightweight and heavyweight options. The Cohere Command models are the engine behind Cohere Generate, an LLM text generation solution that is frequently customized by business users.

Cohere’s models can be used as-is or fine-tuned and customized to align with more specific business use cases. Its API and accompanying documentation are some of the best in this area, offering incredibly straightforward information about how to get started and scale with your own projects. Helpful SDK and getting started documentation is also available.

Pros & Cons

Pricing

Access to Cohere’s Command model is available in both free and paid options:

• Free: Limited, pre-production access to Cohere models and features.
• Command R Default Model: $0.50 per 1 million input tokens and $1.50 per 1 million output tokens.
• Command R+ Default Model: $3 per 1 million input tokens and $15 per 1 million output tokens.
• Command R Fine-Tuned Model: $2 per 1 million input tokens, $4 per 1 million output tokens, and $8 per 1 million training tokens.
• Enterprise: Pricing information available upon request.

Key Features

• Playground environment to test Command and other model functionalities.
• Command R and Command R+ options.
• Retrieval augmented generation (RAG).
• Default and fine-tuned model options.
• Quickstart guides and well-documented API reference.

AI21 Studio

Best for Task-Specific Models

AI21 Studio is an interactive and accessible interface for business users who want to work with AI21 Labs’ foundation and task-specific models. Users frequently praise its easy-to-use interface, API documentation, and enterprise scalability. One of its greatest assets is access for unlimited users in each of its plans; this approach makes it an especially strategic investment for larger teams that want to experiment with and collaborate on AI projects.

AI21 Studio is also a unique solution because of the many different task-specific models it extends to its users. While the majority of these capabilities focus on text generation and other text-related tasks, the operations they can perform are highly specific and useful in a business context. AI21 Studio’s task-specific models and APIs include Wordtune, Text Improvements, Grammatical Error Corrections, Summarize, Text Segmentation, Contextual Answers, Semantic Search, and Embeddings.

Pros & Cons

Pricing

With AI21 Studio, users can subscribe to a Pay as You Go or Custom plan. The Pay As You Go plan operates with usage-based pricing and comes with a limited free trial option for three months. The Custom plan is available with more advanced features and volume discounts.

In addition to these plans, users will likely need to pay for API-specific and model-specific access and usage. Pricing is as follows:

• Jamba-Instruct: $0.50 per 1 million input tokens and $0.70 per 1 million output tokens.
• Jurassic-Ultra: $0.002 per 1,000 input tokens and $0.01 per 1,000 output tokens.
• Jurassic-Mid: $0.00025 per 1,000 input tokens and $0.00125 per 1,000 output tokens.
• Jurassic-Light: $0.0001 per 1,000 input tokens and $0.0005 per 1,000 output tokens.
• Paraphrase: $0.001 per API request.
• Text Improvements: $0.0005 per API request.
• Grammatical Error Corrections: $0.0005 per API request.
• Summarize: $0.005 per API request.
• Text Segmentation: $0.001 per API request.
• Summarize by Segment: $0.005 per API request.
• Contextual Answers: $0.005 per API request.
• Semantic Search: $0.004 per API request.
• Embeddings: $0.0001 per 1,000 tokens.

Key Features

• API access to task-specific language models.
• Text-focused content generation capabilities.
• Jurassic-2 foundation models.
• Wordtune and Wordtune Read applications.
• Jamba hybrid SSM-Transformer model access.

AI Software Benefits

AI software offers several benefits to businesses, ranging from better support for decision making to competitive advantage.

Enhanced Decision Making

With its ability to evaluate large datasets, AI can help enterprise users gain helpful insights from their existing data and operations. It can identify patterns, trends, and correlations that may not be readily apparent to human analysts, enabling businesses to make data-driven decisions and stay ahead of the competition.

Cost Reduction

Automating tasks and optimizing processes with AI minimizes or even eliminates costly errors and the need for manual intervention in various areas, resulting in cost-savings for businesses.

Personalized Customer Experiences

By analyzing customer data and behavior patterns with greater depth and intelligence, you can provide services tailored to your customer’s needs and offer customized customer support. Using AI for CX can boost your retention, loyalty, and customer satisfaction rates.

Workforce Augmentation

AI software can augment human capabilities and expertise, leading to a more skilled and efficient workforce. It can assist employees in their tasks, provide real-time guidance and recommendations, and enable them to leverage AI-generated insights effectively. This collaborative approach between humans and AI improves overall business operational performance and ultimately leads to increased efficiencies and revenue.

Competitive Advantage

Companies can gain a competitive edge in the market by leveraging AI tools, from optimizing marketing campaigns to improving sales forecasting and enabling better product development. Using AI in this way allows enterprises to drive innovation and capture the market early with loyal customers who want these advanced capabilities.

How to Choose the Best AI Software

Choosing the best AI software for your company can be challenging, as there are various factors to consider. Here are some steps to help you make an informed decision:

• Identify your business needs and how AI software aligns with those needs.
• Clearly outline the goals you want to achieve with AI software; SMART goals are a good way to develop clear, measurable, and achievable goals.
• Assess technical requirements, considering scalability, compatibility with existing infrastructure, programming language support, and data integration capabilities.
• Assess the learning curve for your team and research the availability of training resources, documentation, and support from the software provider.
• Evaluate customization options to determine whether AI software can be tailored to your business needs.
• Analyze the algorithmic capabilities of your chosen solution and consider whether the software supports deep learning, machine learning, natural language processing, or other AI techniques that are relevant to your needs.
• Check for data privacy and security features and policies.
• Evaluate vendor reputation and support quality.
• Complete product trials and demos for a better understanding of what you need and what works for your business.
• Consider the total cost of ownership (TCO).
• Seek expert advice if needed, consulting with AI experts or engaging a trusted technology advisor who can guide you based on your business needs and industry requirements.

These steps help to streamline the process of selecting the best AI tool for your business. No matter what, go into this decision-making process with a consistent, unwavering commitment to choosing software that aligns with your specific needs, technical capabilities, and long-term goals.

Bottom Line: AI Software Offers Competitive Advantages

There are thousands of AI software applications and services available, and many more are being released to the market daily. If my past research is any indication, several of these tool’s features will be updated and expanded within a few weeks or months.

Regardless of your industry, team sizes, functions, and technical expertise, there is an AI software solution out there that can help optimize your current business processes and boost your competitive advantage. Especially with the range of price points — including free and open-source AI tools — it’s worth investing the necessary time and research into identifying an AI solution or several that can help you run your business better.

For a full portrait of the AI vendors serving a wide array of business needs, read our in-depth guide: 150+ Top AI Companies

The post Top 20 AI Software of 2024: Best Picks for Business Users appeared first on eWEEK.

Sales managers often use AI-powered sales forecasting to supplement their existing sales forecasting practices with more real-time and far-reaching data, risk management profiles, and recommendations for achieving better results.

When combined with sales team experts and the strategic use of CRM or other sales tools, AI sales forecasting opens up new opportunities for businesses to act on their sales data in future scenarios. In this guide, learn more about AI sales forecasting, how it works, how you can get started with this strategy, and what leading tools you might use.

Understanding AI in Sales Forecasting

AI models that are trained with advanced machine learning algorithms and large sales-specific datasets can provide highly intelligent supplements to marketing and sales forecasts and analytics.

Depending on available data sources and channels, artificial intelligence can identify and incorporate useful finance, chatbot, social media, and customer support conversation data into its analyses to make well-rounded predictions about future revenue, markets, and more.

AI forecasting tools are typically set up to run behind the scenes at all times, collecting new real-time data and updating existing data as conditions change. With this information, sales teams can make well-informed decisions at any point in their sales cycle and for a variety of data points.

To gain a deeper understanding of today’s AI software for sales, read our guide to AI Sales Tools and Software

AI Sales Forecasting vs. Traditional Sales Forecasting

In traditional sales forecasting, past performance data and metrics, various statistical models, and other business intelligence (BI) best practices are leveraged by sales professionals to predict how these same metrics will look in the future.

For example, traditional forecasting may predict a slump in Q2’s sales because the past three years have seen a similar dip. In contrast, AI forecasting takes these existing practices and moves beyond traditional forecasting’s capabilities with large AI/ML algorithms and models, as well as massive large language models.

With the algorithmic training that comes with AI forecasting, AI forecasting tools and solutions can take a more holistic look at all business sales data — including data that is not typically viewed as performance data — and make accurate predictions and recommendations for future outcomes.

For example, an advanced AI forecasting tool may be able to identify negative sentiments in chatbot messages and social media comments that focus on sizing for a particular clothing line; from there, the chatbot may predict a decrease in sales or an increase in returns for this clothing line unless appropriate adjustments are made.

For more information about how these two types of sales forecasting stack up, take a look at our pros and cons comparison:

To learn about AI for customer relationship management, read our guide: Top 8 AI CRM Software 

AI Sales Forecasting Use Cases

Although sales forecasting can be used in highly specific scenarios designed for your business’s sales goals, most AI sales forecasting projects fall into the following use case categories:

• Financial data processing and analysis: Reviewing financial data like revenue, budget spent on past marketing campaigns, cost of deals closed and lost, and more to predict future sales trends.
• Chatbot customer service: Real-time customer feedback and sentiment analysis used to identify fluctuations in product demand, customer acquisition, and customer satisfaction.
• Lead generation and performance scoring: Highly specific analysis and scoring of customers in general and as individuals, looking at different details of their buyer persona, demographics, browsing and purchase history, and social media interactions to determine if they are a worthwhile lead to pursue.
• Customer sentiment analysis: Reviewing the emotions, interactions, and overall behaviors of customers and prospective buyers across different channels, including social media, chatbots, call center transcripts, and customer reviews to determine if product shifts or redesigns are necessary.
• Predictive and prescriptive analytics: Beyond simply predicting performance outcomes based on past data and incoming data, AI sales forecasting tools can often offer recommendations for next steps based on these predictive analytics, whether that’s a possible shift in marketing strategies or an update to a specific webpage.
• Opportunity pipeline and lifecycle management: Instead of looking simply at individual deals and customers, AI forecasting tools can take a look at the sales pipeline as a whole to identify patterns in deal progression and slowdowns, which can help the team make adjustments for the future.
• Risk management: Many AI forecasting tools can look at past events and external data sources — including competitors’ web properties — to identify potential business risks and contingency plans to mitigate these risks.
• Rep-specific performance management and goal-setting: AI can get into the specifics of individual team members’ performance across different metrics, time periods, and channels. Looking not just at straightforward numbers, AI forecasting tools may be able to use sentiment analysis, sales pipeline health, and other murkier or subjective datasets to identify top players and salespeople who could use more training or support.

To learn more about how AI is used in business settings, see our guide: 15 Generative AI Enterprise Use Cases

5 Ways AI Improves Sales Forecasting

With AI as an assistive resource in sales forecasting, businesses can derive new, deeper, and more useful insights at scale. These are some of the benefits that businesses realize almost immediately after incorporating AI into their sales forecasting practices:

• Deeper, more nuanced insights from existing data sources.
• Real-time, accurate updates to predictive analytics and dashboards.
• Multichannel and external data analysis, including on social media.
• Enhanced customer experience focus, with deep reviews of customer interactions and demographics.
• Intelligent performance improvement recommendations, specifically with a shift from predictive analytics to prescriptive analytics and AI-powered recommendations for improvements.

10 Steps to Implement AI Sales Forecasting Solutions

Whether you’re investing in a third-party tool or building your own generative AI model to support sales forecasting work, you’ll need to follow these steps or similar ones to develop an operational and useful tool:

1. Organize and Prepare Data

As with any AI or algorithmic solution, the outcomes are only as good as the training and input data that goes in.

If your organization’s sales data is erroneous, biased, poorly formatted, or incomplete, these problems can severely limit the accuracy and utility of any AI sales forecasts you make. That’s why it’s important to start AI sales forecasting adoption by preparing your data to meet a high data quality standard.

This work may involve cleansing existing data, sourcing new data, finding new data sources, or having multiple team members assess data for biases that may previously have gone unnoticed. This step should take a thorough look at data quality from all angles to achieve the best outcomes.

2. Set Goals and Budget

It will be easiest to measure the success of your AI sales forecasts if you go into this work knowing exactly what you want to measure, how you want to measure it, and how frequently you want to measure it. Specific metrics ensure that AI sales forecasts are focused on the right goals while also helping your team more closely judge the accuracy of the tool’s outputs.

Once measurable, big-picture goals are set, you’ll want to consider what budget is available for AI sales forecasting tools. This will help you decide if it’s time to invest in an entirely new sales platform, work with your existing vendor to embed or integrate AI capabilities, or build or fine-tune your own models for specific use cases.

3. Research AI and Sales Tools

A variety of sales platforms, AI tools, and integrated AI sales solutions are on the market today.

Based on the goals and budget you’ve set as well as your existing tool stack and other preferences, you’ll be able to make an informed decision about the best AI forecasting tool for your business. Most businesses get top value out of investing in an all-in-one sales platform that includes built-in AI capabilities, or even an AI assistant or copilot.

4. Complete Demos or Free Trials

The previous research step should be supported with hands-on testing of your top tool choices. Whether that’s through a customized demo or a free trial, multiple members of your team should test out the tool and its different capabilities.

During this phase, it’s a good idea to not only get familiar with the actual platform but also any community forums, knowledge bases, training, and customer support resources that may be helpful down the road.

5. Adopt or Build a Modifiable Solution

The prep work is done and it’s time to officially implement and customize an AI forecasting solution to your specific needs. Regardless of whether you’ve selected a prebuilt tool or are creating your own sales forecasting AI model, you’ll want to prioritize building a solution that can easily pivot or scale as your teams’ expectations change over time.

6. Start With a Pilot Program

Theoretically, your new AI forecasting sales tool could replace all existing forecast and predictive analytics work, but the problem with this approach is that you won’t be able to easily roll it back if something goes wrong. Instead, start by adopting AI forecasting on a smaller scale for a specific project or use case.

For example, if you work in an e-commerce business, you may want to test the tool first for a specific product’s predicted revenue or a specific sales rep’s expected deal closings for the next two quarters. Focusing a pilot program at a more granular level makes it possible to pinpoint when and where something goes wrong. From there, your team can more effectively make adjustments that will make widespread AI forecasting adoption run more smoothly.

7. Get Buy-In From and Provide Training to Employees

While important stakeholders across teams should already have been involved in the decision-making process, it’s likely that many sales team members are just now learning about this new tool in their stack. Take the time to clearly explain what an AI sales forecasting solution can do, how it can supplement their existing work, and how it can free up their time for the more strategic and interesting work of sales and customer experience management.

From there, take the time to train each team member, going over more general capabilities and then providing role-based training on an individual or team basis. If you take the time to fully communicate and prepare your teams to use these tools well, you’ll not only develop more consistent forecasts but also be working with a team that is more confident in how these tools can support rather than replace them.

8. Develop Monitoring and Review Processes

AI forecasting tools may be prone to error, especially when you’re first getting started. There’s also the possibility of human input errors, data issues, security issues, or customer experience shortcomings. In all of these cases, having a comprehensive performance monitoring and review process will enable you to more quickly identify and address these problems.

For model or AI-specific performance management, human-in-the-loop review workflows are a great way to ensure that each model iteration is closely reviewed and approved by a human expert.

9. Embed or Integrate with Existing Sales Tools

If the AI forecasting tool you’ve selected is not a built-in component of your sales platform or CRM, you’ll likely want to more closely connect it to that platform for smoother workflows and efficiencies. Ideally, your earlier research has already informed you about whether your sales tools natively integrate with your AI forecasting tool.

10. Update Systems and Datasets as Necessary

As your business grows and your sales goals change, you’ll want to update the AI forecasting model’s parameters, data, and other variables to help it keep up with these shifts.

You’ll also want to adjust your tool or even consider moving to a new one if your real-time monitoring results show problems that need to be fixed. Making these updates periodically and regularly will ensure you are handed credible analytical results on a consistent basis.

To see a list of the leading generative AI apps, read our guide: Top 20 Generative AI Tools and Apps 2024

3 Sales Forecasting Tools to Consider

AI sales forecasting tools may be standalone solutions or features built into existing sales platforms. These are some of the leading AI sales and forecasting solutions to consider today:

Salesforce

Salesforce is a leading cloud software suite that includes sales and marketing CRM capabilities as well as solutions to manage customer service experiences and business analytics. With Salesforce Einstein’s Sales AI, users can benefit from AI-powered predictive forecasting and other unique features like deal insights, call insights, a buyer assistant, and relationship graphs and insights.

Pipedrive

Pipedrive is a top CRM software provider that has recently added AI capabilities and features to its platform. Most significant to sales forecasting, the AI Sales Assistant is designed to support revenue forecasting with helpful reminders, suggestions, notifications, and updates to keep your team on track and aware of subtler opportunities.

Zoho CRM

Zoho CRM is a favorite CRM for business users of all backgrounds because of its focus on user experience. Zia, an AI companion, is a recent addition to the tool that helps users manage and optimize the data in their CRM. Some of Zia’s capabilities include conversion predictions, anomaly detection, intelligent automation, competitor alerts, and smart recommendations

7 Best Practices of AI Sales Forecasting

Similar to traditional sales forecasting processes, it’s important to approach your AI sales strategy with strong data management strategies and clear goals in mind. Here are some of the most important best practices for getting started and achieving success with AI sales forecasting:

• Don’t overlook data preparation: Prepare and maintain high-quality, well-rounded, and unbiased data, both for training and inputs.
• Blend AI with existing strategies and strengths: Strategically blend AI with traditional sales forecasting when the situation calls for it; don’t lose the strengths and unique perspectives that your sales experts can provide from years of experience.
• Follow data and AI ethics best practices: Source and use data ethically, especially when operating in industries or regions with strict compliance requirements.
• Focus on employee experience alongside customer experience and sales outcomes: Train team members on how to leverage new insights to make their jobs easier.
• Constantly monitor AI forecasting performance: A real-time monitoring strategy will help your team identify anomalies, performance aberrations, and emerging trends.
• Focus on outcomes and next steps: Don’t let the shine and excitement of a new piece of technology distract you from the goals it should help you achieve in the sales cycle.
• Improve and iterate on AI forecasting models and methods over time: Start small to make it easier to see what does and doesn’t work in short sessions of analysis.

Bottom Line: AI Boosts Sales Forecasting – With Human Help

With its speed, scalability, accuracy, and ability to look at sales data in-depth and from new angles, AI sales forecasting has become a top trend in the sales technology sphere for good reason. But is it “the future” of sales forecasting, or is it nothing more than a trend of the moment?

Right now, the buzz surrounding AI sales forecasting often discounts or downplays the manual effort that makes these tools run. Without sales and data experts preparing the right data, setting relevant goals and parameters, and reviewing performance regularly, AI sales forecasting tools would provide little value to the modern business. In short, salespeople should not worry about being replaced with AI; rather, they should prepare to upskill as their roles shift — AI will certainly take on some of the more tedious work that used to fill their daily schedules.

For a full portrait of the AI vendors serving a wide array of business needs, read our in-depth guide: 150+ Top AI Companies

The post AI Sales Forecasting: Benefits and How-To Guide appeared first on eWEEK.

In this guide, learn more about how AI can be used in CRMs and how your organization can create AI-powered workflows that make sense for your customer relationship management goals and expectations.

What is AI in Customer Relationship Management?

AI in customer relationship management is the use of artificial intelligence software, algorithms, services, and best practices to optimize customer data analytics and lifecycle actions, typically within a CRM platform.

AI in customer relationship management is often used to improve and update customer data in real time, create more engaging customer experiences, expand data-driven knowledge for decision-makers, and automate different aspects of nurturing and working with the customer.

With AI in CRM, marketers, salespeople, and service representatives alike can better handle their day-to-day workflows with automations, integrations, and recommendations that are “smart” and based on up-to-date performance metrics and recent customer interactions.

To learn about AI for customer relationship management, read our guide: Top 8 AI CRM Software 

How CRMs Use AI to Improve Customer Experience

CRMS use data to improve everything from customer data to customer communications and outreach plans. These are some of the most common AI CRM use cases that focus on improved customer experience:

• Reporting and predictive analytics: AI technology is used to collect better data and derive more in-depth information about customer sentiment, performance problems, and other issues that may impact customer experience. From there, many AI analytics solutions can also offer AI-powered recommendations for how to address these shortcomings most effectively.
• Tailored content creation: Generative AI tools can be used to create dynamic web and ad content, personalized emails, and other types of outreach that focus on everything from a customer’s global region to their past buying history to reviews they’ve left on third-party sites. When content is created specifically for individuals, they’re more likely to feel connected and loyal to the brand that’s creating this content.
• Workflow automation: Particularly in the nurture stage of the customer lifecycle, AI can set up complex, always-operational workflow automations so these customers receive regular personalized communications that keep them engaged and satisfied with the service they’re receiving.
• Audience segmentation: A task that has often been done manually (and tediously) in the past, audience segmentation is now frequently handled by AI bots built into the CRM. Based on the data that’s available for each customer profile, AIs can expertly divide these customers into the funnels and outreach groups that make the most sense for their interests and buying history.
• Sentiment analysis: Instead of having a human scour third-party review sites, social media messages, and dozens of chat logs, AI technology can quickly scan through all of these data sources to identify overall sentiments and the sentiments of individual customers in your database. From there, it can make recommendations on any larger changes that need to be made or how to take a better approach based on a specific customer’s frustrations.
• Personalized product recommendations: With the data you’ve collected and that’s been identified over time, AI CRMs have enough knowledge of individual customers to make ultra-personalized product recommendations. This could include upselling and cross-selling based on recent purchases or activity. The likelihood of closing these kinds of deals is all about the relevance and timeliness of recommendations, which are two areas in which AI excels.
• Chatbots and virtual assistants: AI assistants can be used to support both internal employee work and external customer interactions. Especially because most chatbots and copilots are available 24/7, users can complete their work and customers can ask questions whenever it’s convenient for them.

How to Implement AI in Customer Relationship Management

Use CRMs with Native AI

While it is certainly possible to integrate or embed third-party AI technology into your chosen CRM platform, AI will run your workflows and initiatives more seamlessly if it is natively offered as part of the CRM software.

Many of the top CRMs today have built-in AI automation, workflow management, data analytics, and content management features, so take the time to research your own CRM — or prospective CRMs, if you’re on the market — to determine what capabilities are already built into the system. Using a CRM with inbuilt and proven AI functionality will make the adoption and implementation process go more quickly and smoothly.

Map Out Strategic Goals & Outcomes

Before getting started with AI and exploring its capabilities, set your sights on the customer relationship management goals you’re hoping to accomplish with the help of AI. These goals can be far-reaching and generic, but it helps to also set some more specific goals with tasks and initiatives that will help you achieve those outcomes.

Start with highly measurable goals that make the most impact on your business’s customers — and your bottom line — such as the following examples:

• Improve overall customer satisfaction by increasing the organization’s Net Promoter Score (NPS) by 10 points over the next eight months.
• Use AI chatbots to resolve 25% of customer service inquiries and concerns during the customer’s first interaction; make this possible within the first year of AI chatbot adoption.
• Increase customer retention rate by 5% within the year by using AI to improve lead scoring, identify churn risks, and develop targeted customer retention email campaigns.

Other strategic goals for AI in CRM may focus on AI analytics adoption, customer lifecycle management, task automation, and other areas of the CRM workflow where new efficiencies can quickly be realized.

To gain a deeper understanding of today’s AI software for sales, read our guide to AI Sales Tools and Software

Improve Data Quality Prior to AI Adoption

AI does its best work when its training and sourcing data is high quality. Artificial intelligence in CRM technology is a particularly unique enterprise use case of AI, as much of the work that it does focuses solely on your organization’s collected and stored data. This makes it all the more important for your team to prioritize data quality management: cleansing, deduplicating, fact-checking, and updating data are all important parts of this step in the process.

At this time, you’ll also want to consider if your data sources are updated and relevant. When working with customer and customer-service-focused data, it’s important to rely on data from all kinds of customer service channels, including websites, product pages, social media channels, and customer service phone calls and chats. You may want to get even more granular, looking at specific responses to outreach campaign emails, the details of past customer service tickets, and responses to customer surveys and third-party review sites.

Adopt AI Use Cases & Workflows Iteratively

Even if you’re working with a highly sophisticated CRM like Salesforce or HubSpot that offers AI bells and whistles across its feature set, it’s important to not get caught up in the excitement and instead approach AI adoption with cautious logic. Start with your most important goals that you set out before, and take the time to lay out a clear project plan for implementing and adopting this AI use case across your team(s).

The lessons you learn from initial rounds of AI adoption and change management will prepare you to tackle other areas of your AI CRM more efficiently and effectively. Iterative adoption takes time, but it ultimately saves most companies time, as they don’t have to go back to fix repeated errors too often.

Test & Monitor Performance

After initial adoption and implementation, you’ll want to monitor AI technology performance in your CRM to make sure it’s meeting your needs and not creating new errors or problems in your workflows.

While actual performance data may be measurable with usage data in your CRM’s settings, you’ll also want to get creative and look at how customers feel about their AI-based interactions. Make reviewing customer feedback — especially comments on the quality of AI bots — part of your process so you can immediately identify and address any issues customers are having with “nonhuman” support staff or communications.

Develop a Handoff Process for AI Assistants & Human Support Staff

If you’ve decided to use AI as part of your customer service chatbot workflows, don’t forget that AI technology still has its limitations, especially when AI chatbots are in the early days of training and learning your organizational data. Human support staff should remain available at least during regular business hours, as they will occasionally need to take over and handle customer service queries directly.

But even in after-hours scenarios, develop a system where unresolved AI customer service conversations can be passed to the next-available human support staff. Customers will quickly become frustrated if they feel like there’s no way for them to get in touch with an actual company representative for their more complicated questions, especially if the AI chatbot is giving canned or irrelevant responses to their questions.

6 Benefits of Integrating AI into CRM

Integrating artificial intelligence into your CRM can lead to benefits for the business as a whole as well as for key players on the team and the customers themselves. Here are a few of the ways in which both customers and businesses benefit from AI in CRM:

Smart & Timely Data Analytics

AI can support better data collection and cleansing methods while also getting more useful insights out of this data. In essence, AI in a CRM can help manage data and data analytics throughout the data lifecycle.

Most important, AI can operate in the background of all customer service channels at all times, meaning this data can be updated in real time. Having up-to-date, accurate, and diverse customer data at all times leads to more data analytics possibilities, particularly for businesses that want to identify and address any customer churn risks.

Time-Savings Through Workflow & Task Automation

Artificial intelligence can write emails and blog posts, handle customer service queries, organize tasks and action items, and set up strategic outreach campaigns that reach the right people at the right times.

All of these tasks would typically require human intervention on a daily or weekly basis, which takes significant time out of their work schedules. As AI is used to automate and take over employees’ most tedious tasks, they can spend more time working on high-level strategy and customer nurturing campaigns that lead to greater company growth and customer retention potential.

Revenue Growth Potential

Customers who are happier with the outreach and support they receive are more likely to become loyal, repeat customers or even brand advocates. When your brand develops this reputation among loyal customers, you’ll not only earn more of their business but will likely attract more customers who are interested in receiving the same levels of attention and care.

More Personalized Shopping Experiences

With the ability to connect multichannel customer data in nearly any format, AI-powered CRMs have all the data necessary to better personalize shopping experiences for customers. They are more likely to see relevant targeted ads, receive email campaigns on sales or deals that interest them, and get customer support resources that addresses the real struggles they’re having with your products or services.

In sum, AI enables a personal touch that is difficult to replicate on such a large scale with limited human workforces.

Less Spam

AI is frequently used to target CRM outreach campaigns at prospective buyers and current customers who are most likely to be interested in that specific message and buying suggestion. This means customers receive fewer unwanted, spam messages from your organization; they’ll be happier with the communications they do receive from you, and you’ll likely get more engagement from those messages as well.

Real-time Support Services

AI chatbots aren’t limited to a 9-to-5 job schedule or the hours a business can afford to staff. In most cases, AI chatbots can stay up and running at all hours, leading to no additional costs for businesses.

However, customers certainly benefit, as they can get answers to many of their most pressing questions at any time of the day. This is an especially effective feature for global businesses that support customers in varying time zones.

3 Leading CRM Solutions Using AI

HubSpot

HubSpot is a leading CRM platform that divides its system into core hubs: the Marketing Hub, Sales Hub, Service Hub, Content Hub, Operations Hub, and Commerce Hub can all be used together or independently.

Within the Service Hub section, AI focuses primarily on offering real-time customer support and smart insights to business users. HubSpot’s AI features include different generative AI tools to create content for web, email, and social media; AI summarization; brand voice management; chatbot building; and unified customer record management.

Pipedrive

Pipedrive is a sales-focused CRM platform that combines some of its own AI technologies with OpenAI products to create AI-driven solutions and experiences. While Pipedrive’s AI solutions mostly support sales workflows and use cases, Pipedrive’s AI email generator and email summarization can both be used to create and manage content for customer service scenarios.

Freshsales

Freshsales is a sales CRM that also includes robust customer service and relationship management capabilities. With its Freddy AI collection of AI features, business customers can benefit from intelligent self-service chatbots, the Freddy Copilot for operational support and task management, and Freddy Insights for data-driven decision-making and risk management.

Bottom Line: AI is Improving Customer Relationships

For many years, customers have dreaded the possibility of needing to interact with a “robot” to get the answers they want when online shopping. While this dread still exists at some level, the development of AI and generative AI are offering drastic improvements, making it increasingly difficult for customers to tell the difference between a human-driven and an AI-driven interaction; AI tends to give them the answers they need with minimal human intervention.

Moreover, AI has reached the point where it can surpass human customer service representatives’ abilities in certain key areas, including collecting and applying real-time customer data and automating various outreach and lead management tasks. Expect AI to continue its rapid evolution in this sector and within CRM software, especially as businesses continue to realize how more tailored customer data and experiences lead to more revenue.

For a full portrait of the AI vendors serving a wide array of business needs, read our in-depth guide: 150+ Top AI Companies

The post AI In CRM: How AI is Reshaping Customer Experiences appeared first on eWEEK.

Furthermore, generative AI is evolving at a stunningly rapid pace, enabling it to address a wide range of business use cases with increasing power and accuracy. Clearly, generative AI is restructuring the way organizations do and view their work.

With both established tech enterprises and smaller AI startups vying for the next generative AI breakthrough, future prospects for generative AI are changing as rapidly as the technology itself. For better understand its future, this guide provides a snapshot of generative AI’s past and present, along with a deep dive into what the years ahead likely hold for generative AI. 

Generative AI’s Future: 8 Predictions

Looking ahead, expect to see generative AI trends focused on three main pools: quick and sweeping technological advances, faster-than-expected digital transformations, and increasing emphasis on the societal and global impact of artificial intelligence. These specific predictions and growing trends are most likely on the horizon:

1. Growth in Multimodality

Multimodality — the idea that a generative AI tool is designed to accept inputs and generate outputs in multiple formats — is starting to become a top priority for consumers, and AI vendors are taking notice.

OpenAI was one of the first to provide multimodal model access to users through GPT-4, and Google’s Gemini and Anthropic’s Claude 3 are some of the major models that have followed suit. So far though, most AI companies have not made multimodal models publicly available; even many who now offer multimodal models have significant limitations on possible inputs and outputs.

In the near future, multimodal generative AI is likely to become less of a unique selling point and more of a consumer expectation of generative AI models, at least in all paid LLM subscriptions.

Additionally, expect multimodal modeling itself to grow in complexity and accuracy to meet consumer demands for an all-in-one tool. This may look like improving the quality of image and non-text outputs or adding better capabilities and features for things like videos, file attachments (as Claude has already done), and internet search widgets (as Gemini has already done).

ChatGPT currently enables users to work with text (including code), voice, and image inputs and outputs, but there are no video input or output capabilities built into ChatGPT. This may change soon, as OpenAI is experimenting with Sora, its new text-to-video generation tool, and will likely embed some of its capabilities into ChatGPT as they have done with DALL-E.

Similarly, while Google’s Gemini currently supports text, code, image, and voice inputs and outputs, there are major limitations on image possibilities, as the tool is currently unable to generate images with people. Google seems to be actively working on this limitation behind the scenes, leading me to believe that it will go away soon.

2. Wider Adoption of AI as a Service

AI as a service is already growing in popularity across artificial intelligence and machine learning business use cases, but it is only just beginning to take off for generative AI.

However, as the adoption rate of generative AI technology continues to increase, many more businesses are going to start feeling the pain of falling behind their competitors. When this happens, the companies that are unable or unwilling to invest in the infrastructure to build their own AI models and internal AI teams will likely turn to consultants and managed services firms that specialize in generative AI and have experience with their industry or project type.

Specifically, watch as AI modeling as a service (AIMaaS) grows its market share. More AI companies are going to work toward public offerings of customizable, lightweight, and/or open-source models to extend their reach to new audiences. Generative AI-as-a-service initiatives may also focus heavily on the support framework businesses need to do generative AI well. This will naturally lead to more companies specializing and other companies investing in AI governance and AI security management services, for example.

3. Movement Toward AGI and Related Research

Artificial general intelligence, which is the concept of AI reaching the point where it can outperform humans in most taskwork and critical thinking assignments, is a major buzzword among AI companies today, but so far, it’s little more than that.

Google’s Deepmind is one of the leaders in defining and innovating in this area, along with OpenAI, Meta, Adept AI, and others. At this point, there’s not much agreement on what AGI is, what it will look like, and how AI leaders will know if they’ve reached the point of AGI or not.

So far, most of the research and work on AGI has happened in silos. In the future, AGI will continue to be an R&D priority, but much like other important tech and AI initiatives of the past, it will likely become more collaborative, if for no other reason than to develop a consistent definition and framework for the concept. While AI leaders may not achieve true AGI or anything close to it in the coming years, generative AI will continue to creep closer to this goal while AI companies work to more clearly define it.

To see a list of the leading generative AI apps, read our guide: Top 20 Generative AI Tools and Apps 2024

4. Significant Workforce Disruption and Reformation

Most experts and tech leaders agree that generative AI is going to significantly change what the workforce and workplace look like, but they’re torn on whether this will be a net positive or net negative for the employees themselves.

In this early stage of workforce impact, generative AI is primarily supporting office workers with automation, AI-powered content and recommendations, analytics, and other resources to help them get through their more mundane and routine tasks. Though there is some skepticism both at the organizational and employee levels, new users continue to discover generative AI’s ability to help them with work like drafting and sending emails, preparing reports, and creating interesting content for social media, all of which saves them time for higher-level strategic work.

Even with these more simplistic use cases, generative AI has already shown its nascent potential to completely change the way we work across industries, sectors, departments, and roles. Early predictions expected generative AI would mostly handle assembly line, manufacturing, and other physical labor work, but to this point, generative AI has made its most immediate and far-reaching impacts on creative, clerical, and customer service tasks and roles.

Workers such as marketers, salespeople, designers, developers, customer service agents, office managers, and assistants are already feeling the effects of this technological innovation and fear that they will eventually lose their jobs to generative AI. Indeed, most experts agree that these jobs and others will not look the same as they do now in just a couple of years. But there are mixed opinions about what the “refactored” workforce will look like for these people — will their job simply change or will it be eliminated entirely?

With all of these unknowns and fears hanging in the air, workplaces and universities are currently working on offering coursework, generative AI certifications, and training programs for professional usage of AI and generative AI. Undergraduate and graduate programs of AI study are beginning to pop up, and in the coming months and years, this degree path may become as common as those in data science or computer science.

5. Increasing Regulatory, Ethical, and Societal Pressures

In March 2024, the EU AI Act that had been discussed and reviewed for several years was officially approved by the EU Parliament. Over the coming months and years, organizations that use AI in the EU or in connection with EU citizen data will be held to this new regulation and its stipulations.

This is the first major regulation to focus on generative AI and its impact on data privacy, but as consumer and societal concerns grow; don’t expect it to be the last. There are already state regulations in California, Virginia, and Colorado, and several industries have their own frameworks and rules for how generative AI can be used.

On a global scale, the United Nations has begun to discuss the importance of AI governance, international collaboration and cooperation, and responsible AI development and deployment through established global frameworks. While it’s unlikely that this will turn into an enforceable global regulation, it is a significant conversation that will likely frame different countries’ and regions’ approaches to ethical AI and regulation.

6. Bigger Emphasis on Security, Privacy, and Governance

With the regulations already in place and expected to come in the future, not to mention public demand, AI companies and the businesses that use this technology will soon invest more heavily in AI governance technologies, services, and policies, as well as security resources that directly address generative AI vulnerabilities.

A small number of companies are focused on improving their AI governance posture, but as AI usage and fears grow, this will become a greater necessity. Companies will begin to use dedicated AI governance and security platforms on a greater scale, human-in-the-loop AI model and content review will become the standard, and all companies that use generative AI in any capacity will operate with some kind of AI policy to protect against major liabilities and damage.

7. Greater Focus on Quality and Hallucination Management

As governments, regulatory bodies, businesses, and users uncover dangerous, stolen, inaccurate, or otherwise poor results in the content created through generative AI, they’ll continue to put pressure on AI companies to improve their data sourcing and training processes, output quality, and hallucination management strategies.

While an emphasis on quality outcomes is part of many AI companies’ current strategies, this approach and transparency with the public will only expand to help AI leaders maintain reputations and market share.

So what will generative AI quality management look like? Some of today’s leaders are providing hints for the future.

For example, with each generation of its models, OpenAI has improved its accuracy and reduced the frequency of AI hallucinations. In addition to actually doing this work, they’ve also provided detailed documentation and research data to show how their models are working and improving over time.

On a different note, Google’s Gemini already has a fairly comprehensive feedback management system for users, where they can easily give a thumbs-up or thumbs-down with additional feedback sent to Google. They can also modify responses, report legal issues, and double-check generated content against internet sources with a simple click.

These features provide users with the assurance that their feedback matters, which is a win on all sides: Users feel good about the product and Google gets regular user-generated feedback about how their tool is performing.

In a matter of months, I expect to see more generative AI companies adopt this kind of approach for better community-driven quality assurance in generative AI.

8. Widespread Embedded AI for Better Customer Experiences

Many companies are already embedding generative AI into their enterprise and customer-facing tools to improve internal workflows and external user experiences. This is most commonly happening with established generative AI models, like GPT-3.5 and GPT-4, which are frequently getting embedded as-is or are being incorporated into users’ preexisting apps, websites, and chatbots.

Expect to see this embedded generative AI approach as an almost-universal part of online experience management in the coming years. Customers will come to expect that generative AI is a core part of their search experiences and will deprioritize the tools that cannot provide tailored answers and recommendations as they research, shop, and plan experiences for themselves.

For an in-depth comparison of two leading AI art generators, see our guide: Midjourney vs. Dall-E: Best AI Image Generator 2024

Generative AI’s Recent Past Suggests Its Future

With how much has happened in the world of generative AI, it’s hard to believe that most people weren’t talking about this technology until OpenAI first launched ChatGPT in November 2022. Many of generative AI’s greatest milestones were reached in 2023, as OpenAI and other hopeful AI startups — not to mention leading cloud companies and other technology companies — raced to develop the highest-quality models and the most compelling use cases for the technology.

Below, we’ve quickly summarized some of generative AI’s biggest developments in 2023, looking both at significant technological advancements and societal impacts:

• Continued growth and reshuffling of AI startups: Dozens of generative AI startups opened, closed, rebranded, or partnered with larger tech companies. This constant shuffling continues today.
• AI virtual assistants and copilots: Though GitHub Copilot has been around in some form since 2021, most of the Microsoft Copilot technology and other copilots that are being released today first emerged in 2023. These built-in work assistants can help with content generation, enterprise search, coding, data analysis, and more.
• LLM-driven search and content generation: Though large language models did not first appear in 2023, the variety and capabilities that we see now mostly developed in 2023. Throughout this past year, vendors have continued to improve models’ transparency, explainability, accuracy, and connectivity with the internet for better and more comprehensive results.
• User-friendly and affordable interfaces: A growing number of AI vendors have released free or low-cost versions of their generative AI tools in packages that are not just for business use. For example, Inflection released Pi — a generative AI companion that focuses on human-like conversational AI — in May 2023. Additionally, vendors continue to expand their tools’ usability in mobile and lightweight interfaces that all users can access.
• High-powered models with a new focus on multimodality: ChatGPT, Claude, and Gemini are all top examples of generative AI models that came out with new versions in 2023 that could handle multimodal requests.
• Increasing noise about regulations on the horizon: The EU AI Act was first brought forward in 2021, but as it moved through committees and was discussed over the following years, it increased discussions about generative AI regulations and ethics throughout the course of 2023.

Generative AI: The Current Landscape

The generative AI landscape has transformed significantly over the past several months, and it’s poised to continue at this rapid pace. What we’ve covered below is a snapshot of what’s happening with generative AI in early 2024; expect many of these details to shift or change soon, as that has been the nature of the generative AI landscape so far.

Though it has not been widely adopted in many industries, generative AI continues to build its reputation and gain important footholds with both professional and recreational user bases. These are some of the main ways generative AI is being used today:

• Personal and recreational LLM usage for simple questions, conversational AI, research support, etc.
• Coding and software development, with an emphasis on code completion and quality assurance reviews.
• E-commerce and inventory management.
• Customer and online experience management (shopping, travel planning, research support, etc.).
• Marketing, sales, and general content creation in text and multimedia formats.
• Voice and audio synthesis.
• Project management, office assistance, and workflow automation.
• Creative content development (art, video games, music, etc.).
• AI customer service agents and chatbots.
• AI-powered analytics and synthetic data generation.
• Educational content generation and online training.
• Pharmaceutical drug discovery and medical diagnostics.
• Smart manufacturing and supply chain management.
• AI risk management in insurance, fintech, and similar lines of business.
• Search and knowledge graphs.

To learn about today’s top generative AI tools for the video market, see our guide: 5 Best AI Video Generators

Consumer Trust and Ethical Considerations

According to Forrester’s December 2023 Consumer Pulse Survey results, “only 29% agreed that they would trust information from gen AI” and “45% of online adults agreed that gen AI poses a serious threat to society.” In the same results, though, 50% believed that this technology could also help them to find the information they need more effectively.

Clearly, public sentiment on generative AI is currently very mixed. In North America, in particular, there’s excitement and interest in the technology, with more users experimenting with generative AI tools than in most other parts of the globe. However, even among those with enthusiasm for generative AI, there is a general caution about data security, ethics, and the general trust gap that comes with a lack of transparency, misuse and abuse possibilities like deepfakes, and fears about future job security.

To earn consumer trust, more ethical AI measures must be taken at the regulatory and company levels. The EU AI Act, which recently passed into law, is a great step in this direction, as it specifies banned apps and use cases, obligations for high-risk systems, transparency obligations, and more to ensure private data is protected. However, it is also the responsibility of AI companies and businesses that use AI to be transparent, ethical, and responsible beyond what this regulation requires.

Taking steps toward more ethical AI will not only bolster their reputation and customer base but also put in place safeguards to prevent harmful AI from taking over in the future.

To learn more about the issues and challenges around generative AI, read our guide: Generative AI Ethics: Concerns and Solutions

Strategies for Navigating the Future of Generative AI

Generative AI is clearly here to stay, regardless of whether your business chooses to incorporate this technology. The key to working with generative AI without letting it overrun your business priorities is to go in with well-defined effective AI strategies and clear-cut goals for using AI in a beneficial way. Some of these strategies may help:

Create an AI Strategy Specific to Your Business

This strategy should explain what technologies can be used, who can use them, how they can be used, and more. Keep strategies and policies both flexible and iterative as technologies, priorities, and regulations change.

Support Employees Through Role and Workplace Transitions

At the rate generative AI innovation is moving, there’s little doubt that existing jobs will be uprooted or transformed entirely. To support your workforce and ease some of this stress, be the type of employer that offers upskilling and training resources that will help staffers — and your company — in the long run.

Think Globally and Collaboratively

If you’re in a position of power or influence, consider doing work to mitigate the increasing global inequities that are likely to come from widespread generative AI adoption.

Partner with firms in developing countries, work toward generative AI innovations that benefit people and the planet, and support multilingual solutions and data training that are globally unbiased.

In general, partnering with leaders in other countries and organizations will lead to better technology and outcomes for all.

Embrace AI Innovations With Caution

Especially in the pursuit of AGI, be cautious about how you use generative AI and how these tools interact with your data and intellectual property. While generative AI has massive positive potential, the same can be said for its potential to do harm. Pay attention to how generative AI innovations are transpiring and don’t be afraid to hold AI companies accountable for a more responsible AI approach.

Bottom Line: Preparing for the Future of Generative AI

Generative AI has already proven its remarkable potential to reshape industries, economies, and societies even more than initially thought. Research firms and technology companies are continually adjusting their predictions for the future of generative AI, realizing that the technology may be able to take on more of the physical taskwork and cognitive work that human workers do — and by a much earlier date — than previously assumed.

But with this incredible technological development should come a heavy dose of caution and careful planning. Generative AI developers and users alike must consider the ethical implications of this technology and continue to do the work to keep it transparent, explainable, and aligned with public preferences and opinions for how this technology should be used. They must also consider some of the more far-reaching consequences — such as greater global disparities between the rich and the poor and more damage to the environment — and look for creative ways to create generative AI that truly does more good than harm.

So what’s the best way forward toward a hopeful future for generative AI? Collaboration. AI leaders, users, and skeptics from all over the globe, different lines of work, and different areas of expertise must collaboratively navigate the challenges and opportunities presented by generative AI to ensure a future that benefits all.

For more information about generative AI providers, read our in-depth guide: Generative AI Companies: Top 20 Leaders

The post The Future of Generative AI: Trends and Challenges appeared first on eWEEK.

Businesses from all different industries are incorporating new enterprise AI use cases in their workflows to improve products and disrupt their respective industries. To keep up with the competition, business leaders need to develop an AI business strategy that addresses their unique business model while helping them keep pace with industry-wide digital transformations.

In this guide, we’ll walk you through eight key steps of crafting a top AI business strategy. We’ll also cover some of the greatest benefits and biggest challenges that come with adopting AI as part of your business’s operational framework.

1. Identify Current Performance and Technology Gaps

Artificial intelligence tools and strategies can be infused throughout your business operations, but chances are, there are a few key areas where AI will make the highest-value impact in your organization.

To determine where these gaps are, start by looking at your legacy tools and applications, as well as any performance data or support tickets that indicate recurring problems with those systems. Additionally, assess the size and quality of different departments and teams, paying close attention to any resources they’re missing that would make their work more efficient.

Finally, look at things from the investors’ or customers’ perspective and ask this question: What current performance gaps are impacting their experience or the bottom line?

Asking these questions, completing a deep audit of your current resources and processes, and documenting your most crucial gaps is an important first step toward determining which AI solutions, partners, and investments are most strategic for your business.

To develop a plan to improve performance, it’s critical to create metrics. The chart above illustrates the various metrics that can be used to estimate the ROI of an AI investment. Source: Gartner

2. Research the AI Technology and Services Landscape

Once you’ve decided which areas of your business could most benefit from AI tooling and strategy, it’s time to look at the AI technology landscape and what it has to offer.

Depending on your internal skill sets and budget, you may choose to invest in:

• A free AI tool, an open-source solution, or a fine-tuned existing AI model
• A managed large language model
• Or even build your own generative model (though this is a particularly resource-intensive and expensive approach)

There’s also a wide range of prebuilt AI tools that won’t require you to adjust any deep learning algorithms or training data. Instead, you’ll simply work with the vendor or their platform to adjust the AI to your specific needs. You’ll also need to determine if you want to invest in more generic AI solutions — such as chatbots, copilots, and AI automation tools — or if you’re interested in a more specialized solution that is designed for your industry or a enterprise specific use case.

All of these approaches are valid but may not be the best route for your business. To make the best possible investment, spend some time researching leading AI vendors, big and small, assessing their individual products, longterm roadmaps and goals, and the additional resources they provide to support their customers.

It’s also valuable to look at customer reviews on third-party review sites, ratings from technology research firms, and the investors that are currently backing some of these technologies. Through each of these portions of your research, return to this question: Does what I’ve learned about this vendor, product, or service align with our business’s goals and way of doing things?

3. Set SMART Goals for AI Adoption

You’ll likely have a basic idea of your AI adoption goals and desired outcomes at this point, but you’ll stand the best chance of reaching those goals if you spell them out. There are several different ways to do this, but SMART goals provide a straightforward and highly objective way to measure how well you’re staying on track.

SMART goals are:

• Specific: The goal should very clearly state what you want to accomplish and on what timeline. It’s important to include numerical measures of progress and deadlines so everyone understands what the goal is. For example, “increase online conversion rate for sales of personalized AI product recommendations by 10% in the next eight months” is a highly specific SMART goal.
• Measurable: Again, numbers are important for these types of goals, because you’ll only know if you’re on track if there’s a way to measure your progress toward milestones. In the example we’ve listed above, the numbers are all there, and the team can easily track which conversions are happening due to AI-powered recommendations.
• Achievable: AI adoption will fall apart if you don’t set realistic goals for your team to act on. In the case listed here, this goal should be achievable if enough team members are aware of and committed to resourcing it.
• Relevant: Just because AI can do a certain task doesn’t mean that it’s the most efficient way to use it in your business. Be sure to set goals that are relevant to your business operations and longterm revenue goals, or else you’ll end up wasting time on a less-valuable project. This SMART goal is only valuable for businesses that are hoping to increase their product revenue in this way.
• Time-bound: SMART goals should include a realistic timeline for completion. In the details or subtasks that roll up into this goal, consider including monthly or weekly check-ins so progress and bottlenecks can all be assessed.

Your team may have one or several SMART goals like this one, depending on how many AI projects you’re working on at once. A helpful way to organize and visualize all of these goals as part of a bigger picture is through a planning roadmap. And while these goals may look a little different, it’s worthwhile to spell out any AI ethics or compliance goals too, so your team won’t forget about them in the middle of implementation.

4. Partner With Strategic AI Vendors

AI vendors come in all shapes and sizes. There are massive enterprises that were some of the earliest pioneers of AI technology, there are small AI startups that focus on a specific product or use case, and there’s companies that do a little bit of everything for AI products and services.

While it isn’t realistic to research every AI company out there, it’s smart to look at a variety of players to see who would be a strategic partner for your business. In many cases, the biggest name isn’t the most aligned or experienced with what you want to do.

The best way to ensure your AI investments work across all departments and functions is to bring key leaders, managers, and stakeholders from each group into the decision-making process. Consider having them complete a demo or trial period, share their perspective on what is and isn’t working with their current tech stack, and gain more operational data to secure a well-informed partnership or a well-rounded purchase.

5. Develop and Follow an AI Implementation Plan

Realistically, you’ve already completed several steps in creating your AI implementation plan, especially if you were thoughtful while writing out your AI adoption goals. Now, it’s time to figure out the exact details for executing a successful AI rollout.

You’ll want to first prep all key aspects of your internal operations — tools, data, and teams — for AI adoption. With your existing applications and data, this step may involve cleaning up or reformatting data so it works with new tools. With your teams, you may need to take some time to share your tooling and timeline decisions with them so they know where they fit into the AI implementation schedule.

Several examples of AI action or implementation plans can be found online, but ultimately, you know what makes the most sense for your team. Source feedback from your employees, talk with your vendors about what’s possible, and don’t be afraid to adjust your plan if needed along the way.

6. Create Cross-Functional AI Training and Change Management Programs

AI business strategies, no matter how strategic, will fall apart if your teams are unaware of or uninvested in your hoped-for outcomes. That’s why it’s important to train all employees on how AI will impact their role and how they can best use it for success. This can be a particularly sensitive step in an AI business strategy, as some employees may fear they are being replaced by AI.

To address and mitigate these fears, make sure that your change management program offers retraining and professional development resources that can help these employees feel confident if they need to up-skill. Fortunately, many AI vendors provide customers with extensive knowledge bases, learning resources, and even training academies and certifications that can help. These resources are available at all times, so when new employees come aboard or existing employees need a refresher, go back to these resources to keep things running smoothly on all fronts.

7. Track AI Performance Metrics

During and after AI implementation, your business should regularly track AI performance through the metrics that matter most to you. For example, if you’ve adopted an AI healthcare assistant or agent, your metrics may look something like this:

• Patient satisfaction with AI agent interactions
• AI agent’s response timeliness
• AI agent’s response accuracy
• AI agent’s adherence to HIPAA and privacy standards
• Number of patient interactions with AI agents over time

However, if you’ve invested in an AI data analytics platform to support your marketing and sales teams, your metrics may be more like this:

• Quality of predictive insights
• Quantity of predictive insights
• Relevance and actionability of AI recommendations
• User satisfaction with AI and data explainability
• Performance speed and accuracy with larger datasets

As you can see, these metric sets are quite different from each other. But they’re similar in that they each focus on both quantitative and qualitative measures of success. Regardless of what type of AI tool you use, be sure to select a wide variety of useful metrics and measure often; these measurements will help you determine if an AI app needs updating or a team needs retraining for better outcomes.

8. Adjust AI Solutions and Plans Periodically

What AI looks like today is not what it will look like tomorrow. And what your business looks like today is not necessarily what it will look like tomorrow or a month from now. Additionally, the AI tooling and regulatory landscape is changing at a rapid and constant rate, so it’s important to keep your AI implementation and adoption plans iterative and agile.

If you adopt and test AI solutions on an iterative basis while also keeping up with how AI and industry-specific regulations are evolving — as well as how your customers and the general public’s views on AI ethics and usage change over time — you’ll be prepared to shift your approach quickly and keep your company aligned with the best possible outcomes.

For a deeper understanding of AI compliance issues, read our guide: AI Policy and Governance: What You Need to Know

7 Benefits of AI Business Strategy Planning

The potential benefits of AI grow significantly when AI is accompanied by an effective business strategy. These are just a handful of the benefits that come with comprehensive AI business strategy planning:

• More strategic AI adoption and usage: An AI business strategy plan provides clear details about who should use what AI tools, when and why they should use them, and how they should use them most effectively. This comprehensive plan helps your whole team adopt the right solutions and ensure they actually get used in a way that consistently benefits the business.
• AI risk management and disaster recovery planning: An AI strategy doesn’t just cover what tools you’ll be using but also delineates what structures and safeguards will need to be established for success. The preplanning that comes with AI business strategy leads many business leaders to prepare a more effective risk management and disaster recovery plan, which may even extend beyond your AI tools to better protect the rest of your business applications and operations.
• Responsible and ethical approach to AI adoption: AI business strategies help you to think about all of the ways AI can impact your business, both good and bad. Proactive planning and ideating for AI is the best way to ensure you make responsible and ethical decisions that consider the needs of your employees and customers alike.
• Cross-functional and cross-disciplinary AI adoption: Without an AI business strategy, individuals or individual departments may adopt AI tools without much thought given to who else could benefit from these technologies. An overarching AI strategy helps the entire business identify useful tools and how they can be used effectively in different roles and divisions of the company.
• Competitive edge: Businesses that flesh out their AI strategies will have a clear picture of what they want to accomplish and what steps they need to take to get there. While other competitors may simply start using AI and run into performance issues or bottlenecks due to poor planning, businesses with an AI strategy will avoid many of these pitfalls and pass their competitors quickly.
• Automation and productivity support: An AI strategy assists leaders in identifying where current performance gaps or challenges are hindering productivity in the business. From there, they can select the AI software that is most likely to solve these issues, automate complex workflows, and otherwise improve the day-to-day operations of the business.
• Enhanced customer experience and customer insights: Businesses typically start their AI journey with internal solutions to help their employees’ productivity, but with an AI business strategy, they may more quickly identify how AI can improve customer experiences.

7 Common Challenges of AI Business Strategy Planning

AI business strategy planning is a difficult process, especially if you don’t get the right people and solutions in place from the outset. These are some of the most common mistakes and challenges that businesses face when working on AI business strategy plans:

• Making smart cross-functional AI investments: Technology investment decisions are often made from the top down. But with AI technology that is designed to be embedded and incorporated into multiple parts of business operations, this approach may mean you invest in a tool that is not a good fit for certain employees’ day-to-day responsibilities. To avoid this, you should create a cross-functional decision-making team for AI business strategy planning.
• Preparing internal data and operations for AI: Many organizations spend all of their time researching and selecting the right AI tool for their business but never consider all of the work that should go into preparing their data, workflows, and other business assets for AI. Businesses that fail to prepare their data for AI may end up with poorly-trained or ineffective AIs, or worse, an AI that has been exposed to PII or PHI in a noncompliant manner.
• Setting and sticking to reasonable AI goals: AI is an exciting business prospect, and many business leaders are tempted to implement AI solutions across their operations all at once. However, this method can lead to implementation errors and limited utility, as you’re giving your teams minimal time to edit and fact-check AI additions. It’s a good idea to set iterative AI implementation goals, giving your team a chance to learn how these solutions work and optimize them before moving on to the next big thing.
• Considering AI from important ethical and privacy angles: Whether you’re in a highly regulated industry or not, working with AI can be risky because of how some of these models train with and otherwise expose private data and business processes. Businesses should steer clear of AI companies that are unable or unwilling to explain their data collection and training processes; they should also work closely with these vendors to determine what additional AI privacy security safeguards need to be added to protect their data when using AI tools.
• Identifying and enforcing change management best practices: Businesses often make the mistake of subscribing to an AI tool and then letting it collect dust while employees continue to manage their workflows as usual. To make sure AI investments are threaded through your operations effectively, you must first determine what AI should support and why and how this support should be offered. From there, it’s important to train and retrain all impacted workers on how to work with AI in their roles.
• Addressing new security challenges: Because of how AI technology works with data and complex training algorithms, these models can expose your data and operational infrastructure to new security risks. Many businesses fail to prepare for these new attack surfaces, which can lead to severe breaches and data theft or loss.
• Staying up-to-date on changes in AI technology and regulations: The AI technology landscape — and the regulations that are trailing just behind — seems to change on a minute-by-minute basis. If your organization doesn’t keep up with these changes, you may find yourself in a position where you’re using a tool or working with a company that is operating unethically or is noncompliant with regulations that impact your organization. It is your responsibility to stay abreast of regulatory changes and confirm that your AI vendors and tools are in alignment.

Frequently Asked Questions (FAQs)

Why Do You Need an AI Strategy?

Businesses need an AI strategy because investing in AI technologies can be an expensive, risky, and time-consuming practice. Going in with clear objectives and a strategic framework for AI investment will help your team identify the best solutions and get them operational with minimal hassle. This strategy will also help you determine if AI companies, technologies, and methodologies align with your organization’s culture, long-term goals, and ethical and legal expectations.

What Is an Example of an AI Strategy?

An example of an AI strategy is the structured framework, objectives, and measured steps that go into an AI implementation project like deploying a customer-facing AI chatbot on your e-commerce site.

To do this effectively, your organization should follow an AI strategy to get the right stakeholders involved, establish a clear overarching goal, set up objectives and deadlines, determine steps and initiatives to move in that direction, and define metrics to measure the overall success of this strategic implementation.

In the case of the example listed here, this will involve actions like:

• Involving marketing, sales, IT, and product team stakeholders in decision-making
• Setting a goal for how you want the AI chatbot to interact with customers and pass off conversations to human agents
• Developing the steps and investments your organization needs to reach this goal
• Measuring the AI solution’s success with metrics like response accuracy, response speediness, and customer satisfaction

How Do You Implement AI into Your Business?

You should implement AI into your business through an iterative and ongoing strategic process. As business priorities, budgets, stakeholders, and the AI landscape change over time, it will be important to watch for these shifts and make changes to your AI tooling strategy accordingly. Taking measurable, distinct steps in your AI adoption journey will make it easier to pivot.

Bottom Line: Developing an AI Business Strategy That Works for You

AI business strategies should be custom-fitted to your organization, though the steps covered above provide a useful framework for getting started. Ultimately, you know what your business’s weaknesses are and what areas can most benefit from AI adoption. If you don’t know where these weaknesses are now, it’s time to start the internal discovery process and speak directly with internal stakeholders so you can identify where AI support is most needed.

When you begin to develop your AI business strategy, start by reflecting on what’s happening in your particular organization, industry, talent pool, and tool stack. All of these variables should influence the AI partnerships and tools you select, especially as many AI vendors are beginning to specialize in highly specific niches and use cases. If your workforce has limited AI experience or technical knowledge, it may be wise to research and partner with an AI-as-a-service or AI consulting company that has experience with your industry and the goals you are trying to accomplish.

To learn where the next generation of AI companies are headed, see our extensive overview: Top 75 Generative AI Startups

The post Creating a Winning AI Business Strategy: 8 Steps appeared first on eWEEK.

Larger tech companies like Google, Microsoft, and AWS are working hard to build their generative AI technologies as well, but these tech giants don’t always keep up with the agile gen AI startups that are willing to take risks in order to establish their AI niches.

We’ve created a list of the top 75 generative AI startups to watch today and over the next few years. Some of these companies, like OpenAI, have already proven themselves and turned into multi-billion dollar companies. Others have not yet emerged from early rounds of funding. Regardless of where they individually fall in their stages of development, each of these startups has generated enough buzz to earn a spot on our list of the top generative AI startups.

Top 10 Generative AI Startups: Best of the Best

1. OpenAI

OpenAI is one of the biggest AI names in the world and is certainly the largest in the generative AI space. Along with its prebuilt AI solutions, OpenAI also offers API and application development support for developers who want to use its models as baselines. Its close partnership with Microsoft and growing commitment to ethical AI continue to boost its reputation and reach. Most recently, the company has introduced Sora — a text-to-video tool — to its portfolio.

• Founded: 2015.
• Founded by: Sam Altman, Elon Musk, Ilya Sutskever, Greg Brockman, Trever Blackwell, Vicki Cheung, Andrej Karpathy, Durk Kingma, Jessica Livingston, John Schulman, Pamela Vagata, Wojciech Zaremba
• Category and use cases: Language modeling, content generation, image generation and editing, audio transcription and translation, custom and embedded model development.
• Core products and solutions: GPT-4, ChatGPT Free, ChatGPT Plus, ChatGPT Team, ChatGPT Enterprise, DALL-E 3, Whisper, and various fine-tuning and embedding models. API is also a key part of OpenAI’s solution stack.

2. Anthropic

Anthropic’s Claude platform is similar to OpenAI’s ChatGPT, with its large language model and content generation focus. First released widely in March 2023, Claude is viewed as a more customizable platform with less propensity for rude or inappropriate responses. Since its initial start, Claude has evolved into an enterprise-level AI assistant with high-level conversational AI capabilities, a large context window, and an API that allows users to build custom instances of Claude into their products.

• Founded: 2021.
• Founded by: Daniela Amodei, Dario Amodei, Jack Clark, Jared Kaplan, Sam McCandlish, Tom Brown
• Category and use cases: Content generation, coding, customer support, text translation, text classification, text summarization, search, legal document summarization, career coaching, workflow automation, text editing, API, conversational AI.
• Core products and solutions: Claude 3 and Claude API.

3. Cohere

Cohere offers NLP solutions that are specifically designed to support business operations. With Cohere’s conversational AI agent, enterprise users can quickly search for and retrieve all kinds of company information without searching through massive applications and databases. The organization’s different families of language models can be used for business tasks like document analysis, content writing (including for product descriptions), semantic search, and improved internal and external e-commerce experiences.

• Founded: 2019.
• Founded by: Aidan Gomez, Ivan Zhang, Nick Frosst
• Category and use cases: Retrieval-augmented generation, text generation, text classification, semantic search, enterprise conversational AI agent, multilingual embedding, access to language models.
• Core products and solutions: Command (including Command R and Command R+), Rerank, and Embed (including Classify).

4. Glean

Glean is a generative AI enterprise search company that relies on deep-learning models to understand natural language queries in the context of organizational, departmental, and individual user characteristics. Glean connects to a variety of enterprise apps and platforms, making it easier to set up and maintain access to various business information sources. With its focus on AI privacy and governance, small businesses and enterprises alike are investing in this solution for enterprise knowledge management.

• Founded: 2019.
• Founded by: Arvind Jain, Piyush Prahladka, Tony Gentilcore, TR Vishwanath
• Categories and use cases: Cognitive enterprise search, data ingestion and management, knowledge management, enterprise environment app, AI assistance, enterprise security and privacy management, data unification.
• Core products and solutions: Glean Workplace Search, Glean Assistant, Glean Knowledge Management, Glean Work Hub, Glean Connectors, and Glean Security.

5. Jasper

Jasper’s core product is designed specifically for business and marketing content generation. Some core areas where Jasper works well include social media, advertising, blog, email, and website content creation. It is a particularly effective tool for establishing a consistent brand voice and managing digital marketing campaigns. In early 2024, Jasper acquired the AI image platform, Clickdrop, and expects to increase its multimodal capabilities as a result of this acquisition.

• Founded: 2021.
• Founded by: Chris Hull, Dave Rogenmoser, John Philip Morgan.
• Category and use cases: Long-form and short-form content creation, dialog-driven content creation and language modeling, AI copilot, AI assistant browser extension, art creation, multi-language reading and writing.
• Core products and solutions: Jasper.

6. Hugging Face

Hugging Face is a community forum, similar to GitHub, that focuses on Artificial Intelligence (AI) and ML model development and deployment. Some of the community’s main specialties include text classification, question answering, image classification, translation, summarization, audio classification, and object detection. Most notably, Hugging Face offers users access to BLOOM, an open-source LLM that can generate content in 46 languages and 13 programming languages.

• Founded: 2016.
• Founded by: Clement Delangue, Julien Chaumond, Thomas Wolf
• Category and use cases: Open-source development community, multilingual content generation, public submissions and deployments of NLP, computer vision, access to third-party AI models.
• Core products and solutions: BLOOM, Enterprise Hub, Inference Endpoints, and AutoTrain.

7. Inflection AI

Founded by former leaders from LinkedIn and DeepMind in 2022, Inflection AI’s mission and goals were mostly kept under wraps until Pi, a personal AI that focuses on colloquial conversation and advice, was released in May 2023. Even before its initial release, the company had already received major funding rounds and indicated its plans to completely transform how humans can speak to and communicate with computers.

Most recently, two of Inflection’s co-founders — Mustafa Suleyman and Karén Simonyan — have left the company to work in a new AI division at Microsoft. In light of this change, the company announced a new CEO and plans to focus more heavily on offering an AI studio business so more users can access and customize their models.

• Founded: 2022.
• Founded by: Karén Simonyan, Reid Hoffman, Mustafa Suleyman
• Category and use cases: AI model studio, AI chatbot and LLM, human-to-computer communication in plain language, voice search, brain-computer interface (BCI), conversational AI, AI assistance.
• Core products and solutions: Pi.

8. Stability AI

Stability AI is a leading startup in the generative AI space for image and video content generation. Though the company has come under controversy for alleged copyright infringement of artists’ work as well as for some possible financial instability, Stable Diffusion in particular continues to be a popular solution, operating in the background of many other generative AI startups’ platforms.

• Founded: 2019.
• Founded by: Emad Mostaque
• Category and use cases: Text-to-image generation, image editing, audio and video generation, language modeling, open-source AI, 3D object generation and modeling, application development models, API and embed capabilities.
• Core products and solutions: Stable Diffusion 3, Stable Diffusion XL and Turbo, Japanese Stable Diffusion XL, Stable Video Diffusion, Stable Audio 2.0, Stable Video 3D, Stable Zero123, Stable TripoSR, various language models.

9. MOSTLY AI

MOSTLY AI’s synthetic data generation platform balances data democratization and app development efficiencies with data anonymity and security requirements. The platform has proven especially useful in the banking, insurance, and telecommunications industries. It is also compatible with many different operational environments, including for Kubernetes deployment, OpenShift deployment, and API and Python Client connectivity.

• Founded: 2017.
• Founded by: Klaudius Kalcher, Michael Platzer, Roland Boubela
• Category and use cases: Synthetic data generation for AI and software app development, test data generation, data anonymization, Python client synthetic data generation, AI and ML development, data analytics, testing and product development.
• Core products and solutions: MOSTLY AI.

10. Lightricks

Lightricks first gained notoriety with its social-media-friendly image editing app, Facetune. It has since expanded Facetune and its other apps with cutting-edge AI, making it possible to edit and generate new content and avatars for videos, photos, and art projects.

• Founded: 2013.
• Founded by: Amit Goldstein, Itai Tsiddon, Nir Pochter, Yaron Inger, Zeev Farbman
• Category and use cases: Text-to-image generation, image editing, video editing, art generation, avatar generation.
• Core products and solutions: Facetune, Photoleap, Videoleap, Popular Pays, Filtertune, Beatleap, Motionleap, Artleap, Lightleap, and Boosted.

Top 5 Generative AI Startups for Developers

1. AI21 Labs

AI21 Labs creates tools that focus heavily on contextual natural language processing for reading and writing. Third-party developers can build on AI21 Labs’ language models for their own text-based apps and services with AI21 Studio. Its recent emphasis on task-specific APIs makes it easier for businesses to ideate and scale industry-specific and business-specific models with little to no prompt engineering or fine-tuning necessary.

• Founded: 2017.
• Founded by: Ori Goshen, Yoav Shoham.
• Category and use cases: Language modeling, application development, content generation and editing, content summarization.
• Core products and solutions: Wordtune, Jamba, Jurassic-2, task-specific APIs, and AI21 Studio.

2. Tabnine

Tabnine offers generative AI code assistance for software development. It can be useful for both experienced and novice coders due to its focus on code completion and natural language prompting. Many users select this tool for both its robust coding features and its built-in security and governance features.

• Founded: 2017.
• Founded by: Dror Weiss, Eran Yahav.
• Category and use cases: AI-assisted development, code completion, code automation, natural language coding and prompting, coding recommendations.
• Core products and solutions: Tabnine and Tabnine Chat.

3. Mistral AI

Mistral AI is an AI company that offers deployment-ready solutions like le Chat but is more focused on providing its customers with open generative AI models and other developer-friendly resources for scalable AI. Mistral model access comes in various sizes, meaning users can prioritize affordable and lightweight agility or scalable and high-powered performance.

• Founded: 2023
• Founded by: Arthur Mensch, Guillaume Lample, Timothée Lacroix
• Category and use cases: Developer-facing open AI models, deployment resources, AI chat, AI platform.
• Core products and solutions: Mistral Large, Mistral Small, Mistral Embed, Mistral 7B, Mixtral 8x7B, le Chat, and la Plateforme.

4. Codeium

Codeium provides coders, programmers, and even less-technical users with resources to generate logical code for their projects. Autocompletion is an option, but users can also engage with Codeium through chat and gain more contextual knowledge for why code looks a certain way or how it could be optimized. Codeium can be experimented with in the playground environment, and it can also be used on a wide range of IDEs.

• Founded: 2021.
• Founded by: Varun Mohan, Douglas Chen.
• Category and use cases: AI code completion, AI chat, contextualization, AI playground, developer resources and toolkit.
• Core products and solutions: Codeium Chat, Codeium Autocomplete, Codeium Search.

5. Clarifai

Clarifai’s multipurpose platform offers resources to build, deploy, and manage AI and the data that goes into it throughout the full lifecycle. The solution can be used to label and otherwise prepare data for projects, and from there, users can build and operationalize models in various formats and environments, including in serverless and edge versions. Clarifai is best known for its computer vision, generative AI foundation model, and NLP solutions; however, it also offers a range of professional services to customers.

• Founded: 2013.
• Founded by: Matt Zeiler.
• Category and use cases: LLM production, computer vision platform, unstructured data and content modeling, AI lake, data preparation, model building, model operationalization, foundation models.
• Core products and solutions: Production AI Platform, AI Lake, Scribe, Spacetime, Enlight, Armada, Mesh, Flare, UI MOdules, AI Lab, LabelForce, ModelForce.

Top 7 Generative AI Startups for Marketing and Sales

1. Gong

Gong extends to customers a full-service revenue intelligence solution that uses generative AI and other advanced features to support revenue forecasting, customer service engagement, analytics, team productivity management, and more. The company offers a wide range of enterprise-level features, including the Gong partner network and a high-powered Trust Center for security and compliance management. Among its customers are ADP, NASDAQ, LinkedIn, Dropbox, and Snowflake.

• Founded: 2015.
• Founded by: Amit Bendov, Eilon Reshef.
• Category and use cases: Customer and conversation analytics, contextualized customer analysis, AI recommendations, revenue intelligence, forecasting, team productivity management.
• Core products and solutions: Gong Reality Platform and Gong AI.

2. Twain

Twain is designed to help sales professionals write content — particularly outreach emails — that works better for sales outreach. It not only can generate its own content but can also make detailed recommendations for edits to content that a user submits. While it is primarily used for sales content generation, it also works well for recruiting and personal use cases.

• Founded: 2021.
• Founded by: Mohamed Chahin.
• Category and use cases: Content generation, sales outreach, recruitment messaging, content recommendations.
• Core products and solutions: Twain, Chrome extension.

3. Bertha.ai

Bertha.ai is a content generation solution for WordPress users in particular, though it also works with sites like Shopify, WooCommerce, Wix, and Squarespace. It can help with creating written content and imagery for blog posts and other webpages as well as other forms of digital marketing copy.

• Founded: 2021.
• Founded by: Andrew Palmer, Vito Peleg.
• Category and use cases: Content generation, image and illustration creation, blog writing, product description writing.
• Core products and solutions: Bertha AI, Chat, Write Anything, Long Form Content, and Bertha Chrome Extension.

4. Tome

Tome is a creative generative AI company and platform that was established by two former Meta managers and has quickly gained recognition for its creative and usable interface. The founders’ goal was to create a fun tool that fills in some of the gaps left by tools like PowerPoint, especially when it comes to mobile usability. Tome generates both relevant and somewhat randomized content for users’ presentations, acting as a business partner, assistant, or trusted friend who helps to brainstorm the best possible content for the project.

• Founded: 2022.
• Founded by: Keith Peiris, Henri Liriani.
• Category and use cases: AI presentation generation, AI storytelling, marketing and sales, product pitching, creative projects.
• Core products and solutions: Tome.

5. CopyAI

CopyAI takes on the unique role of creating generative AI for go-to-market workflows and strategizing, giving users the technology necessary to more intelligently attract, land, adopt, retain, and expand their reach. The platform is most often used by marketing and sales professionals to help them work through their GTM processes more quickly and smoothly. CopyAI can be used to translate content for a multilingual or global audience, generate blog and social media content, and create the content and structure necessary for a successful email marketing campaign.

• Founded: 2020.
• Founded by: Paul Yacoubian, Chris Lu.
• Category and use cases: GTM AI, task automation, content generation, CRM content enrichment, marketing, sales, brand voice management, email marketing, translation.
• Core products and solutions: CopyAI platform, AI Marketing OS, AI Sales OS.

6. Narrative BI

Narrative BI uses generative AI to build out a new chapter in data democratization. The goal of this platform is to turn data, business intelligence, and analytics into narratives that are easier for all users to understand and contextualize within their roles and the greater frame of the business. With this approach, all employees can more effectively contribute to decision-making wherever important business data lives, including in and through Google Analytics 4, Google Ads, Google Search Console, Facebook Ads, Instagram Ads, LinkedIn Ads, HubSpot, CSV, Salesforce, Slack, Snowflake, Amazon Redshift, PostgreSQL, and MySQL. An API and custom integrations are also available.

• Founded: 2020.
• Founded by: Michael Rumiantsau, Yury Koleda.
• Category and use cases: AI insights for marketing and sales, social media and analytics integrations, automated generative BI, narrativized analytics.
• Core products and solutions: Narrative BI platform and Generative BI.

7. Anyword

Anyword is a generative AI writing solution that focuses specifically on marketing and other business outcomes. Users can optimize existing content for better performance, personalize messaging on their websites at scale, and train AI to understand their brand’s voice and target audience. With features like a predicted performance score and the ability to use Anyword within other tools — including ChatGPT, Notion, and HubSpot — users can improve their content with minimal hassle.

• Founded: 2013.
• Founded by: Yaniv Makover, Adam Habari.
• Category and use cases: Marketing content generation, demand generation, SEO support, API, website automation, LLMs, copy intelligence.
• Core products and solutions: Data-Driven Editor, Blog Wizard, Copy Intelligence Platform, private LLMs, Performance Boost AI Chrome Extension.

Top 13 Generative AI Startups for Audio, Video, and Creative Projects

1. Synthesia

Synthesia is a generative AI company that focuses on AI video creation for personal and enterprise use. Users can rely on AI avatars and voices to communicate in training, marketing, and how-to videos in 120 different languages. Most significantly, professional-looking videos can be generated from users’ text inputs.

• Founded: 2017.
• Founded by: Lourdes Agapito, Matthias Niessner, Steffen Tjerrild, Victor Riparbelli
• Category and use cases: Video generation, AI voice and avatar generation, video templates.
• Core products and solutions: Synthesia.

For a detailed list of the leading AI video providers, see our guide: 10 Best AI Video Generators

2. Midjourney

Midjourney is a generative AI solution for image and artwork creation that primarily gives users access to its features and community support through Discord. Though Midjourney has faced some of the same controversies as Stability AI, the company continues to grow its capabilities and user base. It is particularly well known for its advanced and granular image editing features.

• Founded: 2022.
• Founded by: David Holz.
• Category and use cases: Natural-language-driven image generation, image enhancements and modifications, image editing.
• Core products and solutions: Midjourney.

3. MURF.AI

MURF.AI is a leading voice AI generation company that is frequently praised for the quality of its multilingual voices as well as for its solutions’ ease of use. Murf comes with various third-party integrations that are relevant for creative content production. It also provides users with supportive resources and how-to guides for a diverse range of content types, including Spotify ads, L&D training, animation, video games, podcasts, and marketing and sales videos.

• Founded: 2020.
• Founded by: Divyanshu Pandey, Ankur Edkie, Sneha Roy.
• Category and use cases: Text to speech, voice cloning, AI dubbing, AI translation, API, voices for Windows, voice content generation.
• Core products and solutions: Murf, Murf Studio, Murf API, and AI Translation.

4. PlayHT

PlayHT is a leading AI voice generation company that has gone beyond the purview of most of its competitors to develop solutions like enterprise-specific AI voice agents and podcast.ai, a subsidiary of PlayHT that brings forth a weekly podcast created with generative AI voices and transcripts. The podcast covers a different topic each week and has even used Steve Jobs recordings and biographical information to record an episode with “him.”

• Founded: 2016.
• Founded by: Hammad Syed, Mahmoud Felfel.
• Category and use cases: AI voice generation, AI voice agents, podcast content generation, content transcription.
• Core products and solutions: PlayHT Studio, AI Voice Agents, PlayHT API, podcast.ai.

5. ElevenLabs

ElevenLabs is both an AI research firm and the producer of AI voice generation technology for personal and business use. It is frequently praised for its audio quality as well as its enterprise-level scalability and reasonable pricing structure. The company reached official unicorn status in January 2024, with an estimated value of $1.1 billion.

• Founded: 2022.
• Founded by: Piotr Dąbkowski, Mateusz Staniszewski.
• Category and use cases: AI voice generation, voice cloning, dubbing, text to speech, speech to speech, API, fully managed video and podcast dubbing.
• Core products and solutions: Text to Speech, Speech to Speech, Projects, Dubbing ElevenStudios, API, Languages, Voice Cloning, and Voice Library.

6. Colossyan

Colossyan is a leading competitor in the AI video generation space because its product includes several enable users to create high-quality corporate training videos with no actors or scripting necessary. Customization is a core part of this solution, and the AI assistant is a helpful resource for users who want support in content creation.

• Founded: 2020.
• Founded by: Dominik Mate Kovacs, Kristof Szabo, Zoltan Kovacs.
• Category and use cases: AI video templates, AI avatars, text to speech, AI assistant, auto translation, AI voices, custom avatars, prompt to video generation.
• Core products and solutions: Colossyan.

7. AssemblyAI

AssemblyAI is a unique generative AI company that focuses on speech AI modeling, specifically for transforming speech to text after important conversations and recordings, like calls, video calls, and podcasts. The tool includes several enterprise-ready features, including strong sentiment analysis capabilities and PII redaction. Most recently, the company released Universal-1, a multilingual speech recognition model that apparently surpasses Whisper-3 in performance accuracy and speed.

• Founded: 2017.
• Founded by: Dylan Fox.
• Category and use cases: Speech AI modeling, speech to text, speaker diarization, auto punctuation and casing, confidence scores, automatic language detection, speech recognition modeling, streaming transcriptions, sentiment analysis, content moderation, PII redaction.
• Core products and solutions: AssemblyAI, Universal-1, Speech-to-Text, Streaming Speech-to-Text, Speech Understanding, Audio Intelligence, and LeMUR.

8. Plask

Plask creates technology to make animation easier and more cost-effective. The tool can be used to create animated or hyper-realistic 3D motion videos. It automates the entire process of creating designs and movement. This type of automated animation is certainly the leading edge of a larger trend, as AI influences movie and TV production by allowing faster, cheaper episode creation.

• Founded: 2020.
• Founded by: Jaejun Yu, Junho Lee.
• Category and use cases: AI-generated animation, prototyping, AI motion capture, 3D character building.
• Core products and solutions: Plask Motion.

9. LOVO

LOVO is a video and voice AI generation company that offers most of its features through a comprehensive platform called Genny. It’s a solid contender for users who need a platform with high-quality features for both voice and video, as well as built-in features for AI art generation and AI writing.

• Founded: 2019.
• Founded by: Tom Lee.
• Category and use cases: AI video generation, voice cloning, content generation, art generation, text to speech, corporate training, social content generation.
• Core products and solutions: Genny, Auto Subtitle Generator, Online Video Editor, AI Art Generator, Text to Speech, Voice Cloning, and AI Writer.

10. DeepBrain AI

DeepBrain AI is yet another AI video generation company that is moving upward – rapidly – through the ranks. It includes many of the video features you would expect from this type of generative AI — AI avatars, AI voices, templates, and video editing tools, for example — but it takes things a step further with truly interactive conversational avatars. Its AI Humans solution is currently taking the world by storm, with recent reports indicating that certain governments are investing in this technology to create virtual assistants for their bureaucracies, and others are investing in this technology to support communication needs for ALS patients and others with disabilities.

• Founded: 2016.
• Founded by: Eric Seyoung Jang.
• Category and use cases: AI video generation, AI video editing, AI avatars, deepfakes, text to video, text to speech, conversational AI, AI conversation simulations.
• Core products and solutions: AI Studios and AI Human.

11. Elai.io

Elai.io provides AI video generation tools to users of all backgrounds, but its emphasis is on business and enterprise audiences. Built-in collaborative features include interactive storyboarding, customizable brand kits, and API power to support custom and scalable use cases. While Elai.io is a new competitor in marketplace that’s becoming crowded, it’s emphasis on the lucrative enterprise AI market gives it an edge.

• Founded: 2021.
• Founded by: Vitalii Romanchenko, Alex Uspenskyi.
• Category and use cases: AI video generation, custom avatars, voice cloning, text to video, AI storyboarding, AI video editing, auto translation, video personalization.
• Core products and solutions: Elai platform, AI Storyboard, and API.

12. Sudowrite

Sudowrite is a creative tool offered by an generative AI startup that provides AI support for writers and authors. With this platform, users can flesh out an existing outline, expand on their current story, bounce ideas off the intelligent agent, or take other steps to expand their creative stories — including with images. Though this is a controversial platform, especially among creatives, several users have commented on the impressive nature of Sudowrite’s capabilities.

• Founded: 2020.
• Founded by: Amit Gupta, James Yu.
• Category and use cases: AI writing assistant, AI content generation, autocompletion and expansion, AI rewrite, AI recommendations, AI art generation.
• Core products and solutions: Sudowrite, Describe, Story Engine, Write, Expand, Rewrite, Feedback, Canvas, Brainstorm, Visualize, and Chrome Extension for Google Docs.

13. Tavus

Tavus is a generative AI company that creates new versions of videos that users have already created based on specific viewer qualities and other personalization requirements. The foundational template videos that users create give Tavus enough material to generate believable audio and visuals for future videos on different topics, making it possible to record only one video and send custom messages to each of your contacts.

• Founded: 2020.
• Founded by: Hassaan Raza, Quinn Favret.
• Category and use cases: Automated video generation and personalization, voice cloning, media blending, lip sync, video templates, recruiting and marketing campaign videos.
• Core products and solutions: Tavus, AI video APIs.

Top 10 Generative AI Startups for Healthcare, Pharmaceuticals, and Life Sciences

1. Hippocratic AI

Hippocratic AI takes a unique and much-needed approach to AI healthcare software, offering a foundation model and comprehensive resources for managing patient care and relationships. The platform is designed to follow Health Information Privacy (HIPAA) and other ethical expectations for healthcare, with AI healthcare agents that have been scored and reviewed by nurses and healthcare professionals. Most recently, Hippocratic AI has received funding from and started a partnership with NVIDIA, so expect this platform to scale quickly in the coming months.

• Founded: 2022.
• Founded by: Munjal Shah, Vishal Parikh, Meenesh Bhimani, Subho Mukherjee, Alex Miller, Saad Godil, Kim Parikh, Debajyoti Datta, Paul Gamble.
• Category and use cases: Generative AI healthcare agents, artificial health general intelligence (HGI), LLM designed with constellation architecture.
• Core products and solutions: Polaris model and AI healthcare agents.

2. Paige AI

Paige AI uses generative tissue-based AI for optimized cancer diagnostics and pathology. The platform currently specializes in breast cancer and prostate cancer diagnoses but also offers other diagnostic resources for oncology professionals, hospitals, and labs.

• Founded: 2017.
• Founded by: David Klimstra, Norman Selby, Peter Schüffler, Thomas Fuchs
• Category and use cases: Cancer diagnostics, computational pathology, biomarker detection, AI-driven image viewer.
• Core products and solutions: Paige platform, Paige Prostate Suite (including Paige Prostate Detect), Paige Breast Suite (including Her2Complete), and FullFocus.

3. Iambic Therapeutics

Iambic Therapeutics, previously known as Entos, is a company made up of top scientists, biotechnology experts, and machine learning experts who are working to optimize drug discovery and therapeutics in oncology and other challenging fields. Their pipeline therapeutics has several different candidates in early-phase trials right now or scheduled for the coming months. The company has also patented or contributed to several AI-driven computational processes for drug discovery, all of which are part of its flagship platform.

• Founded: 2019.
• Founded by: Fred Manby, Sarah Trice, Thomas Miller.
• Category and use cases: Drug discovery and development, physics-informed AI design, high-throughput experimentation, generative diffusion for protein-ligand structure prediction, AI-accelerated quantum chemistry, multi-parameter lead selection, generative molecular design, oncology therapeutics.
• Core products and solutions: Pipeline of oncology therapeutics, NeuralPlexer, OrbNet, PropANE, Magnet.

4. Insilico Medicine

Insilico Medicine is a pharmaceutical research and development startup that uses generative AI and machine learning to create more efficient processes across biology, chemistry, and analytics. It’s focused on reducing the time and cost of drug development, particularly in areas such as immunology, oncology, central nervous system disorders, and fibrosis.

• Founded: 2014.
• Founded by: Alex Zhavoronkov.
• Category and use cases: Novel molecules generation with de-novo drug design and scalable engineering, clinical trial design and predictive AI, deep biology analysis engine for multi-omics target discovery.
• Core products and solutions: PHARMA.AI Suite, PandaOmics, Generative Biologics, Chemistry42, and inClinico.

5. Etcembly

Etcembly is a company that is improving T-cell receptor immunotherapies with its machine-learning platform, EMLy. The platform sifts through complex TCR patterns and datasets to discover and identify personalized TCR therapeutic options for patients. Near the end of 2023, the company also developed what it considers the world’s first immunotherapy drug designed through generative AI.

• Founded: 2020.
• Founded by: Michelle Teng, Jacob Hurst.
• Category and use cases: ML database for TCR immunotherapies, AI-driven TCR discovery and identification, computer-assisted engineering, biotechnology, generative AI drug design.
• Core products and solutions: EMLy, bispecific T cell engager.

6. Biomatter

Biomatter uses its Intelligent Architecture platform to design and develop proteins for health and sustainable manufacturing. It also goes beyond more traditional human protein expectations and supports use cases across molecular biology, food and beverage, biotherapeutics, and agriculture projects.

• Founded: 2018.
• Founded by: Donatas Repečka, Laurynas Karpus, Rolandas Meškys, Vykintas Jauniskis.
• Category and use cases: Enzyme and protein design, sustainable manufacturing, biotherapeutics.
• Core products and solutions: Intelligence Architecture platform.

7. Activ Surgical

Activ Surgical uses intraoperative surgical intelligence to give surgeons real-time information and better visuals during surgery. With some of the company’s most recent developments, surgeons can also perform surgeries with the help of augmented reality overlays.

• Founded: 2017.
• Founded by: Peter Kim, Seth Teicher.
• Category and use cases: Surgical intelligence and assistance, multimodal advanced visualization, tissue evaluations.
• Core products and solutions: ActivEdge Platform, ActivSight Intelligent Light.

8. Kaliber Labs

Kaliber Labs focuses on developing AI-powered surgical software for arthroscopic surgery needs. The company also provides solutions, such as Rekap, to help patients and other members of the surgical team get the analytics and other information they need more seamlessly.

• Founded: 2015.
• Founded by: Ray Rahman.
• Category and use cases: Digital surgical assistance, AI-labeled patient communication platform, AI-powered feedback for surgeons, automated surgery stage recognition.
• Core products and solutions: Kaliber products are still in development, and some will require FDA approval.

9. Osmo

Osmo, founded in 2023 as a spinout from Google Research, uses machine learning and has created a map of odors and scents to help computers predict how something smells based on its molecular structure. From there, the company has begun working on “teleporting scent” and generating artificial smells. It hasn’t gone much farther than that at this point, but the vendor has stated its goal to use this technology to support human health and wellness.

• Founded: 2023.
• Founded by: Alex Wiltschko.
• Category and use cases: Olfactory science and computer smelling capabilities.
• Core products and solutions: Osmo AI, Scent Teleportation (in the works).

10. Aqemia

Aqemia uses AI that includes experimental data to scale drug discovery in the pharmatech space. The company touts how it uses both quantum and statistical mechanics algorithms to achieve better outcomes for critical and niche disease categories. At the end of 2023, a $140 million multi-year collaboration with Sanofi was announced, so expect to see more innovations from Aqemia on the horizon.

• Founded: 2019.
• Founded by: Emmanuelle Rolland-Martiano, Maximilien Levesque
• Category and use cases: Drug discovery, drug discovery pipeline, drug design.
• Core products and solutions: Drug discovery pipeline; more in the works.

Top 4 Generative AI Startups for Synthetic Data and Data Analytics

1. Synthetaic

Synthetaic’s platform, RAIC, is primarily designed to generate AI models that can ingest and analyze unstructured and unlabeled datasets from videos, satellite imagery, and video and drone footage. The company famously tracked the origin of a Chinese balloon in February 2023. The company has also partnered with Microsoft and received additional funding for image-focused data analysis, which will likely lead to new products and use cases in the near future.

• Founded: 2019.
• Founded by: Corey Jaskolski.
• Category and use cases: AI prototyping, unstructured data analysis, geospatial analysis, drone-based monitoring, content moderation, model training, unlabeled data ingestion, video security.
• Core products and solutions: RAIC.

2. Synthesis AI

Synthesis AI is a cutting-edge synthetic data generation startup that creates computer-vision-driven imagery, videos, and human simulations. Its use cases span across industries and focus on ethical AI development. Most recently, the company also started OpenSynthetics, an open community for synthetic data usage and development.

• Founded: 2019.
• Founded by: Yashar Behzadi.
• Category and use cases: Synthetic data generation for computer vision, image labeling, image generation, video generation, ID verification, automotive and driver monitoring, pedestrian detection, teleconferencing, security scenarios, virtual try-on, avatar creation, AR/VR/XR, 3D human models.
• Core products and solutions: Data Visualizer, OpenSynthetics.

3. Syntho

Syntho is a synthetic data generation startup that uses generative AI to create synthetic data twins of actual sensitive data. Syntho’s Syntho Engine is often used for realistic product demos, data analytics, and test data generation. Many users select this platform for its comparative ease of use and democratized approach to synthetic data generation and analytics.

• Founded: 2020.
• Founded by: Marijn Vonk, Simon Brouwer, Wim Kees Janssen
• Category and use cases: Synthetic data generation, test data generation, data analytics, smart de-identification.
• Core products and solutions: Syntho Engine.

4. GenRocket

GenRocket is a synthetic data generation solutions provider that emphasizes automation and enterprise-level scalability for data. Test data can be automatically generated, and what’s more, it can be generated in a dynamic format that’s easy to adjust and scale up as needed. The platform works across a variety of industries and use cases, including finance and insurance, healthcare, AI and ML model testing, ETL and big data testing, and other digital transformation projects.

• Founded: 2012.
• Founded by: Garth Rose, Hycel Taylor.
• Category and use cases: Synthetic data generation, test data generation, data subsetting, data masking, data security, anomaly detection, fraud detection.
• Core products and solutions: GenRocket Test Data Automation (TDA).

Top 7 Generative AI Startups for Customer Service and Customer Experience

1. Gridspace

Gridspace offers solutions for organizations that want to better automate, manage, and analyze contact center and customer interactions. The company offers voice bots and live agent training, making it possible to create a hybrid bot-human agent workforce in healthcare, retail, and other customer-service-driven sectors.

• Founded: 2012.
• Founded by: Anthony Scodary, Evan Macmillan, Nico Benitez
• Category and use cases: Conversational AI, virtual agents and voice bots, virtual contact centers, observability and call monitoring, customer service.
• Core products and solutions: Gridspace Grace, Gridspace Sift Analytics, and Gridspace Pulse.

2. Revery AI

Revery AI offers a virtual dressing room and try-on experience that uses generative AI to help users more accurately visualize how clothing will look on them in real life. The company has partnered with some fashion retailers already to create a more integrated virtual shopping experience for users, and through this process, has developed a more comprehensive AI shopping assistant.

• Founded: 2020.
• Founded by: Jeffrey Zhang, Kedan Li.
• Category and use cases: Virtual dressing room and try-on, virtual reality, garment tagging and classification, garment segmentation, smart shopping assistant, e-tail.
• Core products and solutions: Revery.

3. Veesual

Veesual is a generative AI startup that uses deep learning and image generation to enable virtual try-ons for fashion and e-commerce. It gives users the ability to select the model that looks most like them and sort through high-res images of different clothing items.

• Founded: 2020.
• Founded by: Damien Meurisse, Eric Gillaume, Maxime Patte.
• Category and use cases: Virtual try-on and image generation.
• Core products and solutions: Mix & Match, Switch Model, and Digital Dressing Room.

4. Frame AI

Frame AI is a customer service and general audience analytics platform that uses artificial intelligence to support users who want to better understand their audiences’ wants and needs. The company focuses on behavioral and sentiment analysis, customer-specific insights, and customer segmentation. The platform uses stream-trigger augmented generation architecture rather than retrieval-augmented generation (RAG), meaning it’s focused on paying close attention to what’s happening in your business’s specific data stream and building intelligence around that.

• Founded: 2016.
• Founded by: George Davis, Brandon Reiss, Jesse St. Charles, John Gu, Robbie Mitchell.
• Category and use cases: AI analytics for customer service, AI answer engines, trend detection, marketing, customer service, product feedback, stream-trigger augmented generation (STAG).
• Core products and solutions: Frame AI.

5. Zowie

Zowie is a generative AI and conversational AI company that focuses on customer service in e-commerce environments. The platform includes a wide variety of intelligent customer support chatbots, including bots that are focused on email and sales conversations. The company also operates with its own LLM, X2, which is specifically designed for e-commerce conversational scenarios and is GDPR- and SOC-2 compliant.

• Founded: 2018.
• Founded by: Maja Schaefer, Matt Ciolek.
• Category and use cases: AI customer service, e-commerce customer service, conversational AI, chatbot, business intelligence, e-commerce focused LLM.
• Core products and solutions: Zowie Chatbot, Zowie Emailbot, Zowie Inbox, Zowie Salesbot, Zowie Proactive Chats, Zowie Business Intelligence, and Zowie X2.

6. Forethought

Forethought is a top provider of generative AI-driven customer service technology, with various features built in to help businesses understand and better direct customer queries more efficiently. At this time, most of Forethought’s customers are focused in e-commerce, SaaS, fintech, and travel companies. At the end of 2023, the company began to use Autoflows, a new feature for its Solve product that helps users autonomously manage policy creation and issue resolution for a variety of customer service and ticketing workflows.

• Founded: 2017.
• Founded by: Deon Nicholas, Sami Ghoche, Colm Doyle.
• Category and use cases: AI-powered customer service, AI-powered employee resources, support ticket management, conversational routing.
• Core products and solutions: SupportGPT, Triage, Assist, Autoflows, Solve, and Discover.

7. Lily AI

Lily AI is a product management and customer service AI company that helps businesses understand their customers and create smoother shopping experiences for their customers. The platform includes features for product attribution and labeling, site search support, AI-powered recommendations, and demand forecasting. With recent funding rounds and the introduction of a customer-focused content generation solution to the Lily AI stack, expect to see more growth from this company in the coming months.

• Founded: 2015.
• Founded by: Purva Gupta, Sowmiya Narayanan.
• Category and use cases: Branded content generation, product description generation, demand forecasting, recommendations, site search.
• Core products and solutions: Product Attribution, Lily E-Commerce, and Lily Demand Forecasting.

Top 8 Generative AI Startups for Gaming and Entertainment

1. Runway

Runway is a now-established leader in AI-powered, cinema-quality video and content production. Specifically with Runway Studios, filmmakers of varying skill levels can use Gen-1 and Gen-2 models, as well as several other image and content editing tools, to create high-quality video content without actors or original footage. With OpenAI’s recent announcement of the text-to-video platform, Sora, Runway is expected to compete against the new tool and perhaps optimize its existing feature set or add new features to win this race.

• Founded: 2018.
• Founded by: Cristóbal Valenzuela, Anastasis Germanidis, Alejandro Matamala-Ortiz.
• Category and use cases: Text to video generation, video to video generation, text to image generation, image to image generation, frame interpolation, image expansion, 3D texture generation, image variations, inpainting, motion tracking, image editing and effects.
• Core products and solutions: Gen-1, Gen-2, and Runway Studios.

2. Latitude.io

Latitude.io is one of the first and foremost providers of AI-generated gaming experiences. With its flagship AI Dungeon, users can enter actions into the game while AI drives the rest of the game narrative forward.

• Founded: 2019.
• Founded by: Alan Walton, Nick Walton.
• Category and use cases: Gaming.
• Core products and solutions: AI Dungeon.

3. Character.AI

Character.AI is a company that offers creative ways to develop and chat with user-created characters. Though the tool can simply be used for fun conversations with “real” or imagined people, it can also be used to simulate important conversations like job interviews.

• Founded: 2021.
• Founded by: Daniel De Freitas, Noam Shazeer.
• Category and use cases: Character generation with virtual chat and entertainment.
• Core products and solutions: Character.ai.

4. Charisma Entertainment

Charisma Entertainment provides a plug-and-play platform for various entertainment companies and storytellers to create realistic characters and storylines that adjust to player/user inputs. Examples of media created with Charisma include The Kraken Wakes game and the Will Play virtual learning platform.

• Founded: 2015.
• Founded by: Guy Gadney.
• Category and use cases: AI storytelling, entertainment, gaming, virtual learning, intelligent character development, scripting tools, generative AI dialogue engine.
• Core products and solutions: Charisma.ai.

5. Replika

Replika is a generative AI solution that creates AI companions for AI-generated chats that have a more personal touch. The interface of this app is designed to not only allow users to have realistic conversations but also to spend time with their Replika characters in augmented reality experiences.

• Founded: 2015.
• Founded by: Eugenia Kuyda.
• Category and use cases: Conversational AI, AI companion/avatar generation, augmented reality.
• Core products and solutions: Replika.

6. Aimi.fm

Aimi.fm provides users with a generative AI music player that generates endless loops of music in different genres for listeners. With Aimi Studio, music producers of all skill levels can access basic music creation functionalities. With Aimi Music Services, music as a service capabilities are available for business and enterprise users who want to create copyright and royalty-free music.

• Founded: 2019.
• Founded by: Edward Balassanian.
• Category and use cases: Generative music creation, music production, content curation.
• Core products and solutions: Aimi, Aimi Studio, and Aimi Music Services.

7. Inworld AI

Inworld AI is a company that uses generative AI and text-to-character prompts to help gaming and media companies make non-player characters (NPC) seem more realistic. These characters may appear in traditional video games, VR, training, and other types of digital entertainment and experiences.

• Founded: 2021.
• Founded by: Ilya Gelfenbeyn, Kylan Gibbs, Michael Ermolenko.
• Category and use cases: NPC character generation, gaming, training and education, customer experience agents, other forms of digital entertainment and interaction.
• Core products and solutions: Inworld Engine, Inworld Studio, Inworld Core, and Inworld Arcade.

8. SOUNDRAW

SOUNDRAW is a generative AI solution for music composition that can be tailored to different genres, instruments, and other musical variables. It is most frequently used to generate music that can be used in the background of video creations. The company also offers a music generation API to support business users who want to incorporate custom music into their products.

• Founded: 2020.
• Founded by: Daigo Kusunoki.
• Category and use cases: Music and audio generation for videos, podcasts, games, social media, TV, radio, and other mediums.
• Core products and solutions: SOUNDRAW, AI Music Generation API.

Top 8 Generative AI Startups for Project Management, Legal, and Business Operations

1. Notion

Notion has found much of its success in providing task management and other kinds of daily work management capabilities to creatives and other project teams. Notion AI was released to the public in early 2023 and quickly gained traction as an option for teams that want to summarize notes, generate and fill out tables, create quick lists and action items, and write emails with the help of generative AI.

• Founded: 2013.
• Founded by: Chris Prucha, Ivan Zhao, Simon Last
• Category and use cases: Content generation, content summarization, content suggestions and translations, Q&A, note taking, email writing, task management.
• Core products and solutions: Notion AI, Wikis, Projects, and Docs.

2. Harvey

Harvey is a legal AI startup that has grown incredibly quickly, reaching an estimated $715 million valuation after about a year in operation. The company targets its solutions for elite law firms and professional services firms, now offering its tools and support through a Microsoft Azure professional services platform. With recent additional funding rounds, a growing number of top-tier law firm partnerships, and its recent acquisition of Mirage, Harvey is a startup to watch closely.

• Founded: 2022.
• Founded by: Winston Weinberg, Gabriel Pereyra.
• Category and use cases: Legal generative AI, AI support for professional services, AI chatbot, AI-powered professional services platform.
• Core products and solutions: AI-powered professional services platform on Microsoft Azure; most other solutions in the works or under wraps.

3. Ironclad

Ironclad offers AI contract management software for industries and use cases ranging from legal and procurement to marketing, sales, IT, HR, and finance. The platform is designed to help users manage the entire contract lifecycle, providing tools for designing, editing, and reporting on the results of different contracts and terms. Ironclad AI is a subset of the platform that includes intelligent contract analysis and management tools for automated approvals, flagging, and other support that frees up human employees for more complicated tasks and strategy work.

• Founded: 2015.
• Founded by: Jason Boehmig, Cai GoGwilt.
• Category and use cases: AI-powered contract lifecycle management, workflow designer, AI editor, reporting, embedded contracts, terms management.
• Core products and solutions: Ironclad CLM Software and Ironclad Clickwrap.

4. Taskade

Taskade is a productivity and task management solutions company that uses AI agents, AI writing assistants, and other AI-supported tools to help users manage their tasks more effectively. Users can take advantage of Taskade for task list generation and other creative project management visualizations, as well as for more automated workflows in PM, marketing, and sales task management. Taskade is used by many notable companies, including ESPN, Indeed, Verizon, Lyft, Sony, Costco, Nike, Tesla, Netflix, AirBNB, and Disney.

• Founded: 2017.
• Founded by: John Xie, Dionis Loire, Stan Chang.
• Category and use cases: AI task management, AI writing assistant, AI agents, mind map and flowchart creation, project management, marketing assistant, Ai prompt templates, document summarization, AI personas, action item and task generation.
• Core products and solutions: Taskade AI, powered by either OpenAI GPT-3.5 Turbo, GPT-4, or GPT-4 128K.

5. Humata

Humata is an AI company that focuses on helping users quickly get useful information out of PDFs and files, even if they’re incredibly long and dense. Users can upload as many files as they want, ask questions of the files, and highlight important citations for continued reference. A free version of this tool is available, but it also scales to enterprise-level requirements and includes enterprise security and compliance protections.

• Founded: 2022.
• Founded by: Cyrus Khajvandi, Dan Rasmuson.
• Category and use cases: PDF AI, content summarization for documents and files, document-focused Q&A, AI chat, citation highlights, website embedding.
• Core products and solutions: Humata.

6. Simplifai

Simplifai labels itself as an AI-powered automation platform and offers solutions primarily for banking, finance, insurance, and public sector companies. The platform itself includes many features to help users more effectively manage claims, debts, and other complicated document formats. With its commitment to GDPR, ISO/IEC 27001:2013, and other data privacy and security protocols — as well as its purpose-built InsuranceGPT tool — Simplifai is a leading provider of AI tools for highly regulated FinTech businesses.

• Founded: 2017.
• Founded by: Bård Myrstad, Erik Leung.
• Category and use cases: Intelligent automation, business process automation, AI-powered chat and customer service, claims and document handling, compliance management, banking and finance, insurance, public sector.
• Core products and solutions: Simplifai AI Automation Platform, Insurance GPT, Chat Processing Module, Document Processing Module, and Written Inquiry Processing Module.

7. PatentPal

PatentPal is a tool that is specifically designed with patent law requirements in mind. The tool looks at claims that have already been written by the author in order to generate tonally and factually accurate patent specification drafts on its own. All of this works in efforts to protect intellectual property.

• Founded: 2018.
• Founded by: Jack Xu.
• Category and use cases: Content generation and summarization for patent applications and intellectual property.
• Core products and solutions: PatentPal.

8. Adept AI

Adept AI is a newer OpenAI competitor that relies on AI and natural language processing commands to create better interactions between humans and computers in the workplace. It specifically automates and simplifies workflows in common business tools, including Salesforce and Google Sheets. Its ACT-1 model has been around for a bit now, but at the beginning of 2024, Adept released Adept Fuyu-Heavy. This is a highly capable multimodal model that should expand Adept AI’s customer base.

• Founded: 2022.
• Founded by: Ashish Vaswani, David Luan, Niki Parmar.
• Category and use cases: Business and software development, process automation, in-app task and goal development, generative AI models, multimodal content generation.
• Core products and solutions: ACT-1, Adept Fuyu-Heavy.

Top 3 Generative AI Startups for Chatbots, Search, and Personal Assistance

1. Perplexity AI

Perplexity AI is an AI search engine with an interface that slightly resembles all of the other leading chatbots and LLMs, but with a greater focus on personalization and conversational accessibility. In the responses that Perplexity generates, a detailed response and explanation is presented; several sources and relevant images are included in these results, as well as related queries that can support users who want to continue their research. In essence, this tool combines the best of a traditional search engine with an AI model’s power and conversational capabilities.

• Founded: 2022.
• Founded by: Aravind Srinivas, Denis Yarats, Andy Konwinski, Johnny Ho.
• Category and use cases: AI search engine, conversational AI, contextual AI.
• Core products and solutions: Perplexity.

2. Andi

Andi is a generative-AI-driven search bot that not only helps users search for information across the web but also summarizes and further explains that information. Users appreciate Andi’s clean interface and lack of ads. Since the Andi search engine was first released in early 2022, several updates have been made, but the product is still in testing and not intended for commercial or production use at this time.

• Founded: 2021.
• Founded by: Angela Hoover.
• Category and use cases: AI semantic search, chatbot, search results summarization.
• Core products and solutions: Andi.

3. You.com

You.com is a private and secure search engine that summarizes and personalizes results with generative AI. The generative AI solution is available as a Chrome extension and can be used through iOS, Android, and WhatsApp. The company also boasts YOU API, which it claims is the first full web index for LLMs.

• Founded: 2020.
• Founded by: Bryan McCann, Richard Socher.
• Category and use cases: AI-driven search, AI assistant, AI chat, content generation, content summarization and personalization.
• Core products and solutions: You.com, YOU API, and YOU LLM OS.

Why Is Generative AI Important?

Generative AI is important because it takes AI in a more mature direction, making the technology more accessible and useful to a larger audience.

Individuals can make use of this technology in their daily lives at little to no cost. More important, breakthrough innovations in areas like medical imaging and drug discovery are now possible to develop at scale because of generative AI.

Finally, this technology can better define, contextualize, and automate business operational tasks than previous types of AI ever could, making this a technology that is already being applied to and ripe for more enterprise use cases.

How Does Generative AI Work?

Depending on what users are trying to generate, generative AI works through different types of large AI large language models that undergo extensive training with massive datasets and deep learning algorithms on an ongoing basis.

This type of training allows generative AI tools to pull data-driven knowledge from all corners of the web and other resources, which makes it possible for these AIs to generate believable, human-like data and results. The deep learning, neural network design intends to mimic a human brain, which also helps generative AI software to understand context, relationships, patterns, and other connections that have traditionally required human thinking to grasp.

To learn how generative AI models work and how users can make the most of their capabilities, read this guide: What Is a Generative AI Model?

Bottom Line: The Generative AI Startups to Watch

Ever since the debut of ChatGPT in November of 2022, generative AI — and artificial intelligence in general — has taken a huge leap forward and permeated various industries and business sectors. Business leaders, consumers, and investors have all woken up to the vast potential for generative AI to support or take over countless tasks, freeing up actual humans to do higher-value work.

It’s the young companies on this list that will shape the future of AI, which in turn will shape the future of technology and society at large in many profound ways. Much like with any other nascent and dynamic area of technology, expect these players to shift their products, roles, and impact in the coming weeks and months and on an ongoing basis.

To see a list of the leading generative AI apps, read our guide: Top 20 Generative AI Tools and Apps 2024

The post Top 75 Generative AI Startups Innovating In 2024 appeared first on eWEEK.

To better understand how AI works in the Salesforce platform and how different business teams can take advantage of this technology, I did an in-depth study of Salesforce’s AI strategy and specific products and features. This guide covers these AI capabilities in detail.

Understanding Salesforce AI’s Capabilities

Salesforce is a longstanding favorite in the customer relationship management (CRM) software world, providing users with one of the most comprehensive solutions for managing relationships across departments, verticals, and initiatives. With Salesforce, specific clouds are set up so your teams can manage marketing, sales, customer service, e-commerce, and more with purpose-built resources that focus on both customer experience and powerful data and analytics to support internal decision-making.

Salesforce’s generative artificial intelligence has quickly increased the platform’s native automation, workflow management, data analytics, and assistive capabilities when managing customers throughout their lifecycles. Nowhere is this more evident than with Salesforce Copilot, which assists internal users with their outreach and analysis taskwork while also creating a smoother user experience externally.

What Is Salesforce Einstein?

Salesforce Einstein is a set of purpose-built, native AI technology and features for the Salesforce CRM. It is integrated into individual Salesforce Cloud apps, including Sales Cloud, Marketing Cloud, Service Cloud, and Commerce Cloud, as well as more generally through assistive tools like Einstein Copilot.

Salesforce Einstein is multitenant and includes several automated machine learning features, which helps it to better unify an organization’s data with development, AI, and general AI CRM capabilities. It is designed to make intelligent decisions based on your organization’s data and requires no additional installation or knowledge to get started, so long as you select a compatible subscription plan.

For more information about generative AI providers, read our in-depth guide: Generative AI Companies: Top 20 Leaders

7 Key Features of Salesforce Einstein

Einstein 1 Platform

The Einstein 1 Platform is the application development platform and unified data and AI solution that powers most artificial intelligence features currently operating in Salesforce. Einstein, Salesforce’s CRM apps, and the Data Cloud are all part of Salesforce’s metadata framework, which is able to tap into data and application resources in tools like Tableau, Slack, Heroku, Google Workspace, Microsoft 365, and Mulesoft. With the Einstein 1 Platform, users can build custom apps and generative AI models and optimize data security and privacy.

Trust Layer

The Trust Layer is a collection of tools, guardrails, best practices, and other commitments from Salesforce to protect privacy and security for data being used in Salesforce AI products. Some of the features of the trust layer include:

• Dynamic grounding
• Data masking
• Zero data retention
• Toxicity detection
• Secure data retrieval

Copilot & Conversational AI

Einstein Copilot is a CRM assistant that can help users across teams compose useful sales and marketing content, search for information about a recent deal or prospect, and much more. With the conversational AI components of Copilot, Salesforce users can also create intelligent external AI chatbots that can handle more complicated customer experience tasks and questions.

Generative AI

Based on the data you provide and the algorithmic framework of Einstein, users can use Copilot and other tools to generate new content with simple prompting. This content may be marketing or ad copy, email and campaign content, or even tailored analytics results that focus on a specific piece of client lifecycle research. Users can also incorporate their own models into Einstein for custom generative AI taskwork and initiatives.

Predictive AI & AI-Powered Analytics

In addition to creative, generative AI, Salesforce comes with advanced predictive AI features and AI-powered analytics that can guide users in strategic decision-making and planning.

For example, in the Sales AI suite of Sales Cloud, users can receive real-time selling data and information about the likelihood of a deal closing, as well as helpful hints for what this prospect may need to seal the deal. AI for Salesforce analytics is incorporated throughout Salesforce products and clouds, including Sales Cloud, Marketing Cloud, Tableau, and Lightning.

Prompt & Model Builders

These builder solutions from Salesforce AI help admins and other organizational leaders craft the prompting and modeling infrastructure that will work best for their teams. In Prompt Builder, reusable generative AI prompts are built, prompts are grounded and contextualized in your data and preferred AI models, and prompt templates can be designed for easier access and process standardization.

In Einstein 1 Studio, users can take advantage of a low-code AI builder to build their own copilots and custom actions. They can also build or smoothly introduce external, preexisting AI models into Salesforce through the Model Builder feature.

Coding Support

Though still in its early stages and only available to select groups of users, CodeGen is a new AI tool that makes programming and developer tasks operate conversationally and with low-code prompting. Users can develop new apps, improve existing apps, and do several other programming tasks in plain language. This is one of many emerging AI solutions that are working to democratize software and product development.

7 Applications of Salesforce Einstein: Use Cases

Marketing Cloud

Marketing AI is a collection of AI features designed for Marketing Cloud. Users can take advantage of Data Cloud, using natural language prompting and generative AI to more intelligently segment audience data. They can also optimize marketing campaigns, using predictive AI to test various possible campaign options simultaneously.

At the same time, marketers can use generative AI to generate relevant marketing copy and then use Einstein-powered AI analytics to analyze how all of these different components are performing. Other unique AI features in Marketing AI include AI scoring for prospects and leads, key account identification, event triggers and automated offers, and automated planning and pacing for marketing initiatives.

Sales Cloud

Sales AI, found in Sales Cloud, is one of the largest AI solution sets available to Salesforce users. Copilot is included to assist with tasks like email drafting, call summaries, personalized close plans, and prospect and account research. Additional features include AI-driven deal insights, predictive sales and deal forecasting, conversation mining and call insights, and relationship graphs and insights.

Soon, there will also be a revenue-cloud-specific solution to help with contract management. Dedicated Trailhead courses are available to help users get started with these solutions.

Service Cloud

Customer Service AI provides AI-powered assistance and tools throughout the Salesforce Service Cloud. A unique feature of this toolset is Einstein Copilot for Service, which is designed to assist with questions and other messaging from both external customers and internal users.

This tool can source information from your knowledge base, make intelligent responses based on internal data, and route more complicated tasks — including approval workflows — as necessary. AI eases typical holes and challenges in customer experience, giving service reps conversation summaries and quick replies, next-best actions and case classifications for quicker responses. It also offers several different resources to build bots that can handle customer service interactions in multiple languages and contexts.

Commerce Cloud

Commerce AI is a tailored suite of AI solutions that helps Commerce Cloud users improve their product lifecycles, sales, and general e-commerce workflows. Similar to AI solutions in Service Cloud, Commerce AI addresses challenges for both customers and employees.

Commerce AI features include an AI shopping assistant, AI-powered product recommendations, commerce insights, and generative AI support for product page and product description creation.

Data Cloud

Data Cloud is an AI-supported data storage solution in Einstein 1 that works with data in different formats and from different sources — users can even BYOL (bring your own data lake) if they already have a big data storage solution that lives outside of Salesforce, which greatly simplifies typical CRM implementation tasks like data migration and integration.

Through its built-in vector database, users are provided with the resources to initiate AI-powered searches, automate prospecting and audience building tasks, and complete other smart tasks across all of Salesforce’s different cloud products. Data Cloud enables particularly granular data insights, offering users access to a visual dashboard, data streams, data lake objects, and identity resolutions and insights.

Einstein Copilot

Einstein Copilot, now in beta, is an AI copilot that works as a standalone Salesforce feature and also within several different Salesforce product clouds. This copilot can assist with questions that users may have about current leads or opportunities as well as with data sourcing, custom deal closing plans, customer service queries, custom messaging for marketing, and intelligent product recommendations based on customer segment data. In general, this tool can help different members of your team with Q&A about data on the platform, custom action and next-step suggestions, and more.

Tableau Pulse

Tableau Pulse is a new AI feature set in Tableau, the Salesforce-owned data analytics platform, that helps users quickly source and digest analytical data about product, sales, marketing, and relationship management performance. With this data, users can more easily identify how certain metrics have changed over the past week while also looking at daily pulses to see how this data incrementally changes over time.

For users who are less experienced with analytics tools, plain-language prompting and results help to simplify the whole process; Tableau also offers Q&A enhancements and other support resources to help users ask the right questions about their data.

Pros and Cons of Salesforce Einstein

If you’re trying to decide if Salesforce Einstein offers all of the AI and automation capabilities that you’re looking for in a CRM, consider how the following pros and cons stack up:

How to Get Started with Salesforce Einstein

1. Subscribe to an AI-Enabled Salesforce Product or Plan

While most plans have some kind of basic AI running automations and other features in the background, the most advanced AI capabilities are typically available in higher-tier plans. The decision-makers in your organization should first determine the available budget and then wants versus needs; from there, you can select the subscription plan that balances these requirements with robust AI capabilities. It may also be wise to complete a demo with the Salesforce team so you can delineate your expectations and learn how to use the platform’s unique features more effectively.

2. Prepare and Optimize Your Data

While Salesforce is designed to work with the customer and lead data you put into the system without much fuss, there may be issues if your data contains inaccuracies, repetitions, missing information, or other issues. It’s a good idea to cleanse and QA your most important CRM data before you put it into Salesforce rather than after. This will also help you to get started with and scale AI operations more quickly.

3. Complete Relevant AI Trainings

Trailhead offers training and generative AI certification courses on a variety of Salesforce-specific and more general business technology topics. Interested users should sign up for their AI-specific course sets, which include information about how generative AI works and how AI should be used responsibly. Some other Trailhead courses specifically talk about how to use Salesforce AI features and tools most effectively.

Future Trends and Innovations in Salesforce AI

• New use cases for Einstein Copilot: Einstein was only generally released to the public in February 2024, but it already comes with many different use cases in the Marketing, Sales, Service, and Commerce Clouds. Salesforce has recently made announcements for more Copilot functionality in Tableau and platform analytics, as well as a greater emphasis on personal AI agents, so expect to see new Copilot use cases emerge in the coming months.
• Increased developer resources: Einstein has recently provided more users with access to CodeGen for low-code, democratized programming. The company has also announced upcoming AI and app integration opportunities for MuleSoft, which will help to make app development a smoother and speedier process. As more and more users develop an interest in creating custom AI solutions and applications, Salesforce is likely to bring more low-code and no-code resources into its portfolio to serve this population.
• More industry-specific features: Salesforce already offers some AI features that are specific to individual industries, but for the most part, its generative AI tools are divided based on more general clouds. Expect to see more purpose-built AI solutions for some of the company’s core industries that already have Salesforce bundles: automotive, communications, retail and consumer goods, energy and utilities, financial services, healthcare and life sciences, manufacturing, media, and the public sector.
• More support for compliance, privacy, security, and ethical AI: AI tool users, as well as governments and other regulatory bodies, continue to demand more transparency and ethical AI from tech companies. Salesforce already has its Trust Layer and several other enterprise security and privacy features built into its systems, but more features are on the horizon. For example, an audit trail to simplify compliance is coming soon to the Einstein Trust Layer.
• Greater emphasis on sustainability and net zero commitment: Many of the biggest companies around the globe, including Salesforce, have made net zero commitments to reduce emissions and pollution in the environment. Though few of these tools are actually in operation yet, several tech companies are using generative AI to support these sustainability efforts. Some of the programs and initiatives the organization is already working on include ESG disclosure authoring through Einstein, marginal abatement cost insights and visualizations, sustainability program visualizations, and improved emissions factors management.

Bottom Line: Salesforce AI Evolves with Generative AI Landscape

Even before the introduction of generative AI and advanced predictive AI, many aspects of Salesforce’s chatbots, analytics, and automations included some AI-powered characteristics. However, as the generative AI landscape continues to expand and mature, Salesforce is doing the work to keep up with these innovations, as is evidenced by its daily-to-weekly updates on new AI features in its products.

Whether you’re a brand new Salesforce user who’s looking for AI solutions that can help you get up and running or a Salesforce veteran who wants to simplify their current workflows in the platform, Salesforce continues to bolster its reputation for forward-thinking, AI-centric software that automates and streamlines AI customer relationship management and business operations.

For a full portrait of the AI vendors serving a wide array of business needs, read our in-depth guide: 150+ Top AI Companies 2024

The post Salesforce and AI: How Salesforce’s Einstein Transforms Sales appeared first on eWEEK.

In this guide, we’ll take a closer look at the five best AI voice generators available today, but first, here’s a glance at where each of these tools differentiates itself the most:

• Murf: Best for Multichannel Content Creation
• PlayHT: Best for AI Voice Agents
• LOVO: Best Combined AI Voice and Video Platform
• ElevenLabs: Best for Enterprise AI Scalability
• Speechify: Best for AI Narration

Top AI Voice Generator Software Comparison

In addition to text-to-speech and voice cloning capabilities, we’ll primarily compare these tools across these key criteria for generative AI voice generation software:

Murf: Best for Multichannel Content Creation

Murf is one of the top generative AI voice tools available to both casual and business users, providing them with an accessible user interface and a range of scalable voice generation and editing features. Its primary focus areas include text-to-speech content generation, no-code voice editing, AI-powered translation, AI voice deployment to apps via API, voice cloning, and an AI dubbing feature that is currently in beta for more than 20 languages.

Many business users select this tool for its wide range of collaborative features, its enterprise-level security and compliance expertise and features, its vocal quality and variety, and its comprehensive support for various enterprise use cases.

In addition to its easy-to-use enterprise integrations with various creative and product development tools, Murf also offers free creative guides and resources on the following topics: e-learning, explainer videos, YouTube videos, Spotify ads, corporate videos, advertisements, audiobooks, podcasts, video games, training videos, presentations, product demos, IVR voices, animation character voices, and documentaries.

Pros and Cons

Pricing

• Creator Lite: $23 per month billed annually, or $29 billed monthly for one editor to access up to five projects and 24 hours per year of voice generation.
• Creator Plus: $39 per month billed annually, or $49 billed monthly for one editor to access up to 30 projects and four hours per month of voice generation (up to 48 hours per year).
• Business Lite: $79 per month billed annually, or $99 billed monthly for up to three editors and five viewers to access up to 50 projects and eight hours per month of voice generation (up to 96 hours per year). Free trial access to this plan’s features is available for one editor, up to two projects, and up to 10 minutes of voice generation.
• Business Plus: $159 per month billed annually, or $199 billed monthly for up to three editors and five viewers to access up to 200 projects and 20 hours per month of voice generation (up to 240 hours per year). Free trial access to this plan’s features is available for one editor, up to two projects, and up to 10 minutes of voice generation.
• Enterprise: Pricing information available upon request. This plan is designed for more than five editors and unlimited viewers to create custom projects with unlimited voice generation access.
• Murf API: Pricing information available upon request.
• AI Translation: Add-on for Enterprise and Business plan users. Pricing information available upon request.

Features

• Integrations: Integrations are available for Canva, Google Slides, Adobe Audition, Adobe Captivate and Captivate Classic, and HTML Embed Code. Users can also download Murf Voices Installer to directly incorporate Murf voices into Windows apps.
• Vocal library: More than 200 voices, styles, and tonalities in more than 20 languages are available to users.
• Team collaboration and project organization: Folders, sub-folders, shareable links, and private folders and projects all support controlled collaboration.
• Enterprise compliance: Depending on the plan selected, users can benefit from GDPR, SOC2, and EU compliance support as well as SSO, access logs, custom contracts, and security reviews.
• Visual voice editing: Easy-to-use buttons and clickability to adjust pitch, emphasis, speed, interjections, pauses, pronunciation, and more.

To see a list of the leading generative AI apps, read our guide: Top 20 Generative AI Tools and Apps 2024

PlayHT: Best for AI Voice Agents

PlayHT has been a favorite artificial intelligence (AI) voice generation tool for a few years now, extending to users a highly accessible and scalable tool for multilingual AI voice generation. Compared to other AI voice generation tools, PlayHT first and foremost sets itself apart with its range of voice and language options: All plans, including the free plan, can access 907 voices and 142 different languages and accents. The tool also comes with limited instant voice clones and will soon offer high-fidelity clones to enterprise users.

Beyond its more conventional AI voice features and tools, PlayHT has set its sights on a very specific enterprise use case: AI voice agents. With its new feature set, Play Agents, users can create their own AI voice agent avatars with specific parameters and prompts about how they should greet and respond to user interactions. The tool also comes with several prebuilt agent templates, API-driven agent training and tracking for developers, and a simple table for tracking agent conversation history.

Pros and Cons

Pricing

Pricing for PlayHT depends on whether you select PlayHT Studio, AI voice agents, or the API subscription plans:

PlayHT Studio

• Free Plan: $0 for non-commercial access to all voices and languages, one instant voice clone, and up to 12,500 characters.
• Creator: $31.20 per month billed annually, or $39 billed monthly.
• Unlimited: Typically $99 per month, billed annually or monthly. A special discount is currently running for the annual plan for $29 per month.
• Enterprise: Custom pricing.

AI Voice Agents

• Free Plan: $0 for non-commercial access to 30 minutes of agent content creation.
• Pro: $20 billed monthly plus $0.05 per each minute used over 400 minutes.
• Business: $99 billed monthly plus $0.05 per each minute used over 2,000 minutes.
• Growth: $499 billed monthly plus $0.05 per each minute used over 10,000 minutes.
• Enterprise: Custom pricing for unlimited limits and other advanced features.

PlayHT API

• Free Plan: $0 for non-commercial access to all voices and languages, one instant voice clone, and up to 12,500 characters.
• Hacker: $5 billed monthly plus $0.25 per every additional 1,000 characters over 25,000 characters per month.
• Startup: $299 billed monthly plus $0.20 per every additional 1,000 characters over 1.5 million characters per month.
• Growth: $999 billed monthly plus $0.10 per every additional 1,000 characters over 10 million characters per month.
• Business: Custom pricing for large volume discounts and custom rate limits.

Features

• Multilingual voice library: PlayHT’s voice library includes 907 text-to-speech voices and 142 languages and accents.
• Pronunciation library: This feature allows users to define specific pronunciations and save these rules for future projects.
• Multi-voice content creation: A single audio file and project can include multiple voices, which is useful for AI conversational projects.
• Play Agents feature: Custom AI voice agents and preconfigured agent templates for healthcare, hotels, restaurants, front desks, and e-commerce can be used to create more intelligent customer service AI chatbots/agents.
• Real-time streaming API: Character-based pricing for API access, which scales up to include dedicated enterprise clusters and other advanced features.

For more information about generative AI providers, read our in-depth guide: Generative AI Companies: Top 20 Leaders

LOVO: Best Combined AI Voice and Video Platform

LOVO offers its users a suite of useful AI features that not only support AI voice generation and voiceover initiatives but also other creative tasks related to video and image creation. LOVO’s flagship platform, Genny, is a user-friendly tool that uses its own generative AI technologies to enable video editing, subtitle generation, voice generation, and voice cloning tasks. With the help of ChatGPT and Stable Diffusion models, users can also generate shortform and longform text and AI art projects at no additional cost and with no third-party tooling requirements.

Users most appreciate that this tool supports multiple languages and unique vocal tones, is easy to use, and offers high-quality voice outputs compared to many competitors. Many users also appreciate that they can purchase affordable, lifetime deals through AppSumo.

Pros and Cons

Pricing

Pricing for LOVO depends on whether you select an All in One or Subtitles subscription plan:

All in One

• Basic: $24 per month billed annually, or $29 per user billed monthly. Limited to one user per plan subscription.
• Pro: $48 per user per month, billed annually, with a 50% discount for the first year, or $48 per user billed monthly. A 14-day free trial is also available for this plan’s features.
• Pro +: $149 per user per month, billed annually, with a 50% discount for the first year, or $149 per user billed monthly.
• Enterprise: Pricing information available upon request.

Subtitles

• Free: $0 for limited features.
• Subtitles: $12 per user per month, billed annually, or $18 per user billed monthly.

Features

• Genny: All-in-one video creation platform with voice generation, voice cloning, subtitle generation, art generation, text generation, and video editing capabilities.
• Multilingual voice library: The text-to-speech library includes more than 500 voices and more than 100 languages. LOVO also caters voices to 30 different emotions.
• Built-in voice recorder: For voice cloning, users can record their voices directly within the LOVO tool. They also have the option to upload a prerecorded clip, if preferred.
• Simple Mode: For shorter voice generation and voiceover projects (between 2,000 and 5,000 characters), users can work with the lightweight, faster Simple Mode format.
• API access: LOVO voice application development features are available in all plans.

For an in-depth comparison of two leading AI art generators, see our guide: Midjourney vs. Dall-E: Best AI Image Generator 2024

ElevenLabs: Best for Enterprise AI Scalability

ElevenLabs is an artificial intelligence research firm that has developed comprehensive AI voice technologies for text to speech, speech to speech, dubbing, voice cloning, and multilingual content generation. Users frequently compliment ElevenLabs on the quality of the voice products it produces, noting that the vocal tone and overall quality feel more realistic than what most other competitors are producing.

ElevenLabs is one of the most business-friendly AI voice tools on the market today, offering advanced features at different price points. Its free plan is fairly comprehensive, including access to 29 languages and thousands of voices, automated dubbing, custom voices, and API. Six different pricing tiers are available, with the top tier offering unique enterprise draws like custom terms and SSO, unlimited concurrency, and volume-based discounts.

Additionally, ElevenLabs offers a grant program designed for the unique needs of business startups. Eligible startup applicants who can convince the vendor of their longterm strategy and growth potential will be given three months of free access with 11 million characters per month and enterprise features.

Pros and Cons

Pricing

• Free: $0 for 10,000 monthly characters, or approximately 10 minutes of audio per month.
• Starter: $50 per year, billed annually, with the first two months free, or $5 billed monthly with 80% off the first month.
• Creator: $220 per year, billed annually, with the first two months free, or $22 billed monthly with 50% off the first month.
• Pro: $990 per year, billed annually, with the first two months free, or $99 billed monthly.
• Scale: $3,300 per year, billed annually, with the first two months free, or $330 billed monthly.
• Custom Enterprise Plans: Pricing information available upon request.

Features

• Precision voice tuning: With this drag-and-drop editing feature, users can adjust vocal stability and variability, vocal clarity, and style exaggerations on a scale.
• Multilingual voice library: More than 1,000 voices across 29 different languages are available for text-to-speech content generation.
• Speech to speech: Users can upload an audio file or record their voice for voice changing, custom voices, and voice cloning capabilities.
• Dubbing Studio: Video translation and dubbing available in 29 different languages. Speaker. Studio interface allows users to granularly adjust specs.
• AI Speech Classifier: This unique feature allows users to upload an audio file so the vendor can evaluate if the clip was created by ElevenLabs AI.

Speechify: Best for AI Narration

Speechify is an AI voice solution that specializes in text-to-speech technology for mobile platforms and more casual use cases, like audiobook narration. With the Speechify AI platform, users can select from a wide variety of AI voices, including voices that mimic celebrities like Gwyneth Paltrow and Snoop Dogg. All of this is available in various mobile and online locations, including through browser extensions that are accessible and favorably reviewed by users.

While Speechify’s core audience is recreational users, students, and other more casual users who want a convenient solution for reading off text in various formats, the platform offers some key enterprise AI usability features through its Voice Over Studio for Business. With this suite of Speechify solutions, business users can benefit from unlimited video and voice downloads, commercial rights, collaborative project management features, dozens of voices, and enterprise security and compliance features.

Pros and Cons

Pricing

Pricing for Speechify all depends on how you want to use the tool. Here are some of the options you have as a Speechify user:

• Speechify Limited (text to speech): $0 for 10 standard reading voices and limited text-to-speech features.
• Speechify Premium: $139 per year for advanced text-to-speech features and capabilities.
• Speechify Studio Free: $0 for access to basic AI voice and video features with no downloads.
• Speechify Studio Basic: $24 per user per month, billed annually, or $69 per user billed monthly.
• Speechify Studio Professional: $32.08 per user per month, billed annually, or $99 per user billed monthly.
• Speechify Studio Enterprise: Pricing information available upon request.
• Text to Speech API: Users can join the waitlist.
• Speechify Audiobooks: $9.99 per month, or $120 billed annually.

Custom pricing and discounts may also be available for business teams and educational organizations.

Features

• Browser extensions and app: Users can access Speechify through the Chrome extension, Edge Add-on, Android, iOS, and PDF readers like Adobe Acrobat.
• Multilingual voice library: More than 100 voices in over 40 languages are available for enterprise users.
• AI dubbing: Dubbing is available in multiple languages, with the ability to adjust voice, tone, and speed.
• AI video generator: Users can combine Speechify’s AI voiceovers with avatars to create AI videos.
• Various upload and download formats: Content can be uploaded in .txt, .docx, .srt, and YouTube URL formats; Speechify projects can be downloaded as video, audio, or text.

Key Features of AI Voice Generator Software

AI voice generator software typically includes features that help users transform text, existing audio, and other media into voices with adjustable qualities to meet their needs. Additionally, many of these generative AI tools come with features to make enterprise-level collaboration and content creation run more smoothly. In general, expect to find the following features in AI voice generators:

Text to Speech

Text to speech (TTS) is a type of AI technology that changes written text into spoken audio. Most AI voice generator software allows users to upload text of different lengths and in different languages in order to generate a vocal version of the same content.

Voice Cloning

With voice cloning, AI technology can capture the content, tonality, speed, and other characteristics of a person’s voice in a recording and use that information to create a faithful replica or clone of that unique voice. With this capability, users can generate entirely new content and recordings that sound like they were spoken by that person.

Custom Voices or Voice Changing

On some AI voice platforms, if you submit your own voice clip or directly record your voice into the app, you can then change that voice into a completely different character, adjusting the tone, accent, mood, and other features. Many users want this feature for creative projects like video game development.

Multilingual Voice Library

Most generative AI voice tools give users access to a diverse, multilingual library of predeveloped voice models. Through extensive training, these TTS models are prepared to create voice transcripts and recordings that accurately adhere to each language’s specific pronunciations, tonalities, pauses, and other characteristics of that language’s speech patterns.

Dubbing and Translation

Taking TTS a step further, dubbing and translation with AI make the effort to translate an existing text or voice recording into a different spoken language. For dubbing specifically, existing recordings — often movies, commercials, and other visual media — receive a new vocal overlay, typically dubbed in a different language by an AI model.

APIs and Third-Party Integrations

With the help of APIs and built-in third-party integrations, users can more easily add AI voice creation and editing capabilities directly into their app and product development workflows. A growing number of AI voice tools are adding relevant third-party integrations to creative platforms as well as social and distribution channels.

To learn about today’s top generative AI tools for the video market, see our guide: 5 Best AI Video Generators

How We Evaluated AI Voice Generators

To evaluate these AI voice generators and other leaders in this AI market sector, we looked at each tool’s standard and unique features while focusing on the following criteria. Each criterion is weighted based on its importance to the typical business user:

Vocal Quality – 30%

Needless to say, vocal quality, fidelity, and usability are the most important aspects of an AI voice generator. Within this criterion, we evaluated each tool based on the realistic quality of AI voices, the accuracy of AI voice generations, the availability of different voices and languages, and the ability to granularly edit generated voice products. We also considered whether a tool offered users the ability to customize or record their own voices and voiceovers.

Enterprise Scalability – 30%

Enterprise scalability is hugely important for AI voice generators since many companies invest in this type of platform to create global marketing, sales, and product content at scale.

For enterprise scalability, we assessed each tool’s global library of voices and dialects, its adherence to enterprise security and compliance standards, features that go beyond voice content production, collaboration and sharing capabilities, integrations with relevant third-party tools and platforms, and the scalability of APIs. We placed a special emphasis on each tool’s enterprise-level plans and the additional features that are available at this level.

Pricing – 20%

Pricing is a crucial factor when considering AI voice technology, as the cost of these tools varies widely for the features you get at that price point. As part of this evaluation, we identified whether each tool offered a free plan option, we compared how prices scale from package to package, we considered how many price points were available to users, and we looked at the value of the features added to each tier, particularly enterprise-level tiers.

Ease of Use – 20%

AI voice tools are supposed to make content creation a simpler task; for this reason, ease of use and accessibility were also important factors in how we judged each of these tools. We looked at each tool’s no-code features, the user-friendliness of voice editing tools, the quality of customer support at each subscription tier, and the availability of self-service resources and community forums for getting started and troubleshooting.

AI Voice Generators: Frequently Asked Questions (FAQs)

Learn more about AI voice generator technology and the top solutions available through these frequently asked questions:

What is the best AI voice generator?

The best AI voice generator will depend on your particular needs and project plans, but Murf is consistently a top choice for its flexibility, with a wide range of general use cases.

Is there a free AI voice generator?

Yes, several AI voice generators are free or are available in free, limited versions.

What is the best free AI voice generator?

The best free AI voice generator options will vary based on your exact requirements. ElevenLabs is the best free solution for users who require API access and interoperability with other resources, while Speechify is the most generous for users who don’t require downloads or more complex features.

Bottom Line: AI Voice Generators Are Affordable and Customizable

AI voice technology has grown in popularity for content creators of all backgrounds and budgets. These type of generative AI tools enable creative scalability for videos, podcasts, audiobooks, customer service interactions, and a slew of other enterprise use cases that require consistent and original voice content. What’s more, this technology is frequently customizable and available in affordable plans, meaning users of all stripes can try out these tools to figure out their potential for their projects.

If you’re not sure which of the AI voice tools in this guide is the best fit for your organization, take some time to test out the free plans or trials that are available for each tool. You’ll quickly discover if the software meets your particular needs, if it’s user friendly, and if it has the features necessary to keep up with your organization’s security and compliance requirements.

For a full portrait of the AI vendors serving a wide array of business needs, read our in-depth guide: 150+ Top AI Companies 2024

The post 5 Best AI Voice Generators: AI Text-To-Speech in 2024 appeared first on eWEEK.

However, there are some distinct differences between ChatGPT-4 (powered by GPT-4) and 3.5 (powered by GPT-3.5). Yet depending on your budget and particular requirements, either generative AI solution could be considered the best.

We’ll take a closer look at the differences between each of these models, but first, here’s a quick summary of their unique strengths and differentiators:

• GPT-4: Best for higher-powered, quicker, scalable projects that require greater accuracy and multimodality. Compared to GPT-3.5 and the free ChatGPT version that accompanies it, GPT-4 is a better solution for users who want more diverse content outputs and inputs, require more accurate and nuanced outcomes, and need a heavier emphasis on enterprise safety and privacy.
• GPT-3.5: Best for an affordable and accessible version of ChatGPT and the GPT model for more basic use cases. Compared to GPT-4 and paid versions of ChatGPT, GPT-3.5 is a free tool that is easy for non-developers and less technical users to access and manipulate according to their needs.

ChatGPT-4 vs. ChatGPT-3.5 at a Glance

Note that the winner in each category earns a green check:

ChatGPT-4 Overview

ChatGPT-4, also known as ChatGPT 4 and GPT-4, is the latest AI chatbot and generative AI model from OpenAI, a leading generative AI company that focuses on scalable content generation for wide-ranging personal and business use cases.

ChatGPT-4 capabilities are available in all paid ChatGPT plans, and users can also fine-tune and set up various OpenAI APIs with the same capabilities. Compared to ChatGPT-3.5 and other earlier iterations of the tool, ChatGPT-4 has greater content accuracy and creative capabilities, a larger context window (32K or 128K, depending on user choice), multimodal capabilities, and several plugins and collaborative features to make the solution more useful for generative AI enterprise use cases.

To see a list of the leading generative AI apps, read our guide: Top 20 Generative AI Tools and Apps 2024

Key Features

• Multimodal image generation: GPT-4 allows users to submit visual inputs and can generate relevant visual outputs, especially with ChatGPT paid plans that include a DALL-E tool integration for text-to-image content generation.
• Larger context windows: Depending on which ChatGPT-4 plan you select, users can benefit from 32K or 128K context windows, as compared to GPT-3.5’s 8K context window.
• Internet connectivity: Unlike previous OpenAI GPT models and ChatGPT versions, ChatGPT-4 users (Plus plan and higher) can access the internet for limited browsing capabilities, primarily through plugins.

Pros

• Stronger commitment to safety and alignment in training and research.
• Multimodal, scalable, and more creative outputs.

Cons

• More expensive than many similar models, especially with API and direct model access.
• APIs and fine-tuning may have a steep learning curve.

For more information about generative AI providers, read our in-depth guide: Generative AI Companies: Top 20 Leaders

ChatGPT-3.5 Overview

ChatGPT-3.5, also sometimes called ChatGPT 3.5 and GPT-3.5, is an earlier generation of ChatGPT that has since been superseded by ChatGPT, a more advanced and powerful model.

However, ChatGPT-3.5 is still a very capable and popular tool, especially since it is the only chat model that Free plan users can access. With ChatGPT-3.5, users can benefit from mobile and web access, several unlimited AI conversational features, an 8K context window, multifactor authentication, and regular updates as OpenAI advances its generative AI technology.

Key Features

• Unlimited user interactions: Though the quality, resolution, and other advanced features may be limited, all ChatGPT-3.5 users can send unlimited messages and receive unlimited interactions; they can also keep track of unlimited conversation history.
• Mobile and web access: Users can access ChatGPT-3.5 directly from the OpenAI website or through dedicated iOS and Android applications.
• Mobile voice capabilities: With the mobile app version of ChatGPT-3.5, users can take advantage of their built-in microphone and AI voices to have a true conversation that requires no typing or reading.

Pros

• Free to use in ChatGPT Free plan.
• Easy access across online and mobile app versions.

Cons

• Less power and accuracy than the latest model.
• No multimodal capabilities, including for images.

To understand how ChatGPT compares to another major generative AI tool, read our guide: ChatGPT vs Watson Assistant  

Best for Pricing: ChatGPT-3.5

Simply put, ChatGPT-3.5 is completely free to use and access, while ChatGPT-4 is only available in paid plans and APIs. Therefore, GPT-3.5 is the best option for affordable pricing.

GPT-3.5 is the model that runs behind the ChatGPT Free plan for individuals, which offers users access to unlimited messages, chat history, interactions, and mobile and web access at no cost.

While the Free plan has several limitations compared to plans running on GPT-4, users can still benefit from fairly quick response times, an 8K context window, multifactor authentication, regular model quality and speed improvements, and the ability to opt out of personal content being used as part of model training sets.

Outside of the GPT-3.5 access that is available directly through ChatGPT, users can also pay for the following Turbo, fine-tuning, and older 3.5 models:

• gpt-3.5-turbo-0125: $0.50 per 1 million input tokens and $1.50 per 1 million output tokens.
• gpt-3.5-turbo-instruct: $1.50 per 1 million input tokens and $2 per 1 million output tokens.
• gpt-3.5-turbo (fine-tuning model): $8 per 1 million training tokens, $3 per 1 million input tokens, and $6 per 1 million output tokens.
• gpt-3.5-turbo-1106: $1 per 1 million input tokens and $2 per 1 million output tokens.
• gpt-3.5-turbo-0613: $1.50 per 1 million input tokens and $2 per 1 million output tokens.
• gpt-3.5-turbo-16k-0613: $3 per 1 million input tokens and $4 per 1 million output tokens.
• gpt-3.5-turbo-0301: $1.50 per 1 million tokens and $2 per 1 million output tokens.

In contrast, GPT-4 is not available in any free or lower-cost plan options. The access option that is typically most affordable is the ChatGPT Plus plan, which costs $20 per user and is billed monthly. GPT-4 is also available in ChatGPT business plans, including Team — which costs $25 per user per month, billed annually, or $30 per user billed monthly — and Enterprise, which only has pricing information available upon request.

As far as fine-tuning, APIs, and other direct model access options go for GPT-4, here is what pricing looks like:

• GPT-4 Turbo preview plans: $10 per 1 million input tokens and $30 per 1 million output tokens.
• gpt-4: $30 per 1 million input tokens and $60 per 1 million output tokens.
• gpt-4-32k: $60 per 1 million input tokens and $120 per 1 million output tokens.

ChatGPT-3.5 is the clear winner for affordability, offering both free ChatGPT capabilities and model access that is much cheaper than GPT-4 model access, especially as you scale past the first few million tokens.

However, it’s important to note that ChatGPT-3.5 has fewer advanced features to justify this lower price tag. Depending on your needs, ChatGPT-4 may offer greater value despite the higher cost.

To gain a deeper understanding of the AI app sector, read our guide to Best AI Apps for Mobile

Best for Core Features: ChatGPT-4

ChatGPT-4 is a more advanced tool than ChatGPT-3.5, offering a wider range of features as well as greater accuracy, creativity, nuanced understanding, and safety training.

GPT-3.5 and GPT-4 both use a transformer-based architecture as part of a neural network that handles sequential data. ChatGPT-3.5 is less advanced, has a smaller number of potential parameters included, and its knowledge cutoff appears to be more dated: it was sometime in 2021 or 2022, as compared to GPT-4’s April 2023 knowledge cutoff.

It’s also important to know that ChatGPT plans powered by GPT-4 can access the internet for browsing tasks, handle advanced data analysis, and operate a significantly larger context window. With these features and more, GPT-4 and paid ChatGPT plans are better at handling complex problems and multimodal challenges compared to GPT-3.5.

GPT-3.5 is still a robust model that powers a free version of ChatGPT. This free, more limited version of ChatGPT is quite capable of various user-requested tasks, including language translation, simple coding and troubleshooting, creative problem-solving and storytelling, and straightforward Q&As.

But GPT-4 is “smarter,” can understand and generate AI images, and can process between four and 16 times as many words as its predecessor with greater accuracy. As a newer model with more R&D to back it up, OpenAI is also committed to stronger safety, privacy, and ethical AI measures in its ChatGPT-4 model.

In nearly all ways, ChatGPT-4 offers more and higher-quality core features than ChatGPT-3.5.

Best for Ease of Implementation: ChatGPT-3.5

ChatGPT-3.5 barely edges out ChatGPT-4 for ease of use and implementation, due to the fact that it is free and requires no payment plans or installations to get started.

With ChatGPT-3.5, all users need to do is log into or create a free OpenAI account. Users have the option to log in with their email address and their preferred password, or they can log in through existing Google, Microsoft, or Apple accounts.

Once an account is created and opened, users can immediately begin having conversations with ChatGPT and save their conversation history for later access. The login/signup process is similar for the mobile app, which can easily be downloaded from your mobile device’s app store, just as you would with any other mobile app.

Making an account for ChatGPT-4 is also fairly simple, but it comes with the added layer of setting up subscriptions and payment plans. While this shouldn’t be all that difficult, especially for business users who manage other cloud and app subscriptions, it does add a layer of complexity, especially for accounts with multiple users and/or user counts that are frequently changing.

In summary, both of these generative AI apps are fairly easy to implement and use, but ChatGPT-3.5 is slightly easier. Remember, this comparison focuses primarily on the ChatGPT interfaces for each of these tools rather than APIs and fine-tuning model versions. If you choose to install or subscribe to either of these models outside of the ChatGPT subscription framework, bear in mind that both pricing and ease of use will grow more complex, as models are more customizable and pricing is usage-based.

Best for Content Quality: ChatGPT-4

ChatGPT-4 outperforms ChatGPT-3.5 in all content quality measures, because it is a more recent and advanced iteration of ChatGPT technology, supported by more robust research and development.

Compared to previous GPT generations from OpenAI, GPT-4 has been trained on more and wider-ranging datasets, has more computation power and parameters, and has a greater context window for user inputs. These features enable ChatGPT-4 to generate more nuanced, creative, accurate, and relevant responses to even the most complex and layered user queries.

Additionally, according to OpenAI’s own research and admission, GPT-4 is 82% less likely to respond to dangerous or prohibited requests and 40% more likely to produce accurate, fact-based responses compared to GPT-3.5.

For safety and alignment, GPT-4 takes GPT-3.5’s great strides a step further, incorporating more user feedback into GPT-4 behavioral training as well as stronger training, evaluation, and monitoring classifiers.

As far as plagiarism and QA-specific features are concerned, ChatGPT-3.5 includes AI Text Classifier, which is a plagiarism checker. This feature is good at indicating potential cases of plagiarism, though it’s important to fact-check these suggestions as well: OpenAI recommends that once ChatGPT spots possible plagiarism issues and candidates, humans should look at the data and determine the truth.

Meanwhile, ChatGPT-4 spots plagiarism examples with more certainty, though it is far from 100%. It also does a better job at distinguishing between AI-written and human-written text, as well as in the detection of automated misinformation campaigns that take advantage of AI tools.

Users are warned, however, that the general limitations of both versions of ChatGPT include a higher likelihood of inaccuracy with texts below 1,000 characters; plagiarism-checking results also work better with English than other languages.

In general, ChatGPT-4 is a better tool for content quality measurements, especially as this tool has been designed to handle more advanced reasoning tasks with ease.

Best for Enterprise Use Cases: ChatGPT-4

ChatGPT-4 is a better solution for most enterprise use cases when compared to ChatGPT-3.5, especially since multi-user collaboration and security features are available. 

ChatGPT-3.5 can work for many solo business use cases, whether it’s drafting digital marketing content, brainstorming a product idea or launch schedule, or outlining a better HR onboarding or interviewing process.

Additionally, the free version of ChatGPT can handle some basic coding and QA programming tasks, so long as users are willing and able to supplement ChatGPT’s outputs with their own research and knowledge. If your business has simple text-based content generation tasks that require little to no collaboration and no advanced security features, then ChatGPT-3.5 may have everything that you need.

However, most enterprise-level content generation tasks will require ChatGPT-4’s more advanced capabilities. The longer context window, faster response and processing times, and multimodality of this product version open up the generative AI tool to scalable, more diverse, and more complex content generation tasks. Advanced data analysis is also included in each GPT-4-based ChatGPT plan, so users can benefit from a tool that handles their data more expertly.

The Team and Enterprise versions of ChatGPT are the most powerful ChatGPT subscriptions that run on GPT-4, each including collaboration features, larger context windows, and more security and admin capabilities. While ChatGPT-3.5 only includes MFA among its security features, subscribers to these GPT-4 plans can access advanced features like a dedicated workspace, unified billing, GPTs analytics and management, admin consoles and roles, bulk member management, and compliance management features.

A growing number of businesses are developing their own fine-tuned versions of GPT-4 to meet very specific business use cases and goals. These include Duolingo, Stripe, and Morgan Stanley, which are using GPT-4 to deepen conversation quality, combat fraud, improve user experience, and organize complex knowledge base data, respectively.

Many other businesses continue to use GPT-3.5 to fine-tune models for custom enterprise use cases, but as competition heats up and GPT-4 continues to prove its more robust capabilities, expect many of these enterprise users to upgrade to GPT-4 in the future.

Who Shouldn’t Use ChatGPT-4 or ChatGPT-3.5?

While both ChatGPT-4 and ChatGPT-3.5 support a wide range of personal and enterprise use cases, there are several instances when a different model or tool would be a better fit. The following users should seek out alternatives to GPT-4 and GPT-3.5:

Who Shouldn’t Use ChatGPT-4

• Users who require a free content generation AI tool.
• Users who need a high-powered developer and coding tool; GPT-4 is best for coding assistance.
• Users who are unwilling or unable to quality-check the model’s outputs.
• Users who need basic AI content generation capabilities for text-only outputs.
• Users who require deep internet search integration and functionality (though GPT-4 does have basic Internet capabilities in beta now).

Who Shouldn’t Use ChatGPT-3.5

• Users who want a multimodal generative AI tool, particularly for AI image generation.
• Users who need a high-powered developer and coding tool.
• Users who are unwilling or unable to quality-check the model’s outputs.
• Users who want larger context windows, higher resolution, and other advanced generative AI model features.
• Users who need real-time Internet access and up-to-date training data for their queries; while ChatGPT-4’s knowledge cutoff is April 2023, most users speculate that ChatGPT-3.5’s cutoff is either 2021 or 2022.

3 Best Alternatives to GPT-4 and GPT-3.5

If ChatGPT-4 and ChatGPT-3.5 don’t offer the features and capabilities that your organization needs, these are three of the best alternatives available today:

Gemini

Gemini, previously known as Bard, is one of the top ChatGPT competitors on the market today. Similar to ChatGPT, users can input multimodal prompts and receive relevant outputs on both web and mobile interfaces. While many users still prefer ChatGPT, believing it is more accurate, stable, and scalable, a growing number of users are shifting to Gemini, especially due to its greater multimodal integrations (including for free plan users) and its deep integration of Google Search and other web-based extensions.

For a deeper understanding of how Gemini compares with ChatGPT, read our guide: Gemini vs ChatGPT: AI Apps Head-to-Head

Claude

Claude is a powerful generative AI model and AI chatbot solution from Anthropic AI. It is a favorite of proponents of ethical AI, as Anthropic has committed to ethical development, transparency, high levels of built-in security and compliance, and inoffensive outputs while developing the Claude model. Users can access this model via claude.ai, through paid plans, and through API access. Significantly, Claude also has one of the largest context windows on the market today: 200K.

For an in-depth analysis of how ChatGPT compares with Claude, see our guide: ChatGPT-4 vs Claude: Chatbot Comparison

Microsoft Copilot

Microsoft Copilot is a flexible generative AI tool and chatbot that users can access through multiple interfaces. The Bing-powered Copilot labels itself an “everyday AI companion” and works similarly to ChatGPT, though with more multimodality in its free plan version. Copilot assistance and content generation capabilities are also available through multiple Microsoft products, including most Microsoft 365 business applications and GitHub.

Review Methodology: ChatGPT-4 vs. ChatGPT-3.5

To assess and compare ChatGPT-4 vs. ChatGPT-3.5, we reviewed each tool with the following criteria in mind. The percentages listed next to each criterion indicate how heavily we — and typical generative AI users — weigh that quality of a generative AI model against all others.

Content Quality – 40%

We most heavily prioritized content output quality when reviewing these two tools, as scalable content production is only as good as the quality of content produced. Our content quality review focused most specifically on output relevance to prompts, human-like tone and quality of outputs, output accuracy, and range of content output types.

We also paid close attention to any information OpenAI disclosed about safety and user privacy training each tool received, which could greatly impact content accuracy and the frequency of problems like AI hallucinations.

Scalability – 30%

Scalability was our next-highest criterion weight because of how important it is for enterprise users who plan to integrate generative AI into routine operations. Within our review of each tool’s scalability, we focused primarily on context windows, response times, new features available at each pricing tier, advanced data analysis capabilities, team collaboration features, and security and administrative capabilities.

Accessibility – 20%

Accessibility and ease of use are key to the success of a generative AI model, especially for less technical users. In our accessibility scoring process, we emphasized multiple user interfaces and channel options, mobility, ease of implementation and ongoing use, user-friendly research and training guides, and chat history accessibility.

Affordability – 10%

Though affordability is unlikely to be the most important criterion for enterprise users, many small businesses and solopreneurs must prioritize generative AI tools that meet their needs without breaking the bank. For this criteria, we considered whether a free version was available, how quickly costs went up from plan to plan, how much APIs and fine-tuning models cost, and how many features users can access at each pricing tier.

Bottom Line: ChatGPT-4 vs. ChatGPT-3.5

Both GPT-4 and ChatGPT have earned formidable reputations as excellent generative AI tools. There are obvious similarities between them, as ChatGPT-4 is essentially an upgrade to ChatGPT-3.5.

GPT-4 is more advanced and beats GPT-3.5 in nearly all criteria, at least as far as performance is concerned. Regardless, though, each tool has its place, and ChatGPT-3.5 can be a powerful tool for many individual and business use cases — especially for users on a budget. When comparing ChatGPT-4 vs. ChatGPT-3.5, the most important initial step you can take is to determine your available budget and nonnegotiable feature requirements. Once these requirements have been defined by you or your organization, it should be a fairly straightforward decision between the two.

For a full portrait of the AI vendors serving a wide array of business needs, read our in-depth guide: 150+ Top AI Companies 2024

The post ChatGPT-4 vs. ChatGPT-3.5: AI App Comparison appeared first on eWEEK.