The Project Management Institute’s (PMI) Pulse of the Profession report reveals that companies using AI-driven tools deliver 61% of their projects on time, compared to 47% for those that do not use AI. Their research also shows that 69% of projects in AI-adopting companies realize 95% or more of their business benefits, in contrast with 53% in non-AI-using companies. These statistics highlight the potential of AI in improving project outcomes.

This guide explores the diverse use cases, benefits, and considerations of integrating AI into project management.

How Can AI Be Used in Project Management?

Artificial intelligence is deployed in project management to significant benefit in data analysis, risk management, and communication and collaboration.

Data Analysis and Predictive Analytics

AI can analyze vast amounts of historical project data to identify patterns and trends, helping project managers understand factors that contributed to past successes or failures. AI PM tools can forecast project outcomes such as completion dates and budget requirements. They can also predict future resource needs, ensuring optimal allocation.

Predictive analytics tools help project managers proactively address issues before they escalate.

Risk Management

You can use AI to enhance risk management by identifying potential pitfalls through data analysis, assessing their impact and likelihood, and prioritizing them effectively. AI can monitor project progress and external conditions to detect emerging risks in real-time, allowing for proactive management and quicker response to issues.

Communication and Collaboration

AI chatbots facilitate instant communication among team members, answering queries and providing updates around the clock. They transcribe and summarize meeting notes, highlight key action items, notate tasks from meeting notes, and integrate with collaboration tools like Slack and Microsoft Teams to offer intelligent recommendations.

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

5 Benefits of AI in Project Management

The benefits of AI in project management range from automated task management to help with project planning – and they’re increasing on a constant basis.

Automated Task Management: AI can streamline and automate routine tasks, such as scheduling meetings, updating progress reports, and assigning tasks to team members. This frees project managers to focus on more strategic aspects of project planning and execution.

Efficient Resource Allocation: To ensure that the right people are assigned to the right tasks at the right time, maximizing productivity and minimizing resource wastage, AI algorithms can analyze historical project data, current workload, and team member skill sets to optimize resource allocation.

Assisted Project Planning: AI-powered project management tools can assist in creating comprehensive project plans by analyzing project requirements, constraints, and dependencies. These tools can generate realistic timelines and identify potential bottlenecks.

Improved Accuracy: While 100% accuracy is not guaranteed in AI, it can significantly enhance accuracy compared to traditional methods by minimizing human errors. Even though perfection may not be achievable, incremental improvements in accuracy can still lead to substantial benefits in project management, such as better decision-making, reduced risks, and improved project outcomes.

Boosted Team Collaboration with AI: Enhanced collaboration leads to a more cohesive team environment, allowing members to work together seamlessly, thereby improving project performance and team morale. AI-powered collaboration tools can facilitate better communication and coordination among team members.

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

4 Key Considerations Before Integrating AI Into Your System

Integrating AI is not always the optimal strategy. Before you use this powerful technology, make sure you have considered important points like data quality, security, and costs. Specifically, include the following considerations into your planning:

• Data quality and integration: The effectiveness of AI depends on the quality and completeness of the data it analyzes. Organizations must ensure their data is accurate, up-to-date, and well-integrated across systems.
• Change management: Introducing AI requires a cultural shift within the organization. Project managers and team members must be trained to trust and properly use AI tools. Clear communication about the benefits and capabilities of AI can help mitigate resistance to change.
• Security and privacy: Consider the security and privacy implications of integrating AI, especially if sensitive data is involved. Ensure the AI solution complies with relevant regulations and standards to protect data privacy and confidentiality.
• Cost of implementation: While AI can lead to cost savings in the long run, the initial investment in AI tools and technology can be significant. Organizations must evaluate the return on investment (ROI) to justify the upfront costs.

How to Integrate AI Into Your Project Management System

When integrating AI into your project management system, you can choose any PM software that suits your needs. I will use Asana for this tutorial, but the process can be adapted to other PM platforms.

Some project management tools offer built-in AI capabilities, either free or at an additional cost. For instance, Asana Intelligence is available to all paid plans. However, these AI features can sometimes be limited. Integrating a third-party app might be necessary if you need specific AI functionalities.

For this guide, I’ll integrate Fireflies AI with Asana. Fireflies AI is an app that transcribes meetings and generates actionable insights. This integration allows you to automatically create tasks on Asana from AI-assisted meeting action items or using voice commands.

Steps to Integrate Fireflies AI with Asana

1. Create a Fireflies account. Visit the Fireflies AI website and sign up for an account.
2. Log in to your Fireflies AI account. Use your credentials to access your Fireflies AI dashboard.

3. Navigate to the integration section. On the side menu bar, click on “Integration”.

4. Select Asana project management. From the list of available integrations, select Asana.

5. Click on connect. Initiate the connection process by clicking on the “Connect” button.

6. Grant permission. On the authentication page, click “Allow” to grant Fireflies access to your Asana account.

7. Configure integration settings.

• • Fill in the fields on the right-hand side with your project data, such as workspace, specific project, and list.
  • Toggle on/off the “Create AI Assisted Tasks from Action Items” and “Create Task Using Topic Tracker” options.
  • Add topics if you plan to use voice commands.

8. Once you have filled out all necessary information, click “Save Settings” to finalize the integration.

The process may vary slightly depending on the AI solution and the project management tool you are integrating, but the general steps are usually similar. Integrating AI can significantly enhance your project management capabilities, streamlining workflows.

3 Leading Project Management Tools with AI Capabilities

Notion

Notion is an all-in-one workspace that combines notes, tasks, databases, and more into a single tool. It is known for its flexibility and customization options, making it popular among individuals and teams. Notion AI costs extra, at $8 per member per month billed annually or $10 per member per month billed monthly.

AI Capabilities

• Smart search: Notion uses AI to enhance search functionality, making finding relevant documents and tasks easier.
• Automated workflow suggestions: The tool can suggest templates and workflows based on your usage patterns.
• Content generation: The software can help you brainstorm new ideas and create first drafts.

Use Cases

• Ideal for teams looking to streamline documentation and task management in one place.
• Great for creating dynamic and interactive documents that can evolve with project needs.

Wrike

Wrike is a project management tool designed for collaboration and project tracking. It offers various features tailored to multiple industries and team sizes.

AI Capabilities

• AI subitem creation: Wrike’s AI can identify key tasks in plain text, such as project plans or meeting notes, create a parent task, and automatically create subtasks.
• Predictive analysis: AI-driven insights help predict project risks and outcomes, allowing teams to make proactive decisions.

Use Cases

• Suitable for large teams and enterprises that require comprehensive project tracking and collaboration features.
• It’s beneficial for teams needing advanced reporting and analytics to monitor project health and progress.

 monday.com

Monday.com is a versatile collaborative operating system that allows teams to build custom workflows. It is known for its user-friendly interface and flexibility in managing different types of projects.

AI Capabilities

• Automation recipes: Monday.com offers AI-driven automation recipes to streamline repetitive tasks, such as notifications and task assignments.
• Data insights: AI analyzes project data to provide insights and trends, helping teams optimize workflows.

Use Cases

• Perfect for teams that need customizable workflows and automation to reduce manual workload.
• Great for organizations that value intuitive and visually appealing interfaces for managing projects.

Bottom Line: AI Will Drive Project Management in the Future

The future of project management will be defined by intelligent systems that continuously learn and adapt, ensuring that projects are completed on time, within budget, and to the highest quality standards.

AI will drive project management towards greater efficiency, better decision-making, and more effective collaboration. By automating routine tasks, providing predictive insights, personalizing project plans, and enhancing communication, AI will enable project managers to focus on strategic initiatives and deliver projects more successfully.

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 Project Management: Use Cases, Benefits and Considerations appeared first on eWEEK.

Redshift and Databricks provide the volume, speed, and quality demanded by business intelligence (BI) applications. But there are as many similarities as there are differences between these two data leaders. Therefore, selection often boils down to platform preference and suitability for your organization’s data strategy:

• Databricks: Best for real-time data processing and machine learning capabilities.
• AWS Redshift: Best for large-scale data warehousing and easy integration with other AWS services.

Databricks vs. Redshift: Comparison Chart

Databricks Overview

Databricks is a unified analytics platform that provides a collaborative environment for data engineers, data scientists, and business analysts to work together on big data and machine learning projects. It is built on top of Apache Spark, an open-source data processing engine, and offers several tools and services to simplify and accelerate the development of data-driven applications.

Databricks is well-suited to streaming, machine learning, artificial intelligence, and data science workloads — courtesy of its Spark engine, which enables use of multiple languages. It isn’t a data warehouse: Its data platform is wider in scope with better capabilities than Redshift for ELT, data science, and machine learning. Users store data in managed object storage of their choice and don’t get involved in its pricing. The platform focuses on data lake features and data processing. It is squarely aimed at data scientists and highly capable analysts.

Databricks Key Features

Databricks lives in the cloud and is based on Apache Spark. Its management layer is built around Apache Spark’s distributed computing framework, which makes management of infrastructure easier. Some of Databricks’ defining features include:

Auto-Scaling and Auto-Termination

Databricks automatically scales clusters up or down based on workload demands, optimizing resource usage and cost efficiency. It can also terminate clusters when they are no longer needed, reducing idle costs. This feature is particularly beneficial for companies with fluctuating workloads or those looking to optimize cloud costs.

MLflow

Databricks MLflow simplifies the machine learning lifecycle by providing tools to manage the end-to-end ML process—from experimentation to production deployment and monitoring. Data science teams in various industries benefit from MLflow for reproducibility, collaboration, and operationalizing machine learning models.

Delta Lake

Databricks Delta Lake provides reliable data lakes with ACID transactions and scalable metadata handling. It allows for more efficient data management and streamlines data engineering workflows. Companies dealing with large-scale data processing and analytics, especially those with real-time data needs, find Delta Lake valuable. It’s often used in industries like finance, healthcare, and retail.

Databricks Pros and Cons

Databricks offers some great strengths, including its ability to handle huge volumes of raw data, and and its multicloud approach – the platform interoperates with the leading cloud providers. However, a challenge for some: the platform is geared for advanced users; many use cases require real expertise.

Pros

• Databricks uses a batch in-stream data processing engine for distribution across multiple nodes.
• As a data lake, Databricks’ emphasis is more on use cases such as streaming, machine learning, and data science-based analytics.
• The platform can be used for raw unprocessed data in large volumes.
• Databricks is delivered as software as a service (SaaS) and can run on AWS, Azure, and Google Cloud.
• There is a data plane as well as a control plane for back-end services that delivers instant compute.
• Databricks’ query engine is said to offer high performance via a caching layer.
• Databricks provides storage by running on top of AWS S3, Azure Blob Storage, and Google Cloud Storage.

Cons

• Some users, though, report that it can appear complex and not user-friendly, as it is aimed at a technical market and needs more manual input for resizing clusters or configuration updates.
• There may be a steep learning curve for some.

AWS Redshift Overview

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. It allows users to analyze large amounts of data using SQL queries and BI tools to gain insights. Major AWS users would be best on Redshift due to better integration with the entire Amazon ecosystem.

AWS Redshift Key Features

Redshift positions itself as a petabyte-scale data warehouse service that can be used by BI tools for analysis. Some of its best features include:

Columnar Storage and Massively Parallel Processing

Amazon Redshift uses columnar storage and MPP architecture to deliver high performance for complex queries on large datasets. It’s optimized for analytics workloads. Redshift is designed for scalability and performance, making it suitable for enterprises processing terabytes to petabytes of data.

Integration with AWS Ecosystem

Redshift seamlessly integrates with other AWS services like S3, Glue, and IAM, simplifying data ingestion, transformation, and security management within the AWS cloud. Companies heavily invested in the AWS ecosystem and those looking for a fully managed data warehousing solution often choose Redshift.

Concurrency Scaling

Redshift’s concurrency scaling functionality automatically adds and removes query processing power in response to the workload, ensuring consistently fast query performance even during peak usage. This capability is essential for businesses with unpredictable query patterns or those needing consistent performance under heavy loads, such as during business intelligence reporting.

AWS Redshift Pros and Cons

Redshift certainly benefits from being a product of the powerful AWS platform – it offers enormous scalability, and provides a long list of services. However, in some instances it can be expensive, and it doesn’t support all types of semi-structured data.

Pros

• Redshift scales up and down easily.
• Amazon offers independent clusters for load balancing to enhance performance.
• Redshift offers good query performance — courtesy of high-bandwidth connections, proximity to users due to the many Amazon data centers around the world, and tailored communication protocols.
• Amazon provides many services that enable easy access to reliable backups for Redshift datasets.

Cons

• Some users noted that Redshift can sometimes be complex to set up and use at times and ties up more IT time on maintenance due to lack of automation.
• A lack of flexibility in areas, such as resizing, can lead to extra expense and long hours of maintenance.
• It lacks support for some semi-structured data types.

Databricks vs. Redshift: Support and Ease of Implementation

Databricks offers an array of support of advanced use cases, while Redshift tends to be more user friendly.

Databricks

Databricks offers a variety of support options that can be used for technical and developer use cases:

• Databricks can run Python, Spark Scholar, SQL, NC SQL, and other platforms.
• It comes with its own user interface as well as ways to connect to endpoints, such as Java database connectivity (JDBC) connectors.

Redshift

Amazon Redshift is said to be user-friendly and demands little administration for everyday use:

• Setup, integration, and query running are easy for those already storing data on Amazon S3.
• Redshift supports multiple data output formats, including JSON.
• Those with a background in SQL will find it easy to harness PostgreSQL to work with data.

Support and Implementation Winner: Redshift

This category is close, although Redshift is the narrow winner. The platform benefits from its support by AWS. The platform offers relatively accessible ease of implementation.

Databricks vs. Redshift: Integration

Databricks in some cases calls for third party solutions to integrate certain tools, while Redshift is of course a top choice for existing AWS customers.

Databricks

Databricks requires some third-party tools and application programming interface (API) configurations to integrate governance and data lineage features. Databricks supports any format of data, including unstructured data. But it lacks the vendor partnership depth and breadth that Amazon can muster.

Redshift

Obviously, those already committed to the AWS platforms will find integration seamless on Redshift with services like Athena, DMS, DynamoDB, and CloudWatch. The level of integration within AWS is excellent.

Integration Winner: It Depends

Redshift wins in this category, if a company is an AWS client. Obviously, the fact that Redshift is an integral part of the AWS platform helps in this category. In contrast, Databricks integrates with all the major cloud providers (including AWS, of course) and is used by multicloud clients – it clearly is not AWS-dependent.

Databricks vs. Redshift: Pricing

Pricing can vary considerably based on use case: Databricks can be pricey for users who require consultant help, and Redshift charges by the second if daily allotment is exceeded. This category is practically a toss-up.

Databricks

Databricks takes a different approach to packaging its services. Compute pricing for Databricks is tiered and charged per unit of processing, with its lowest paid tier starting at $99 per month. However, there is a free version for those who want to test it out before upgrading to a paid plan.

Databricks may work out cheaper for some users, depending on the way the storage is used and the frequency of use. For example, consultant fees for those needing help are said to be expensive.

Redshift

Redshift provides a dedicated amount of daily concurrency scaling. But you get charged by the second if it is exceeded. Customers can be charged an hourly rate by type and cluster nodes or by amount of byte scanning. That said, Redshift’s long-term contracts come with big discounts.

Roughly speaking, Redshift has a low cost per hour. But the rate of usage will vary tremendously depending on the workload. Some users say Redshift is less expensive for on-demand pricing and that large datasets cost more.

Pricing Winner: Redshift

This is a close one, as it varies from use case to use case, but Amazon Redshift gets the nod.

The differences between them make it difficult to do a full apples-to-apples comparison. Users are advised to assess the resources they expect to need to support their forecast data volume, amount of processing, and analysis requirements before making a purchasing decision.

Databricks vs. Redshift: Security

Like pricing, this category is a close call. Both platform are focused on security.

Databricks

Databricks provides role-based access control (RBAC), automatic encryption, and plenty of other advanced security features. These features include network controls, governance, auditing and customer-managed keys. The company’s serverless compute deployments are protected by multiple layers of security.

Redshift

Redshift does a solid job with security and compliance. These features are enforced comprehensively for all users.

Additionally, tools are available for access management, cluster encryption, security groups for clusters, data encryption in transit and at rest, SSL connection security, and sign-in credential security. These tools enable security teams to monitor network access and traffic for any irregularities that might indicate a breach.

Access rights are granular and can be localized. Thus, Redshift makes it easy to restrict inbound or outbound access to clusters. The network can also be isolated within a virtual private cloud (VPC) and linked to the IT infrastructure via a virtual private network (VPN).

Security Winner: Tie

Both platforms do a good job of security, with strong compliance and monitoring tools, so there is no clear winner in this category.

Who Shouldn’t Use Databricks or AWS Redshift?

Who Shouldn’t Use Databricks 

• Small businesses with minimal data needs: For small businesses with relatively simple data processing and analysis requirements, Databricks may be overly complex and expensive.
• Companies not leveraging cloud platforms: Databricks is tightly integrated with major cloud platforms like AWS, Azure, and GCP. If an organization prefers on-premises solutions or has strict data residency requirements that limit cloud adoption, Databricks may not be the best fit.
• Limited use cases: If the primary focus is on traditional data warehousing and analytics without extensive machine learning or data engineering needs, simpler tools like traditional SQL-based data warehouses might be more suitable.

Who Shouldn’t Use Redshift

• Non-AWS cloud users: Although Redshift is tightly integrated with AWS services, organizations using other cloud providers like Azure or Google Cloud Platform might face challenges in terms of interoperability and data transfer costs when considering Redshift.
• Small-scale or start-up companies: Redshift, being a powerful data warehousing solution, may not be cost-effective for smaller businesses with limited data volumes and budget constraints.

2 Top Alternatives to Databricks & AWS Redshift

Google Cloud Dataproc

Google Cloud Dataproc is a managed Apache Spark and Hadoop service offered by Google Cloud Platform. Similar to Databricks, it provides a fully managed environment for running Spark and Hadoop jobs. However, unlike Databricks, Google Cloud Dataproc supports a broader range of open-source big data tools beyond Spark, such as Hadoop, Hive, and Pig.

Snowflake

Snowflake is a cloud-based data warehouse solution that offers similar capabilities to Redshift. It is known for its simplicity, scalability, and separation of storage and compute. Snowflake automatically handles infrastructure management, scaling, and performance optimization, making it easier to use compared to Redshift.

How We Evaluated Databricks vs. AWS Redshift

To write this review, we evaluated each tool’s key capabilities across various data points. We compared their features, ease of implementation, support, pricing, and integrations to help you determine which platform is the best option for your business.

Our analysis found that Databricks and Redshift tie for features and security, the integration category is a toss-up, and Redshift tops for ease of implementation and pricing – though pricing can vary of course based on utilization.

Bottom Line: Databricks and AWS Redshift Use Different Approaches 

In summary, Databricks wins for a technical audience, and Amazon wins for a less technically savvy user base. Databricks provides pretty much all of the data management functionality offered by AWS Redshift. But it isn’t as easy to use, has a steep learning curve, and requires plenty of maintenance. Yet it can address a wider set of data workloads and languages. And those familiar with Apache Spark will tend to gravitate towards Databricks.

AWS Redshift is best for users on the AWS platform that just want to deploy a good data warehouse rapidly without bogging down in configurations, data science minutia, or manual setup. It isn’t nearly as high-end as Databricks, which is aimed more at complex data engineering, ETL (extract, transform, and load), data science, and streaming workloads. But Redshift also integrates with various data loading and ETL tools and BI reporting, data mining, and analytics tools. The fact that Databricks can run Python, Spark Scholar, SQL, NC SQL, and more will certainly make it attractive to developers in those camps.

The post Databricks vs. Redshift: Data Platform Comparison appeared first on eWEEK.

Our AI jobs salary guide analyzes various AI roles and their average compensation to help you understand the earning potential of each role.

AI Jobs Salary: Comparison Chart

Important Note:  The salary data provided in this guide is sourced from Glassdoor, a reputable platform for job seekers and professionals to access salary insights and company reviews. It’s important to keep in mind that actual salaries may vary slightly based on factors such as the type of company (e.g., startup, mid-sized corporation, large enterprise) and the financial standing of the company at the time of employment.

While the artificial intelligence salary ranges discussed here represent typical compensation levels for AI roles based on industry standards, individual offers may vary based on negotiation, location, specific job responsibilities, and other factors.

Machine Learning Engineer

Machine learning engineers design and implement machine learning algorithms and models to solve specific business problems. Machine learning, because it already has so many practical applications, is generally considered to be one of the fastest growing job titles in the AI landscape. Salary increases can come quickly with experience in advanced skills like machine learning frameworks.

• Early to mid-career: $111,000 – $195,000
• Advanced professional: $147,000 – $284,000.

To learn about ML certifications that can advance your career, see our guide, 6 Best Machine Learning Certifications.

AI Engineer

AI engineers develop and deploy AI systems, including machine learning models and deep learning algorithms. They create algorithms, build AI models, and integrate them into applications and products. The amount you earn leans on your level of experience. AI engineers tend to be employed in the technology, finance, healthcare, and consulting industries.

• Early to mid-career: $102,000 – $204,000
• Advanced professional: $113,000 – $215,000

To learn about AI certifications that can advance your career, see our guide, 30 Top AI Certifications.

Data Scientist

Data scientists are responsible for collecting, analyzing, and interpreting complex data to inform business decisions and strategies. They utilize statistical techniques and machine learning algorithms to derive insights and solve problems. Data scientists require a solid understanding of programming languages like Python or R, as well as expertise in data manipulation and visualization. Experienced data scientists with advanced degrees or specialized skills are in very high demand.

• Early to mid-career: $110,000 – $183,000
• Advanced professional: $119,000 – $229,000

Computer Vision Engineer

A computer vision engineer specializes in developing algorithms and systems that enable machines to interpret and understand visual information from digital images or videos. They work on tasks such as object detection, image recognition, and video analysis. Computer Vision Engineers often collaborate with software developers and researchers to implement and optimize computer vision solutions for various applications such as autonomous vehicles, medical imaging, robotics, and augmented reality. Startups and companies focusing on advanced technologies often offer competitive compensation packages with additional perks like stock options.

• Early to mid-career: $100,000 – $186,000
• Advanced professional: $107,000 – $237,000

Natural Language Processing Engineer

Natural language processing (NLP) engineers develop algorithms and models to enable computers to understand, interpret, and generate human language, or a reasonable facsimile of human speech. They work on tasks such as text classification, sentiment analysis, machine translation, and chatbot development. There are already many highly practical applications of NLP, hence the high salary range at the top end.

• Early to mid-career: $129,000 – $221,000
• Advanced professional: $147,000 – $344,000

Deep Learning Engineer

A deep learning engineer is a specialized role within the broader field of artificial intelligence and machine learning. These professionals focus on developing and implementing deep neural networks and other machine learning algorithms to solve complex problems like image recognition, natural language processing, and autonomous driving. Due to their high-demand skill set, deep learning engineers command competitive compensation.

• Early to mid-career: $109,000 – $189,000
• Advanced professional: $134,000 – $270,000

AI Research Scientist

An AI research scientist is at the forefront of developing new algorithms, models, and techniques to advance the field of artificial intelligence. These professionals typically hold advanced degrees in computer science, mathematics, or related fields and are skilled in areas like machine learning, statistics, and data analysis. Because an AI research scientist may do more theoretical work, the pay is not always as high as other AI specialities.

• Early to mid-career: $129,000 – $194,000
• Advanced professional: $141,000 – $265,000

Business Development Manager

The role of a business development manager in AI is crucial for bridging the gap between technical innovation and market growth. These professionals identify business opportunities, forge strategic partnerships, and drive revenue growth for AI products and services. They play a momentous role in scaling AI solutions and driving adoption among customers. Experience level and track record of successful business development initiatives significantly impact compensation.

• Early to mid-career: $94,000 – $162,000
• Advanced professional: $97,000 – $193,000

To learn about generative AI certifications that can advance your career, see our guide, 12 Best Generative AI Certifications.

AI Product Manager

AI product managers oversee the development and deployment of AI-powered products and solutions. They collaborate with cross-functional teams, including engineers, designers, and marketers, to define product roadmaps, prioritize features, and ensure alignment with business objectives. Experience in product management coupled with AI expertise commands a premium in this role.

• Early to mid-career: $131,000 – $221,000
• Advanced professional: $144,000 – $276,000

AI Consultant

AI Consultants provide expert guidance to organizations seeking to leverage AI technologies for business transformation. They assess client needs, design AI solutions, and oversee implementation to deliver tangible outcomes. Salary for AI consultants can vary widely based on consulting firm, experience level, and project scope. Geographic location also influences compensation, with major consulting hubs offering higher salaries.

• Early to mid-career: $108,000 – $187,000
• Advanced professional: $116,000 – $219,000

How Do I Get Paid More as an AI Professional? 

To increase your earning potential and get paid more as an AI professional, the following strategies can be effective:

Hone Your Skills

Continuously improving and refining your skills is key to increasing your value in the job market. Stay updated with the latest AI trends and technologies in your sector. Take online courses, attend workshops, and actively seek opportunities to deepen your expertise.

Get Certified

Obtaining relevant AI certifications can significantly boost your credentials and marketability. Certifications validate your expertise and demonstrate your commitment to professional development.

Get a Degree

While not always necessary, having an advanced degree (such as a master’s or MBA) can enhance your earning potential, especially in certain fields like business, healthcare, or technology.

Build a Portfolio

Showcase your skills and accomplishments through a strong portfolio. This could include projects you’ve worked on, case studies, articles, or designs. A compelling portfolio demonstrates your capabilities and can differentiate you from other candidates when negotiating higher compensation.

Network

Attend industry events, join professional associations, and connect with peers and mentors in your field. Networking can lead to new job opportunities, referrals, and valuable insights into salary trends.

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

5 Tips For Negotiating a Higher AI Job Salary

• Research market rates: Before entering negotiations, research the typical salary range for the specific AI role and location. Websites like Glassdoor, PayScale, or industry reports can provide valuable insights into what companies pay for similar positions.
• Highlight your value proposition: During negotiations, emphasize the unique skills, experience, and qualifications you bring. Share specific examples of past projects or achievements demonstrating your AI expertise. Quantify your contributions whenever possible.
• Be prepared to negotiate beyond base salary: Salary negotiations aren’t just about base pay. Consider other compensation components such as bonuses, stock options, healthcare benefits, or professional development opportunities. If the company can’t meet your desired salary, explore different avenues for compensation that may be negotiable.
• Practice effective communication: Approach salary negotiations with confidence and professionalism. Clearly articulate your salary expectations based on market research and your value proposition. Be respectful but assertive, and avoid underselling yourself.
• Know when and how to negotiate: Timing is crucial. Ideally, negotiate after receiving a job offer but before accepting it. Keep the negotiation collaborative rather than adversarial, aiming for a win-win outcome for you and the company.

AI Jobs Salary Outlook: Major Job Growth 

The salary outlook for AI jobs remains promising due to the increasing demand for skilled professionals.

According to ZipRecruiter, the job outlook for AI careers is expected to grow massively as AI becomes capable of accomplishing more tasks. Data from the National Bureau of Labor Statistics shows that employment in computer and information technology occupations is projected to grow much faster than the average for all occupations from 2022 to 2032. The Bureau stated that about 377,500 openings are projected each year, on average.

​​This significant demand underscores the favorable salary outlook for AI professionals as organizations compete to attract top talent with specialized skills in artificial intelligence, machine learning, data analytics, and related fields.

Bottom Line: The Future for AI Salaries is Bright

AI jobs can be highly lucrative, especially for individuals with the right skills, experience, and qualifications. Salaries for AI-related roles vary based on job title, location, industry, level of expertise, and labor market demand-supply dynamics – yet in any case, are some of the highest salaries across the tech sector.

The increasing demand for AI expertise is driving the overall salary outlook upward. Organizations are willing to invest in top talent, and this talent is scarce. To maximize earning potential in AI jobs, professionals should focus on continuous learning, skill development, and staying abreast of industry trends.

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

The post AI Jobs Salary Guide 2024 appeared first on eWEEK.

To be sure, AI is highly complex, and its complexity requires professional expertise to build a successful career. Still, for tech professionals, AI offers an incredible open door to get in on the ground floor of many emerging career paths.

In this guide, we look at 10 AI career paths to consider, as well as core issues for you to be aware of as you consider this career shift.

AI Careers: Leading Positions

The artificial intelligence career positions below range from AI consultant to AI programmer to research scientist, but many of these positions may overlap with one another. Indeed, the actual duties of AI careers are shifting as the AI sector itself undergoes rapid growth.

1. AI Consultant

The AI consultant is currently one of the most sought-after professionals in the artificial intelligence sector. AI consultants are responsible for advising and supporting businesses and other organizations in implementing AI. Responsibilities include everything from managing implementation projects to ensuring AI software are compatible with an organization’s needs.

Because of these varying duties, AI consultants must understand AI in its entirety. They must understand complex topics such as machine learning and deep learning, as well as how to construct and use algorithms.

Certification or Degree

• Certified Artificial Intelligence Consultant (CAIC™) offered by USAII.
• Certified Artificial Intelligence Practitioner (CAIP) offered by CertNexus.

Skills

• Computing expertise — proficiency in programming languages like Java, C++, Python, and Ruby.
• Technical abilities.
• Mathematical mastery.
• Knowledge of data processing and visualization techniques.

Salary Range

Data from Glassdoor shows that the average salary for an AI consultant is $172,835 per year.

Career Advancement Opportunities

AI consultants often advance to managerial positions, overseeing teams of consultants and driving AI strategy for organizations.

To learn about AI certifications that can advance your career, see our guide, 30 Top AI Certifications. 

2. AI Prompt Specialist

Also known as prompt engineers, these specialists are tasked with developing prompts to be used with generative AI tools. Some of these tools include ChatGPT and Midjourney.

Prompts guide these AI tools and help them produce specific results. Prompt specialists must understand how these tools work and how best to communicate with them. Because of the wide use of these tools, prompt specialists can work in a wide range of industries, from advertising to manufacturing.

Certification or Degree

• Prompt Engineering for ChatGPT by Vanderbilt University.
• ChatGPT Prompt Engineering for Developers by OpenAI and DeepLearning.AI.
• The Complete Prompt Engineering for AI Bootcamp.
• ChatGPT Masterclass: The Guide to AI & Prompt Engineering – Udemy.

Skills

• Expertise in natural language processing and understanding to generate effective prompts for AI systems.
• Proficiency in developing algorithms and models to create relevant and engaging prompts for various applications.
• Linguistic proficiency.
• NLP knowledge.
• Creative and adaptable writing.
• Iterative testing and learning.

Salary Range

The salary range for an AI prompt specialist can vary based on factors such as location, level of experience, and company size. On average, the salary for an AI prompt specialist ranges from $80,000 to $150,000 per year. However, in high-demand markets or with additional specialized skills, the salary range can exceed $150,000.

Career Advancement Opportunities

As an AI consultant gains experience and demonstrate proficiency in creating effective prompts for AI systems, they may advance to a senior AI prompt specialist role. In this position, they may lead prompt generation projects, mentor junior team members, and have higher-level responsibilities.

3. AI Programmer

A career as an AI programmer requires expertise in using algorithms to program AI-based services, apps, and software. These programmers are also responsible for training machines to produce human-like input and for troubleshooting issues within AI-based tools.

AI programmers must have a solid handle on programming languages such as Python and Java. They must also understand the basics of software development and have an extensive knowledge of how AI concepts support software development.

Certification or Degree

• Maryville University’s online artificial intelligence certificate.
• AI Nanodegree by Udacity.

Skills

• Programming languages: Proficiency in languages such as Python, Java, C++, or R is essential for AI programming.
• Knowledge of machine learning algorithms, techniques, and frameworks such as TensorFlow, Keras, or Scikit-learn.
• Algorithm design and optimization.
• Problem-solving.

Salary Range

Our research found that AI programmers can expect to earn an average salary of around $143,000 per year in the United States. Salaries can be higher for those with advanced degrees, specialized skills, and several years of experience in the field.

Career Advancement Opportunities

AI programmers could transition into a research-focused role, which involves conducting research in an array of advanced artificial intelligence topics, publishing papers, and contributing to advancements in the field.

For more information about generative AI providers – all of which hire AI experts – read our in-depth guide: Generative AI Companies: Top 20 Leaders

4. AI Developer

While similar to programmers, AI developers develop and design the AI-based services, apps, and software organizations use. For example, AI developers ensure that the user interface of an AI-based tool is easy to use.

AI developers also test software applications to ensure they result in the desired outcomes. In some cases, AI programmers and developers will work alongside one another.

Developers must have solid problem-solving skills and creativity while also understanding data engineering and analytics. This AI career tends to be highly lucrative.

Certification or Degree

• IBM AI Developer Professional Certificate on Coursera.
• Global Tech Council’s Certified Artificial Intelligence (AI) Developer certification.
• Certified AI Developer by Blockchain Council.

Skills

• Proficiency in programming languages such as Python, Java, or C++.
• Knowledge of machine learning algorithms and techniques.
• Experience working with big data technologies like Hadoop, Spark, or Apache Flink.
• Strong problem-solving skills and analytical thinking.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Familiarity with software development best practices and version control systems like Git.

Salary Range

Data from ZipRecruiter shows that the average salary for an AI developer in the US in 2024 is $129,348 per year, or $62 per hour.

Career Advancement Opportunities

AI developers can transition into a senior AI developer, an AI architect, a research role, or become an AI consultant.

5. Data Scientist

A data scientist’s key responsibility is solving complex problems using data. Data scientists perform a wide range of duties, including building models using programming languages, finding patterns in data sets, communicating insights to stakeholders, and consulting with organizations on how to best use their data.

Data science plays a crucial role in AI. Scientists work to build the advanced algorithms necessary to uncover complex patterns. Those patterns can then be used to train still more advanced AI models.

Certification or Degree

• IBM Data Science Professional Certificate on Coursera.
• The Data Science Council of America’s (DASCA) Senior Data Scientist (SDS) certification program.
• Azure Data Scientist Associate certification from Microsoft.

Skills

• Strong knowledge of programming languages such as Python, R, or SQL.
• Proficiency in data analysis and statistical tools.
• Familiarity with machine learning algorithms and techniques.
• Ability to work with large datasets and databases.
• Attention to detail and accuracy in data analysis.

Salary Range

According to Indeed, the average salary for a data scientist is $124,337 per year in the United States.

Career Advancement Opportunities

At a more senior level, data scientists may have the opportunity to become a chief data officer, where they are responsible for the organization’s data strategy, governance, and ensuring that data-driven insights are used to drive business decisions.

6. AI Research Scientist

As AI continues to evolve, organizations will need to find innovative ways to use it. A career as an AI research scientist involves exploring the use of AI to uncover new opportunities – most of which can now hardly be imagined. They also build algorithms and perform experiments to test new AI methods.

Research scientists must be highly inquisitive and creative. They must also have a firm grasp on complex subjects such as statistics, data analytics, machine learning, and natural language processing.

Certification or Degree

• Certified Research Scientist (CRS) offered by the American Statistical Association (ASA).
• Certified AI Research Scientist (CAIRS) program offered by American Institute of Business and Management.

Skills

• Strong background in computer science, mathematics, and statistics.
• Deep understanding of machine learning algorithms and neural networks.
• Proficiency in programming languages such as Python, Java, or C++.
• Ability to analyze data and draw meaningful insights from it.
• Experience with AI research projects and publications.
• Continuous learning and staying updated on the latest developments in the field of AI.

Salary Range

According to Glassdoor, the estimated pay for an AI research scientist is $171,630 per year.

Career Advancement Opportunities

As AI technology evolves, there is a growing need for professionals who can address the ethical considerations and policy implications of AI. AI research scientists could transition into roles focused on AI ethics, responsible AI development, and policy-making related to AI technologies. Alternatively, they could transition into a career in academia, teaching AI courses and conducting research at universities or research institutions.

7. Machine Learning Engineer

The machine learning engineer plays a critical role in the development of AI programs, applications, and software. Machine learning engineers work to design the models and algorithms that make AI work.

The process of designing these models requires the ML engineer to organize data, test models, program AI models using languages like Python, and perform production-level tasks to support ML development.

Certification or Degree

• Professional Machine Learning Engineer by Google.
• Andrew Ng’s Machine Learning Specialization on Coursera.
• IBM Machine Learning Professional Certificate on Coursera.
• AWS Certified Machine Learning – Specialty on Coursera.

Skills

• Strong knowledge of machine learning algorithms and techniques.
• Proficiency in programming languages such as Python, R, or Java.
• Familiarity with data manipulation and analysis tools like NumPy, Pandas, and Scikit-learn.
• Experience with data visualization libraries like Matplotlib and Seaborn.
• Understanding of statistics and probability theory.
• Knowledge of deep learning frameworks such as TensorFlow or PyTorch.
• Ability to work with big data technologies like Hadoop, Spark, or SQL.
• Strong problem-solving and analytical skills.
• Good communication and teamwork abilities.

Salary Range

Data from Indeed shows that the average salary for a machine learning engineer is $165,685 per year.

Career Advancement Opportunities

ML engineers can become a machine learning researchers with a focus on developing new algorithms, techniques, and technologies within the field of ML. This role requires a strong background in computer science and mathematics.

To learn about ML certifications that can advance your career, see our guide, 6 Best Machine Learning Certifications

8. Deep Learning Engineer

While deep learning is a branch of machine learning, deep learning goes beyond machine learning in that it enables AI models to learn and make their own decisions. In other words, deep learning enables machines to learn and act in manner similar to the human brain.

Deep learning engineers use this process to build highly intelligent AI models, most often for the purpose of predictive analytics. These engineers must be experts in machine learning, neural networking, data science, and programming.

Certification or Degree

• IABAC’s Certified Deep Learning Expert Certification (CDLE – AI3060).
• Certified Deep Learning Associate by Skillfloor.
• Certified Deep Learning Expert Course offered by Datamites Global Training Institute.

Skills

• Proficiency in programming languages such as Python, C++, or Java.
• Strong foundation in mathematics, especially linear algebra, calculus, and statistics.
• Understanding of machine learning algorithms and techniques.
• Familiarity with deep learning frameworks such as TensorFlow, PyTorch, or Keras.
• Experience with data preprocessing, feature engineering, and model evaluation.
• Knowledge of neural network architectures and how to design and optimize them.

Salary Range

The average salary for a deep learning software engineer is $148,300 per year, per Glassdoor.

Career Advancement Opportunities

With experience and proven expertise in deep learning, professionals can advance into leadership positions such as technical lead, team manager, or project manager, where they can oversee and guide teams of AI/ML engineers.

9. Natural Language Processing (NLP) Engineer

Natural language processing, or NLP, refers to a computer’s ability to understand human language. NLP is a major force behind popular AI tools such as chatbots like ChatGPT.

A career as an NLP engineer requires deep expertise in programming AI models and systems that can understand human language. This means that while NLP engineers must understand programming and AI, they must also be experts in language and linguistics.

Certification or Degree

• Natural Language Processing Specialization on Coursera (offered by DeepLearning.AI).
• Natural Language Processing (NLP) with Python for Machine Learning on Udemy.
• Natural Language Processing in TensorFlow on Coursera (offered by Deeplearning.AI).
• Natural Language Processing Nanodegree by Udacity.

Skills

• Proficiency in programming languages such as Python, Java, or C++.
• Strong knowledge of natural language processing techniques and algorithms.
• Familiarity with data preprocessing and text processing tools.

Salary Range

According to Talent.com, the average NLP engineer salary is $170,000 per year. Entry level positions start at $130,000 per year, while many experienced workers make up to $208,400 per year.

Career Advancement Opportunities

An NLP engineer might transition into a role as an NLP researcher. These researchers focus on advancing the field through theoretical and applied research, publishing papers, and collaborating with academic institutions. Optionally, they can become an NLP architect and be responsible for designing and implementing the overall architecture of NLP systems within an organization.

10. Robotics Engineer

Robotics engineers are tasked with using machines and algorithms to build robots, typically software robots but sometimes “humanoid” and/or mechanical robots. They’re also responsible for testing and maintaining robotic systems and machines across a wide range of industries, from manufacturing to retail.

The skills required to be a robotics engineer are a unique – and complex – mix. Not only do you need to understand AI, programming, and mathematics, but in some cases you must also have mechanical skills.

Certification or Degree

• Robotics Engineering & Applications offered by L&T EduTech on Coursera.
• Certified Robotic Engineer Courses by SMEC.
• Certified Robotics Engineer (CRE) certification program offered by the Institute of Electrical and Electronics Engineers (IEEE).

Skills

• Strong understanding of robotics principles and systems.
• Proficiency in programming languages such as C++, Python, and MATLAB.
• Knowledge of electronics and electrical systems.
• Ability to design, build, and troubleshoot robotics systems.
• Strong analytical and mathematical skills.
• Understanding of mechanical engineering principles.
• Familiarity with robotic sensors and actuators.

Salary Range

On average, robotics engineers can expect to earn between $110,638 and $139,428 per year, per salary.com.

Career Advancement Opportunities 

Robotics engineers can advance their careers by specializing in specific areas of robotics, such as autonomous navigation, computer vision, or industrial automation.

How to Start a Career in Artificial Intelligence

If a career in AI seems like a fit for you, there are a few steps you must take to get started, including selecting a career path, obtaining the proper training, and building a portfolio to showcase your experience to potential employers.

Select an AI Career Path

The first step is to choose an AI career path. This choice should be based on factors such as your future career goals, current skill set, and interests.

For example, while all aspects of AI require the use of math, some careers will be more math-heavy than others. If you’re a numbers person, a career as a data scientist or programmer might be a solid choice for you.

However, if you simply enjoy being on the bleeding edge of technology, you may opt for robotics. As AI is a wide-open field, be sure to do your research to uncover the constantly shifting AI job market.

Obtain an AI-Focused Certification or Degree

The next step is to obtain the proper education and training for your selected career path. Education will vary, depending on what you choose.

For example, if you wish to start a career as an AI programmer, you’ll need to understand programming languages such as Python, as well as AI-specific concepts like machine learning. On the other hand, an AI consultant will need to understand data science and the many layers of AI.

There are various methods for obtaining the required training. For example, you can take various AI-related courses online or opt for a traditional college program. Often, organizations will also offer internship programs where students can learn AI in a real-world environment.

Build your AI portfolio

Building your portfolio will ensure you have proof of your AI-specific skill set. To build a portfolio, complete AI projects that pertain to your chosen career path. For example, build a machine learning model or algorithm or design a chatbot.

Apply to Entry-Level Jobs

Once you have obtained the necessary education and built a portfolio, it’s time to start applying for entry-level AI jobs. Look for opportunities at AI companies or departments within larger organizations. You can also network with professionals in the field and attend industry events to increase your chances of finding job openings.

The Future of AI Career Paths

The artificial intelligence industry is in the middle of a boom. According to recent data, the market size was $95.60 billion in 2021. However, by 2030, the market size is forecasted to reach $1.8 trillion, growing at a stunning CAGR of 32.9%.

This growth will drive AI companies and other organizations to look for additional support. According to the Bureau of Labor Statistics, computer and information technology jobs will increase by 15% between now and 2031. This is much faster than average growth when compared to all other occupations.

Job opportunities range from those specific to AI to those specific to data and machine learning. Due to the nature of AI and its complexities, there are virtually endless career paths to choose from.

Frequently Asked Questions (FAQs)

Is AI a Good Career?

AI is a highly promising and lucrative career field with a growing demand for skilled professionals. With advancements in technology and the increasing integration of AI in various industries, pursuing a career in AI can lead to numerous opportunities for growth and development.

What Skills Do You Need To Land an Entry-Level AI Position?

To land an entry-level AI position, you typically need skills in programming languages such as Python, R, Java, or C++, as well as knowledge of data structures, algorithms, and machine learning concepts. Strong analytical and problem-solving abilities, as well as a solid understanding of statistics, are also important for success in AI roles.

Which Industries Hire AI Professionals?

AI professionals are in demand across a wide range of industries, including but not limited to healthcare, finance, retail, manufacturing, automotive, and technology.

The Bottom Line: AI Career Opportunities are Growing

As the AI industry continues to grow, so will the opportunities for a highly successful career as an AI professional. While AI leverages software and hardware to perform its highly technical work, humans must still be the masterminds behind it. If you’re considering a new career path in tech, a career in AI might be an exceptionally rewarding fit.

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 10 Best AI Career Paths 2024: Securing a Future in AI appeared first on eWEEK.

Especially with today’s increasingly complex projects, AI project management tools empower organizations to deliver projects with far greater efficiency.

In our review of the best AI project management software, we analyzed the best PM software for different categories of users across various industries. See our top picks for the best AI project management tools of 2024 to determine which one is the best option for your company.

• ClickUp: Best overall
• Notion: Best for simplicity and ease of use
• Taskade: Best for real-time collaboration
• Basecamp: Best for small teams and startups
• Asana: Best for advanced PM features
• Wrike: Best for the enterprise
• Trello: Best for affordability and integrations
• OneCal: Best for schedule management
• Forecast: Best for predictable project execution and risk management
• Motion: Best for automating project planning

Best AI Project Management Tools: Comparison Chart

ClickUp: Best Overall

Overall rating: 4.5/5

ClickUp helps teams organize tasks, track progress, collaborate, and manage workflows. It offers a wide array of features, including task management, document sharing, time tracking, goal setting, calendar integration, and more.

Our review of ClickUp highlights the platform’s exceptional feature set, earning a solid 5 out of 5 rating. Users appreciate the robust project management capabilities, collaboration tools, and integrations that ClickUp offers. We scored its pricing 4.1 due to its affordability relative to comparable tools on the market. While the platform’s ease of use received a rating of 4, indicating minor room for improvement in user experience and onboarding, the excellent support provided by ClickUp garnered a perfect 5 rating, as it offers 24/7 support and extensive resources to assist users.

ClickUp has an artificial intelligence add-on feature available in ClickUp’s paid plans. With ClickUp AI, users can access over 100 crafted prompts and inputs tailored for different roles within the team. This feature allows you to generate ideas, action items, documents, and summaries, helping to fast-track writing and workflows with consistency and clarity in communication.

Pros and Cons

Pricing

• Free: No cost for unlimited team members, but it lacks support for AI capabilities.
• Unlimited: $7 per user per month, billed annually, or $10 per user per month, billed monthly.
• Business: $12 per user per month, billed annually, or $19 per user per month, billed monthly.
• Enterprise: Custom quote.
• ClickUp AI: You can add it to any paid plan for $5 per user per month.

Features

• AI compatibility across all paid plans.
• Multiple views – up to 16 – including list, kanban, Gantt, timeline, and table.
• Advanced automation.
• In-app video recording.
• Agile project management.

Notion: Best for Simplicity and Ease of Use

Overall rating: 4.5/5

Notion is a workspace app that lets you take notes, write documents, create databases, collaborate with others, and organize your ideas in one centralized place. It is ideal for personal organization, work projects, and team collaboration. Notion has an artificial intelligence feature within the platform, offering unlimited access to tools such as Q&A, autofill, and writer. With Q&A, you can get instant answers to your questions; the autofill allows you to pull insights from hundreds of pages at once; and the writer tool can help you brainstorm new ideas and create first drafts for your projects.

Notion received an overall rating of 4.5 out of 5. It earned top marks in feature set and pricing, earning a perfect 5. While Notion excels in feature set and pricing, it falls short in support, receiving a medium score of 3 out of 5.

Pros and Cons

Pricing

• Free: No cost for personal use. Unlike ClickUp, you can add Notion AI to its free plan.
• Plus: $8 per user per month, billed annually, or $10 per user per month, billed monthly.
• Business: $15 per user per month, billed annually, or $18 per user per month, billed monthly.
• Enterprise: Custom quote.
• Notion AI: You can add Notion AI to your workspace for $8 per user per month billed annually or $10 per member per month billed monthly.

Features

• Notion AI’s key capabilities include question and answer, autofill, and writing tools.
• Multiple views (timeline, boards, calendar, and more).
• Basic and advanced automation.
• Subtasks and dependencies.
• Real-time collaboration.

Taskade: Best for Real-Time Collaboration

Overall rating: 4.2/5

Taskade is a productivity tool that helps individuals and teams collaborate, manage tasks, and stay organized. It offers features such as task lists, notes, outlines, and mind maps, along with the ability to easily share and collaborate on projects with others.

Taskade received a perfect score of 5 out of 5 for features, as it’s equipped with many AI project management capabilities that facilitate task organization, scheduling, and collaboration.

The platform uses various AI models such as GPT-3.5, GPT-4 Turbo, and GPT-4 Turbo 128K. For instance, the AI Project Studio is powered by GPT-4, enabling users to generate tailor-made, AI-powered workflow templates, thus enhancing project creation speed. The AI Chat Assistant and AI Task Assistant offer many AI-assisted commands to streamline task management and workspace interactions.

Pros and Cons

Pricing

• Free forever: No cost for one user.
• Taskade Pro: $8 per user per month, billed annually, or $10 per user per month, billed monthly.
• Taskade for Teams: $16 per user per month, billed annually, or $20 per user per month, billed monthly.

Features

• 1,000+ AI agent templates.
• 500+ AI prompt templates.
• Real-time collaboration.
• AI-assisted productivity.
• Project management templates.

Basecamp: Best for Small Teams and Startups

Overall rating: 4.1/5

Basecamp is popular among small to medium-sized businesses and remote teams for its user-friendly interface and robust features. It is a project management and collaboration tool that helps teams stay organized and work together more efficiently. It allows teams to create to-do lists, share files, communicate with team members, and track project progress.

Basecamp uses AI in its Move the Needle and Mission Control features to analyze project data and provide insights on a project’s performance. This AI-driven feature can help project managers identify potential risks or areas of concern, allowing them to take proactive steps to address issues and keep projects on track.

Pros and Cons

Pricing

A 30-day free trial is available.

• Basecamp: $15 per user per month.
• Basecamp Pro Unlimited: $299 per month, billed annually, or $349 per month, billed month-to-month.

Features

• Document management.
• Extensive collaboration options.
• Task management.
• Move the Needle and Mission Control special tools.
• Real-time chat.
• Automatic check-in.

Asana: Best for Advanced Project Management Features

Overall rating: 4.1/5

With an excellent rating of 4.1 out of 5, Asana earns a spot on our list for its extensive project management features. Through its Asana Intelligence, it offers various features and functionalities that use AI and machine learning to enhance the user experience and improve productivity.

For example, its smart editor analyzes the content entered by the user and provides suggestions for formatting and organization. Through smart summary, Asana can generate summaries of tasks, projects, or team progress based on data within the platform, giving users a quick overview of important information without digging through individual tasks.

The smart answers feature uses AI to provide users instant answers to common questions or problems within Asana.

Pros and Cons

Pricing

• Personal: No cost for up to 10 team members. Ideal for small teams or individuals.
• Starter: $10.99 per user per month, billed annually, or $13.49 per user per month, billed monthly. Ideal for small businesses.
• Advanced: $24.99 per user per month, billed annually, or $30.49 per user per month, billed monthly. Best for small and mid-sized businesses.
• Enterprise: Custom quotes. Best for large enterprises.
• Enterprise+: Custom quotes. The solution is a good fit for large enterprises with strict compliance requirements.

Features

• Integrates with hundreds of third-party tools.
• Task dependencies.
• Asana Intelligence.
• Multiple view options.

Wrike: Best for Enterprise

Overall rating: 3.8/5

Wrike received a perfect score for support and an excellent mark for ease of use. However, it falls short on pricing as its cost is somewhat high and only supports annual subscriptions, making it less flexible for smaller businesses. Despite this, its range of AI project management features and customization options make it popular for larger enterprises and teams looking for a more robust project management solution.

For AI PM features, Wrike has an AI-powered Work Intelligence capability that helps teams predict project risks by analyzing patterns and data to identify potential issues before they arise. It also offers generative AI capability, AI-recommended tasks, AI subitem creation, smart search, document processing and text recognition, smart replies and quick reactions, and voice assistant interaction on its mobile app. These AI features can enhance productivity, streamline workflow, and improve team collaboration.

Pros and Cons

Pricing

• Free: No cost for an unlimited number of users.
• Team: $9.80 per user per month, billed annually.
• Business:  $24.80 per user per month, billed annually.
• Enterprise: Custom quotes.
• Pinnacle: Custom quotes.

Features

• AI Recommended Tasks.
• AI Project Risk Prediction.
• Smart search.
• Voice assistant interaction.
• Smart replies and quick reactions.
• Document processing and text recognition.
• Advanced analytics and data visualizations.

Trello: Best for Affordability and Integrations

Overall rating: 3.8/5

Trello’s overall score of 3.8 out of 5, includes a rating of 3.5 for its feature set, indicating that it has a decent range of AI project management features, but it’s not as comprehensive as some other project management tools.

Trello is user-friendly and intuitive, making it easy for users to navigate and use its features, hence the excellent score for ease of use. Its support rating of 5 highlights Trello’s outstanding customer support, meaning you can expect prompt and helpful assistance when needed. Trello’s AI PM feature is backed by the Atlassian Intelligence tool. You can use this feature to boost your Trello card description with AI-powered content generation, correction, and brainstorming.

Pros and Cons

Pricing

• Free: Up to 10 boards per workspace.
• Standard: $5 per user per month if billed annually, or $6 per user per month if billed monthly.
• Premium: $10 per user per month if billed annually, or $12.50 per user per month if billed monthly.
• Enterprise: Range from $7.38 to $17.50 per user per month when billed annually. Your actual rate depends on the number of users in your organization.

Features

• Extensive integrations (known as Power-Ups).
• Multiple views, including calendar, timeline, table, dashboard, and map.
• AI-driven content generation, grammar correction, and brainstorming with Atlassian Intelligence.

OneCal: Best for Schedule Management

Overall rating: 3.1/5

While OneCal received good to excellent score across our ranking criteria, it performs poorly in terms of feature set — this is largely due to the fact that the software is not a dedicated project management tool, though it does have significant PM functionality.

Whether you’re an executive, manager, freelancer, or consultant, OneCal can streamline your scheduling process and ensure you stay organized across all your calendars.

OneCal lets you easily synchronize your project deadlines, team meetings, and personal events across all your calendars. This will help you stay on track with your projects, avoid conflicts, and maintain a healthy work-life balance. For example, managers can use OneCal to coordinate team meetings, client calls, and project milestones without the risk of double booking or missing important deadlines, while freelancers can leverage OneCal to automate their calendar scheduling and prioritize their client projects, ensuring they meet all their project deadlines.

Pros and Cons

Pricing

A 14-day free trial is available.

• Starter: $4 per user per month, billed annually, or $5 per user per month, billed monthly.
• Essential: $8.3 per user per month, billed annually, or $10 per user per month, billed monthly.
• Premium: $25 per user per month, billed annually, or $30 per user per month, billed monthly.

Features

• Calendar sync.
• Booking link.
• Calendar view.
• Multi-way syncs.

Forecast App: Best for Predictable Project Execution and Risk Management

Overall rating: 3.1/5

Forecast combines AI project management with resource and financial management in one single platform. Its AI project management capability offers AI-assisted task management, capacity and demand planning, task assignment, scheduling, approvals, risk management, status tracking, and timesheets.

This integrated solution helps streamline workflows, improve collaboration, and optimize resource allocation, leading to more efficient project delivery. One downside of this solution is that it lacks transparent pricing and might not be suitable for small businesses with limited budgets. Thus, it received a poor rating for pricing in our analysis.

Pros and Cons

Pricing

Contact the company for a custom quote.

Features

• AI-assisted task management.
• AI-assisted capacity and demand planning.
• Task assignment, scheduling, and approvals.
• AI-assisted risk and status management.
• AI assisted timesheets.

Motion: Best for Automating Project Planning

Overall rating: 3/5

With Motion, project tasks are seamlessly integrated into daily workflows, reducing the chances of tasks being forgotten or overlooked.

Motion project management tool integrates with your team’s calendars to ensure project work is visible and prioritized. It automates the planning process, creating personalized schedules for each team member to help them meet project deadlines. Motion is designed to automate 90% of project planning tasks, making it easier for teams to stay on track and maximize productivity.

It received a below-average score for pricing due to its lack of a free plan and high cost compared to other project management tools.

Pros and Cons

Pricing

• Individual: $19 per month, billed annually, or $34 billed monthly.
• Team: $12 per user per month, billed annually, or $20 per user per month, billed monthly.

Features

• Task management.
• Calendar management.
• Project management.
• Automated scheduling and planning with AI.
• Automated planning and prioritization of tasks for each team member.

How to Choose the Best AI Project Management Tool for Your Business

To determine the best AI project management software for your company, you must assess your organization’s needs to determine which aspect of your PM you need to augment with AI. ClickUp and Trello work well for AI content generation and summarization, while Notion and Wrike excel at smart searches and answer generation.

If you don’t want to miss tasks, consider using OneCal and Motion to sync tasks with your calendar. Forecast App will benefit those interested in project risk prediction and evaluation.

Additionally, consider the scalability of the AI project management tool. As your business grows and evolves, you want a tool that can grow with you. Integration capabilities are also necessary, so ensure the tool can easily integrate with other software and tools you may already be using.

How We Evaluated the Best AI Project Management Tools

We analyzed the most satisfactory AI project management software across four categories using 13 data points to help you discover which tool will serve your company’s needs best.

Feature Set – 35%

We assessed the range and quality of features offered by each tool, including AI capabilities, assistance with writing, automated task management, extensive integrations, and the use of AI/ML to boost performance. The more comprehensive and advanced the feature set, the higher the score.

Pricing – 25%

We considered the cost of each tool and its value for money. We looked at pricing plans, including any additional costs for AI features or premium functions, and compared them with competitors to determine each tool’s affordability and competitiveness.

Ease of Use – 25%

To determine the score for this category, we tested each tool’s user interface and user experience to evaluate how intuitive and user-friendly it was. We also assessed the learning curve involved in setting up and using the tool as well as the learning curve for users who may be new to AI project management tools.

Support – 15%

We looked at the level of customer support provided by each tool, including availability, responsiveness, and the quality of assistance offered. This includes resources such as tutorials, documentation, and online communities.

Frequently Asked Questions (FAQs)

Are AI Project Management Tools Suitable for All Types of Projects?

AI project management tools can benefit various types of projects. However, their suitability depends on the project’s complexity, team size, industry, and specific needs.

They are particularly effective for projects that involve repetitive tasks, data analysis, resource optimization, and complex scheduling. For straightforward or smaller-scale projects, simpler project management tools might suffice.

Can AI Project Management Tools Integrate with Other Software and Tools?

Yes, you can connect it with other software and tools commonly used in project management and business operations. These integrations can include collaboration platforms like Slack or Microsoft Teams, document management systems, CRM software, accounting tools, and more. All the tools we analyzed in this top product integrate with several third-party applications.

What Types of Tasks Can AI Project Management Tools Automate?

• Scheduling and resource allocation.
• Task assignment and prioritization.
• Data analysis and reporting.
• Risk assessment and mitigation.
• Communication and collaboration.
• Document management.
• Progress tracking.
• Report generation.

Bottom Line: AI Project Management Tools Improve Workflow

The best AI project management tools offer a blend of automated and advanced features and functionalities to support your project management processes. Keep in mind that finding the best AI project management tool for your business requires a thorough assessment of your organization’s needs and goals.

Whether you’re looking to streamline content generation, enhance task synchronization, or predict project risks, the tools we reviewed in this guide can improve your workflow – and help you achieve your objectives. Consider implementing one of the top AI project management tools mentioned above to optimize your complex projects.

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 10 Best AI Project Management Tools 2024 appeared first on eWEEK.

Business automation software helps reduce manual errors and allows businesses to focus on more important tasks that require human intervention. In our review of the best business automation software, we analyzed the top-rated tools to help you find the best automation solution for your organization.

Microsoft Power Automate: Best for cloud automation
HubSpot: Best for omni-channel marketing automation
Brevo: Best for email marketing automation
Zoho CRM: Best for lead management
ActiveCampaign: Best for marketing automation
Hootsuite: Best for social media management
SS&C Blue Prism: Best for automating rules-based tasks
Zapier: Best for integration automation
Workato: Best for workflow automation

Top Business Automation Software: Comparison

Microsoft Power Automate: Best for Cloud Automation

Overall rating 4.8/5

Power Automate is part of the Microsoft Power Platform, including Power BI for analytics, Power Apps for building custom applications, and Power Virtual Agents for creating AI chatbots. Together, these tools empower organizations to improve operational efficiency through automation.

Microsoft Power Automate received a perfect score of 5 out of 5 across 4 rating criteria (feature set, ease of use, support, and quality of feature set) thanks to its robust feature set, user-friendly interface, strong support resources, and high-quality functionality. On the cost side, I found the tool affordable for the features it offers — the multiple pricing options mean you can choose a plan that fits your organization’s budget.

Microsoft Power Automate enables users to automate tasks between various services and applications without the need for coding expertise. With Power Automate, users can create automated workflows to streamline repetitive tasks, trigger actions based on certain events, and integrate disparate systems and data sources. It offers a user-friendly interface that allows users to visually design their workflows using a wide range of pre-built connectors and templates.

Pros and Cons

Pricing

• Free: 30-day free trial
• Power Automate Premium: $15 per user per month
• Power Automate Process: $150 per bot per month

Features

• Interoperation with Microsoft 365
• Document automation
• Desktop and cloud automation
• Its AI features include AI authoring, AI insights, AI processing, and AI generation
• Robotic process automation (RPA)
• Task and process mining

HubSpot: Best for Omni-Channel Marketing Automation

Overall rating 4.6/5

HubSpot, a well-known name in business automation and marketing, offers a comprehensive platform that includes tools for CRM, marketing, sales, and customer service.

The platform earned an excellent overall score of 4.6 out of 5, reflects the quality of the automation functionality offered by both HubSpot’s Sales and Marketing Hubs across key criteria.

HubSpot allows businesses to create and manage marketing campaigns across multiple channels, including email, social media, and website. Users can automate various marketing tasks, personalize messages, track customer interactions, and analyze campaign performance all in one place. This integrated approach helps businesses deliver a cohesive and consistent experience to their customers across many different touch points.

Pros and Cons

Pricing

HubSpot Marketing Hub

• Marketing Hub Professional: Starts at $890 per month, billed month-to-month, or $800 per month billed annually
• Marketing Hub Enterprise: Starts at $3,600 per month

HubSpot Sales Hub

• Sales Hub Professional: Starts at $100 per month per seat, billed month-to-month, or $90 per month per seat billed annually
• Sales Hub Enterprise: Starts at $150 per month per seat

Features

• Omni-channel marketing automation
• Ad management
• Email marketing
• Website traffic analytics
• Reporting dashboard
• SEO recommendations and optimizations

Brevo: Best for Email Marketing Automation

Overall rating 4.3/5

Though Brevo scores a low mark for cost, it makes up for this by earning a perfect mark across other ranking metrics such as features, ease of use, and customer satisfaction. Users consistently praise Brevo for its powerful capabilities and user-friendly interface. Brevo’s reliable performance and positive user feedback make it a top choice for those willing to invest in a quality product.

Brevo is a comprehensive marketing and CRM platform, offering various tools and features to help businesses grow and manage customer relationships. It includes modules for email marketing, marketing automation, SMS campaigns, chat, pipeline management, and more.

With Brevo, you can create automated email campaigns, segment your audience, personalize content, and track performance metrics to optimize your email marketing efforts. It offers drip campaigns, trigger-based emails, A/B testing, and more to help you engage with your customers and drive conversions.

Pros and Cons

Pricing

Sales Platform

• Free: No cost for unlimited users
• Pro: Starts at $12/month per user

Marketing Platform

• Free: No cost for unlimited contacts
• Starter: Starts at $25 per month
• Business: Starts at $65 per month
• Enterprise: Custom pricing

Conversations Platform

• Free: No cost for one user
• Pro: Starts at $15/month per user

Features

• Deal pipelines and management
• Sales automation
• Drag and drop editor
• A/B testing

Zoho CRM: Best for Lead Management

Overall rating 4.3/5

Zoho stands out for its business automation features. It excels in Salesforce automation, workflow automation, marketing automation, and process management.

Businesses of all sizes can use Zoho CRM to handle customer relationship management activities and improve their overall capability. Zoho CRM helps companies to manage their sales, marketing, and customer support processes. It allows businesses to track customer interactions, automate sales processes, and analyze data to make informed decisions.

Zoho CRM offers lead management, contact management, sales pipeline management, email marketing, and reports and analytics.

Pros and Cons

Pricing

• Free: No cost for three users
• Standard: $14/month per user billed annually or $20 per user billed monthly
• Professional: $23/month per user billed annually or $35 per user billed monthly
• Enterprise: $40/month per user billed annually or $50 per user billed monthly
• Ultimate: $52/month per user billed annually or $65 per user billed monthly

Features

• Salesforce automation
• Workflow automation
• Contact management
• Process management
• Performance management
• Marketing automation

ActiveCampaign: Best for Marketing Automation

Overall rating 4.1/5

With a high score of 4.8 for the feature set, ActiveCampaign provides users with a wide range of capabilities to meet their marketing needs. The platform also earns a strong rating of 4.5 for ease of use, making it accessible to users of all levels of expertise. On the downside, the platform receives a lower score of 2.8 for cost, indicating that it may be a more expensive option for some businesses.

ActiveCampaign offers email marketing, CRM, and automated messaging tools to help businesses create personalized experiences for their customers. It allows businesses to create targeted campaigns, automate repetitive tasks, and track customer interactions.

The platform supports email marketing, SMS, social media advertising, and more, allowing businesses to engage with their audience on multiple channels and touch points. It also provides detailed analytics and reporting features that help businesses track campaign performance, understand customer behavior, and make data-backed decisions to optimize their marketing activities.

Pros and Cons

Pricing

Marketing: Email & Marketing Automation

• Lite: Starts at $29/month billed annually or $39/month billed monthly (1 user)
• Plus: Starts at $49/month billed annually or $70/month billed monthly (3 users)
• Professional: Starts at $149/month billed annually or $187/month billed monthly (5 users)
• Enterprise: Starts at $259/month billed annually or $323/month billed monthly (10 users)

Sales: CRM & Sales Engagement

• Plus: Starts at $19/month billed annually or $23/month billed monthly
• Professional: Starts at $49/month billed annually or $59/month billed monthly
• Enterprise: Starts at $99/month billed annually or $119/month billed monthly

Bundles: Marketing Automation + Sales CRM

• Plus: Starts at $93/month billed annually or $116/month billed monthly (5 users)
• Professional: Starts at $386/month billed annually or $482/month billed monthly (10 users)
• Enterprise: Starts at $734/month billed annually or $918/month billed monthly (15 users)

Features

• Email marketing and marketing automation
• Sales automation
• Sentiment analysis
• Win probability

Hootsuite: Best for Social Media Management

Overall rating 4.1/5

Businesses and social media professionals commonly use Hootsuite to streamline their social media marketing efforts and maintain a consistent online presence. Hootsuite allows users to manage and schedule posts across various social media platforms like Facebook, Twitter, Instagram, LinkedIn, and YouTube.

It also provides features for monitoring social media activity, engaging with followers, and analyzing social media performance through data and analytics. Aside from allowing users to create and schedule posts in advance, it also shows the best time to post suggestions based on data analysis, tracks engagement metrics such as likes, comments, and shares, and provides insights to help optimize social media strategies.

One of Hootsuite’s standout features is its stellar support (5), providing users with the assistance they need to maximize the platform’s capabilities. This high rating underscores the company’s commitment to customer satisfaction and ensuring users have the guidance and resources to succeed in their social media marketing efforts.

Pros and Cons

Pricing

• A 30-day free trial is available
• Professional: $99 per month, billed annually. Limited to 1 user
• Team: $249 per month, billed annually. Support up to 3 users
• Enterprise custom: Custom quote

Features

• Publish and schedule
• Social media analytics
• AI content creation
• Best times to post
• Social advertising

SS&C Blue Prism: Best for Automating Rule-Based Tasks

Overall rating 3.9/5

SS&C Blue Prism earned a moderate score for features and quality of features, a perfect score for ease of use and support, and a poor score for costs, as it lacks pricing visibility. The lack of pricing transparency makes it challenging for potential users to assess the software’s value proposition against their financial resources.

SS&C Blue Prism provides intelligent and robotic process automation (RPA) software solutions. Their tool enables organizations to automate repetitive, rule-based tasks to increase efficiency, productivity, and accuracy. Blue Prism’s RPA technology uses software robots to mimic human actions, interacting with applications, systems, and data to perform different tasks. This helps organizations reduce errors and free employees to focus on higher-value work.

Pros and Cons

Pricing

A 30-day free trial is available. For your actual pricing, contact the company for a custom quote.

Features

• Process orchestration and automation
• Document automation
• Intelligent automation
• Robotic process automation
• Business process management
• It offers process automation, analytics, monitoring, and management capabilities to help organizations automate complex business processes

Zapier: Best for Integration Automation

Overall rating 3.7/5

Zapier is a web automation tool that allows users to easily connect different apps and automate workflows without any coding knowledge. It works by creating “Zaps,” automated workflows connecting two or more apps to automate tasks. Zapier supports thousands of apps and services, making it a versatile tool for streamlining processes and increasing productivity.

Businesses of all sizes can benefit from using Zapier as a business automation tool due to its extensive integration and automation capabilities, allowing for seamless connectivity between different apps and systems.

While Zapier excels in ease of use and support, it falls slightly short in the quality of its feature set, with a score of 2.5 out of 5. This is due to limitations in AI capabilities and assistance in CRM management.

Pros and Cons

Pricing

Note that the actual amount you will pay depends on the number of tasks you need to perform monthly.

• Free: No cost for one user
• Professional: Starts at $19.99/month billed annually or $29.99/month billed monthly
• Team: Starts at $69/month billed annually or $103.50/month billed monthly
• Enterprise: Custom quote

Features

• AI power-ups
• Visual no-code editor
• Zap version control
• Unlimited app integrations

Workato: Best for Intelligent Integration and Workflow Automation

Overall rating 3.6/5

Workato is an enterprise automation platform that allows organizations to automate workflows and integrate data across various applications. It allows businesses to connect their apps, automate workflows, and optimize business processes. Workato offers over 1,200 pre-built connectors to popular business applications such as Salesforce, Slack, and ServiceNow, allowing for straightforward integration and automation of tasks.

The platform scores high marks in feature set and quality, thanks to its extensive connectors and functionalities, which cater to a wide range of business processes. However, Workato falls short on cost, with a score of 0. The platform lacks pricing transparency and may not be the most cost-effective solution for organizations with limited budgets.

Workato can accelerate various organizational functions, including IT operations, marketing, HR, finance, sales, and more, by automating tasks such as ticket escalation, threat detection and resolution, compliance management, lead management, payroll and benefits processing, sales forecasting, and integration processes.

Pros and Cons

Pricing

Contact the company for a custom quote.

Features

• Several automation features: process, task, experience, AI workflows, and insights
• Several integration capabilities, including app, data, API, event streams, and LLMs
• ​​AI-driven knowledge bot

How to Choose the Best Business Automation Software for Your Business

When choosing the best business automation software, carefully consider your specific needs. Assess your current business processes and identify areas that could benefit from automation. Look for software that offers features and functionalities that align with these needs, such as workflow automation, task management, data integration, and reporting capabilities.

Our review analyzed various software that fit the needs of different user categories to help you determine the best option for your business. Key factors to consider are ease of use, affordability, feature set, and customer support. Opt for intuitive and user-friendly software, as this will encourage adoption and maximize the benefits of automation. Evaluate the pricing structure to ensure it aligns with your budget and offers good value for the features provided.

How We Evaluated the Best Business Automation Software

We evaluated the best business automation software based on the following criteria:

Feature set – 30%

We allocated 30% of the evaluation criteria to the available features of the software. We looked at the range of automation features offered by each software. Software with a comprehensive set of features scored higher in this category.

Ease of use – 25%

We consider the software user-friendly and intuitive for both beginners and advanced users. Software with a simple and easy-to-navigate interface received higher scores in this category.

Cost – 20%

20% of the evaluation criteria was allocated to the pricing structure of the software. This includes the overall affordability, transparency of pricing, and value for money.

Support – 15%

We examined the quality of customer support services provided by the software vendors, including response times, resource availability, and user reviews.

Quality of feature set – 10%

10% of our evaluation criteria was allocated to the overall quality and innovation of the feature set. This includes advanced capabilities such as CRM integration, AI implementation, and predictive analytics.

Frequently Asked Questions (FAQs)

What is a business automation tool?

A business automation tool is software that helps automate various business processes and tasks. These tools can automate repetitive tasks, manage workflows, improve efficiency, and reduce human errors in business operations.

How can a business automation tool benefit my organization?

• Increased productivity
• Improved efficiency
• Reduced operational costs
• Better accuracy in tasks
• Enhanced communication and collaboration
• Increased overall business agility and competitiveness

What are some common features of business automation tools?

Standard features of business automation tools include workflow automation, task automation, document management, data integration, reporting and analytics, scheduling and reminders, collaboration tools, CRM integration, and customizable templates for various business processes.

Bottom Line: Automation Software Moves Business Forward

By leveraging automation tools effectively, businesses can free up valuable time and resources, allowing employees to focus on strategic and high-value tasks. The key to success is identifying the best software that aligns with your business objectives and workflow processes – and making sure that your chosen solution can scale with time.

Whether automating sales and marketing processes, managing customer relationships, or optimizing supply chain operations, investing in the right business automation software can be a game-changer for organizations looking to drive growth.

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 9 Best Business Automation Software in 2024 appeared first on eWEEK.

To accomplish this, NLP tools leverage machine learning algorithms, linguistic rules, and statistical techniques. NLP’s ability to understand human language is enabling AI to advance at an exponentially faster pace. These tools offer significant competitive advantage to those companies that effectively use them.

This review of the best NLP software analyzed the eight top-rated tools for various users and organizations. We evaluated each tool’s features, pros and cons, and pricing. See our top picks below and read to the end to find out which NLP software is best for your business.

• Gensim: Best for Budget-Conscious NLP Projects
• spaCy: Best for Lightning-Fast Text Processing NLP Tasks
• IBM Watson: Best for Advanced Text Analytics
• Natural Language Toolkit: Best for Educators and Researchers
• MonkeyLearn: Best for Ease of Use and Performing Text Analysis
• TextBlob: Best for Initial Prototyping in NLP Projects
• Stanford Core NLP: Best for Executing Complex NLP Tasks
• Google Cloud Natural Language API: Best for Extracting Insights from Text Data

Top Natural Language Processing Software Comparison

Gensim: Best for Budget-Conscious NLP Projects

Gensim is an open-source Python library – so it can be used free of charge – for natural language processing tasks such as document indexing, similarity retrieval, and unsupervised semantic modeling. It is commonly used for analyzing plain text to uncover the semantic structure within documents. The solution provides algorithms and tools for implementing various machine learning models, such as Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and word2vec.

Gensim also offers pre-trained models for word embeddings, which can be used for tasks like semantic similarity, document classification, and clustering. Our research found that Gensim can process large text collections using incremental online algorithms without requiring all the text data to be stored in memory, making it suitable for analyzing extensive web-based text datasets.

Pros and Cons

Pricing

Gensim is free, open-source software published under the GNU Lesser General Public License (LGPL).

Features

• Data streaming
• Similarity retrieval
• The software runs on Linux, Windows, OS X, and any other platform that supports Python 3.6+ and NumPy
• Gensim allows users to efficiently index and store large collections of text documents for easy retrieval and analysis

spaCy: Best for Advanced NLP Tasks

spaCy stands out for its speed and efficiency in text processing, making it a top choice for large-scale NLP tasks. Its pre-trained models can perform various NLP tasks out of the box, including tokenization, part-of-speech tagging, and dependency parsing. Its ease of use and streamlined API make it a popular choice among developers and researchers working on NLP projects.

SpaCy’s support for over 75 languages and 84 trained pipelines for 25 languages makes it a versatile tool for working with text in different languages. It uses multi-task learning with pre-trained transformers like BERT, allowing users to leverage state-of-the-art models for various NLP tasks.

Pros and Cons

Pricing

SpaCy is a free, open-source library that can be used for both personal and commercial projects.

Features

• Sentiment analysis
• Dependency parsing
• Word vectors
• Components for named entity recognition, part-of-speech tagging, sentence segmentation, text classification, lemmatization, morphological analysis, and entity linking

IBM Watson: Best for Advanced Text Analytics

IBM Watson Natural Language Understanding (NLU) is a cloud-based platform that uses advanced artificial intelligence and natural language processing techniques to analyze and understand text data. It can extract critical information from unstructured text, such as entities, keywords, sentiment, emotion, and categories. IBM Watson NLU can be used in various applications, such as social media monitoring, customer feedback analysis, and content categorization.

IBM Watson NLU is well-suited for organizations that require advanced text analytics capabilities to enhance their decision-making processes and gain a deeper understanding of customer feedback, market trends, and other textual data sources.

Pros and Cons

Pricing

• Lite: The Lite plan includes 30,000 NLU items and one custom model per month. If you exceed this limit, your NLU instance will be temporarily suspended until the start of the following month.
• Standard: The Standard Plan includes support for over 5 million items per month, with pricing starting at $0.003 per item. This version also offers unlimited custom entities and relations models trained with WKS for $800 and custom classification models for $25.

Features

• Text analytics
• Domain customization
• Entity detection — detects people, places, events, and other entities mentioned in your content
• Sentiment analysis
• Emotion extractions

Natural Language Toolkit: Best for Educators and Researchers

The Natural Language Toolkit (NLTK) is a Python library for NLP tasks. It includes modules for functions such as tokenization, part-of-speech tagging, parsing, and named entity recognition. NLTK also provides access to various corpora (over 50) and lexicons for use in natural language processing projects.

NLTK is widely used in academia and industry for research, education, and NLP application building, and so have major community support. It offers a wide range of functionality for processing and analyzing text data, making it a valuable resource for those working on tasks such as sentiment analysis, text classification, machine translation, and more.

Pros and Cons

Pricing

NLTK is a free, open-source, community-driven project.

Features

• NLTK is available for Windows, Mac OS X, and Linux
• It allows you to tokenize and tag some text
• WordNet integration
• Named entity recognition

MonkeyLearn: Best for Ease of Use in Text Analysis

MonkeyLearn is an ML platform that offers a wide range of text analysis tools for businesses and individuals. With MonkeyLearn, users can build, train, and deploy custom text analysis models to extract insights from their data. The platform provides pre-trained models for everyday text analysis tasks such as sentiment analysis, entity recognition, and keyword extraction, as well as the ability to create custom models tailored to specific needs.

Using MonkeyLearn’s APIs, you can integrate MonkeyLearn with various third-party applications, such as Zapier, Excel, and Zendesk, or your platform.

Pros and Cons

Pricing

Although MonkeyLearn doesn’t publicly advertise its rates on its website, publicly available information shows that the MonkeyLearn API costs $299/month, including 10k queries/month. For your actual rate, contact the company for a custom quote.

Features

• Text classifiers.
• Text extractors.
• Data visualization capability.
• CSAT analysis.
• Pre-built and custom machine learning models.

TextBlob: Best for Initial Prototyping in NLP Projects

TextBlob is a Python library that provides an intuitive interface for performing everyday NLP tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. It is built on top of NLTK and Pattern, two widely used NLP libraries in Python.

While it is excellent for initial prototyping in NLP projects, its lower performance inherited from NLTK may make it unsuitable for enterprise large-scale production use. On the other hand, its ease of use is certainly a major positive for TextBlob: there is an enormous amount of new AI projects being launched by beginners (or near beginners) so TextBlob’s user base will likely grow.

Pros and Cons

Pricing

TextBlob is a free, open-source library in Python.

Features

• Part-of-speech tagging
• Sentiment analysis
• Classification (Naive Bayes, decision tree)
• Tokenization
• Noun phrase extraction

Stanford Core NLP: Best for Information Extraction in NLP Tasks

Stanford CoreNLP is written in Java and can analyze text in various programming languages, meaning it’s available to a wide array of developers. Indeed, it’s a popular choice for developers working on projects that involve complex processing and understanding natural language text.

CoreNLP provides a set of natural language analysis tools that can give detailed information about the text, such as part-of-speech tagging, named entity recognition, sentiment and text analysis, parsing, dependency and constituency parses, and coreference. Its scalability and speed optimization stand out, making it suitable for complex tasks.

Pros and Cons

Pricing

Stanford CoreNLP is a free, open-source tool.

Features

• Dependency parsing
• Tokenization
• Part-of-speech tagging
• Sentiment analysis
• Constituency parsing
• Multilingual Support — up to 8 languages

Google Cloud Natural Language API: Best for Extracting Insights from Text Data

Google Cloud Natural Language API is a service provided by Google that helps developers extract insights from unstructured text using machine learning algorithms. The API can analyze text for sentiment, entities, and syntax and categorize content into different categories. It also provides entity recognition, sentiment analysis, content classification, and syntax analysis tools.

It allows users to build custom ML models using AutoML Natural Language. This API leverages Google’s advanced question-answering and language-understanding technology to help with natural language processing tasks.

Pros and Cons

Pricing

Monthly prices per 1,000-character unit:

Features

• Entity sentiment analysis
• Content classification
• Content targeting and discovery
• Custom content classification

How to Choose the Best Natural Language Processing Software for Your Business

When shopping for the right natural language processing tool for your needs, consider the specific tasks you want to accomplish with the tool, the complexity of the language data you are working with, the accuracy and performance required, and your budget and technical expertise. And of course this includes your future plans for using AI: you’ll need a NLP solution that not only handles current needs but also scales with time.

Key technical features to consider before making a decision include:

• Natural language understanding
• Text processing and analysis
• Machine learning and deep learning techniques
• Multilingual support
• Scalability and performance
• Integration and APIs
• Accuracy and reliability

Frequently Asked Questions (FAQs)

How Do Natural Language Processing Tools Work?

Natural language processing tools use algorithms and linguistic rules to analyze and interpret human language. These tools can extract meanings, sentiments, and patterns from text data and can be used for language translation, chatbots, and text summarization tasks.

What Are the Applications of Natural Language Processing Tools?

• Sentiment analysis
• Information retrieval
• Speech recognition
• Chatbots
• Machine translation
• Text classification
• Text summarization

What Is the Best NLP Software?

There’s no best natural language processing (NLP) software, as the effectiveness of a tool can vary depending on the specific use case and requirements. Our review captured the best tool for different users and use cases. Generally speaking, an enterprise business user will need a far more robust NLP solution than an academic researcher.

Bottom Line: Natural Language Processing Software Drives AI

In recent years (since the end of 2022, precisely), NLP has become a core part of modern AI, machine learning and other business applications – even existing legacy apps are integrating NLP capabilities into their workflows. Incorporating the best NLP software into your workflows will help you maximize several NLP capabilities, including automation, data extraction, and sentiment analysis.

Investing in the best NLP software can help your business streamline processes, gain insights from unstructured data, and improve customer experiences. Take the time to research and evaluate different options to find the right fit for your organization. Ultimately, the success of your AI strategy will greatly depend on your NLP solution.

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 8 Best Natural Language Processing Tools 2024 appeared first on eWEEK.

By retrieving relevant data or documents before generating a response, RAG improves the generated text’s accuracy, reliability, and informativeness. This approach helps ground the generated content in external sources of information, ensuring that the output is more contextually relevant and factually accurate.

Read on to learn more about RAG, how it works, its use cases, and how it differs from the traditional process of natural language processing (NLP).

What Exactly is Retrieval-Augmented Generation (RAG)?

You’ve probably heard people say that AI-generated content is susceptible to plagiarism and lack of originality. In traditional natural language processing tasks, language models generate responses based solely on patterns and information in their training data. While this approach has shown impressive results, it also comes with limitations, such as the potential for generating incorrect or biased output, especially when dealing with complex or ambiguous queries.

Retrieval-augmented generation is a technique that addresses this issue by combining the power of both natural language processing and information retrieval.

Imagine trying to write a research paper without access to the Internet or any external resources. You may have a general understanding of the topic, but to support your arguments and provide in-depth analysis, you need to consult various sources of information.

This is where RAG comes in — it acts as your research assistant, helping you access and integrate relevant information to enhance the quality and depth of your work.

Large language models (LLMs) are trained on vast volumes of data. They are like well-read individuals who have a broad understanding of various topics and subjects. They can provide general information and answer various queries based on their vast knowledge-base. But to generate more precise, reliable, and detailed responses backed up by specific evidence or examples, LLMs often need the assistance of RAG techniques. This is similar to how even the most knowledgeable person may need to consult references or sources to provide thorough and accurate responses in certain situations.

To gain a deeper understanding of today’s top large language models, read our guide to Best Large Language Models

How Retrieval-Augmented Generation Works in Practice

Retrieval-augmented generation (RAG) is a AI model architecture that combines the strengths of pre-trained parametric models (like transformer-based models) with non-parametric memory retrieval, enabling the generation of text conditioned on both the input prompt and external knowledge sources.

The workability of the RAG model starts from the user query or prompt. The retrieval model is activated when you type your questions into your generative AI text field.

Query Phase

In the query or prompt phase, the system searches a large knowledge source to find relevant information based on the input query or prompt. This knowledge source could be a collection of documents, a database, or any other structured or unstructured data repository. It could also be your company knowledge base.

For example, if the input query is “What are the symptoms of COVID-19?” the RAG system would search and retrieve relevant information from a database of medical documents or articles.

Retrieval

Once the relevant information is found, the RAG system selects a set of candidate passages or documents likely to contain useful information for generating a response. This step helps filter out irrelevant or redundant information and only picks the most relevant answer to your question.

In the COVID-19 example, the system might select passages from medical articles that list common symptoms associated with the disease.

Generation Phase

In the generation phase, the result is returned to the user. RAG uses the selected candidate passages as context to generate a response or text.

This generation process can be based on various techniques, such as neural language models (e.g., GPT) or other generation architectures. The generated response should be coherent, relevant, and informative based on the input query and the retrieved context.

3 Key Benefits of Retrieval-Augmented Generation

Reduction of Response Bias

RAG systems can mitigate the effects of bias inherent in any single dataset or knowledge repository by retrieving information from diverse sources. This helps provide more balanced and objective responses as the system considers a broader range of perspectives and viewpoints. By promoting inclusivity and diversity in the retrieved content, RAG models create fairer and more equitable interactions.

Reduced Risk of Hallucinations

Hallucinations refer to the generation of incorrect or nonsensical information by large language models. RAG systems mitigate this risk by incorporating real-world information retrieved from external knowledge sources.

By retrieving and grounding responses in verified, external information, RAG models are less likely to generate hallucinatory content. This reliance on external context helps ensure that the generated responses are grounded in reality and aligned with factual information, reducing the likelihood of producing inaccurate or misleading output.

Improved Response Quality

The RAG technique can generate relevant, fluent, and coherent responses by combining retrieval and generation techniques, leading to higher-quality outputs than purely generative-based approaches. Clearly, even the best LLM has its limitations – RAG is the technology needed to add a deeper knowledge base.

When to Use RAG vs. Fine-Tuning an AI Model

This chart summarizes the considerations for choosing between RAG and fine-tuning an AI model based on various aspects.

How is Retrieval-Augmented Generation Being Used Today?

Question Answering Systems

RAG models are used in question answering systems to provide more accurate and context-aware responses to user queries. These systems can be deployed in customer support chatbots, virtual AI assistants, and search engines to deliver relevant information to users in natural language.

Search Augmentation

RAG can enhance traditional search engines by providing more contextually relevant results. Instead of simply matching keywords, it retrieves relevant passages from a larger database and generates responses that are more tailored to the user’s query.

Knowledge Engines

RAG can power knowledge engines where users can ask questions in natural language and receive well-informed responses. This is particularly useful in domains with a large amount of structured or unstructured data, such as healthcare, law, finance, or scientific research.

RAG vs. Traditional Approaches

Traditional question and answer approaches rely heavily on keyword matching for information retrieval, which can lead to limitations in accurately understanding user queries and providing relevant results.

In contrast, RAG offers a more advanced and contextually aware approach to information retrieval. Instead of relying solely on keyword matching, RAG leverages a combination of techniques, including natural language understanding and machine learning, to comprehend the semantics and context of user queries. This allows RAG to provide more accurate and relevant results by understanding the intent behind the query rather than just matching keywords.

Bottom Line: Embracing the Potential of RAG

Retrieval-augmented generation holds significant promise for transforming various aspects of natural language processing and text generation tasks. By incorporating the strengths of retrieval-based and generation-based models, RAG can improve the quality, coherence, and relevance of generated text.

Embracing RAG’s potential can lead to more effective and human-like interactions with AI systems, better question answering systems, and enhanced content creation capabilities. This approach can also help address common AI challenges by generating more diverse and informative responses, reducing biases in generated text, and improving the overall performance of language models.

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

The post What is Retrieval-Augmented Generation? How it Works & Use Cases appeared first on eWEEK.

These AI social media apps help automate content creation, scheduling, monitoring, engagement, and audience-targeting tasks. Some AI tools even offer advanced capabilities that enable analyzing data, identifying trends, making data-driven recommendations, and helping businesses better understand and reach their target audience on social media platforms.

Our review of the top AI social media software evaluated several platforms and selected the best options to help you discover the right tool for your company.

Top AI Social Media Software Comparison

See how the best AI social media software compare in terms of pricing, the segment they serve, and their features.

HubSpot: Best for Social Media ROI Reporting

Overall rating: 4.5

HubSpot is a provider of inbound marketing and sales software. It helps businesses attract visitors, convert leads, and close customers. It offers various marketing, sales, customer service, and CRM tools. HubSpot’s platform includes email marketing, social media management, website analytics, lead nurturing, and more.

HubSpot has an artificial intelligence social media post generator that helps businesses create, refine, and automate their social media posts. With this generative AI tool, you can overcome writer’s block, generate more leads, and save time by having the AI assist with the imagination and creation of social media content for platforms like Facebook, Instagram, LinkedIn, and X. The AI social media post generator not only helps you rewrite but also expand upon your initial post ideas, ensuring the content is engaging, relevant, and tailored to your brand’s tone of voice.

HubSpot’s AI social media post generator is great for marketers who want to analyze the performance of their social media posts and track their return on investment. It provides easy-to-use reporting capabilities that allow users to monitor and assess the engagement metrics of their AI-assisted posts on various social media platforms.

Pros and Cons

Pricing

• Free Tools: Free for up to 5 users.
• Starter: $15 per month per seat when paid upfront, or $20 per month per seat when paid monthly.
• Professional: Starts at $800 per month when paid upfront. It includes three seats and additional seats cost $45 per month. When paid monthly, it costs $890 per month. It includes three seats and additional seats cost $50 per month.
• Enterprise: Starts at $3,600 per month. It includes five seats and additional seats cost $75 per month.

Features

• SEO recommendations and optimizations.
• Reporting dashboard.
• Facebook Messenger and WhatsApp integration.
• Multi-language content creation.
• A/B testing.

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

ContentStudio: Best for Post Scheduling

Overall rating: 4.5/5

ContentStudio helps creators manage and publish content across various digital channels, such as websites, social media, and email marketing. It typically provides AI tools for creating, scheduling, optimizing, and analyzing content to help businesses enhance their online presence and engage with their audience.

From AI-generated captions and images to hashtag suggestions and emoji integration, ContentStudio offers a range of tools to enhance your social media presence. Additionally, you can manage your social campaigns, schedule posts, and analyze performance all in one place.

ContentStudio’s hands-free scheduling feature helps you plan, create, and schedule posts automatically published to the proper channels at the right time.

Pros and Cons

Pricing

A 14-day free trial is available.

• Starter: $20 per month when billed annually or $25 per month when billed monthly. One user only.
• Pro: $40 per month when billed annually or $49 per month when billed monthly. Two users only.
• Agency small: $80 per month when billed annually or $99 per month when billed monthly. Five users only.
• Agency medium: $160 per month when billed annually or $199 per month when billed monthly. 10 users only.
• Agency large: $240 per month when billed annually or $299 per month when billed monthly. 20 users only.

Features

• Supported social networks include X (formerly Twitter), Facebook, Instagram, TikTok, Pinterest, LinkedIn, YouTube, and more
• Publishing to blogs
• Planning and collaboration capabilities
• Analytics and reporting
• Content and influencer discovery
• AI assistant
• AI caption generator

Hootsuite: Best for Brand Social Listening

Overall rating: 4.3/5

Hootsuite is a social media management platform that allows users to manage all their accounts from one centralized dashboard. With Hootsuite, you can schedule posts, engage with your audience, monitor analytics, and run social media advertising campaigns. It also offers features for social listening, collaboration among team members, and reporting on social media performance.

With Hootsuite’s AI-powered real-time monitoring and social listening capabilities, you can track mentions of your brand, company, products, and competitors to stay informed and respond effectively. The tool provides crisis management capability, including automatic alerts and response planning, to help you navigate any unexpected situations or spikes in social activity.

The platform supports Instagram (business and personal profiles), Twitter, Pinterest, Facebook (profiles, pages, and groups), LinkedIn (profiles and pages), and YouTube channels.

Pros and Cons

Pricing

A 30-day free trial is available.

• Professional: $99 per month — one user only
• Team: $249 per month for up to 3 users
• Enterprise custom: Custom quote. Starts with five users

Features

• LinkedIn page target audience
• Content discovery streams
• Asset and content library
• Team productivity reporting
• Bulk scheduling

Synthesia: Best for Creating Video Content

Overall rating: 4.2/5

Synthesia is an AI video generation platform designed to help you create realistic videos from text input. It can be used for various applications, such as making personalized videos, virtual avatars, and digital storytelling. It can also be used to create marketing videos, e-learning content, and visual content for social media.

You can create videos by inputting your script, customizing the visuals, and generating the video. The platform offers over 160 AI avatars and 120 languages for voiceovers. Synthesia allows users to collaborate with their team by sharing the video for feedback and then either downloading the video in .mp4 format, getting a shareable link, or using an embed code to share it.

Pros and Cons

Pricing

• Starter: $22 per month, billed annually, or $29 monthly
• Creator: $67 per month, billed annually, or $89 monthly
• Enterprise: Custom quote

Features

• Supports 120 languages plus voices
• Offers 140+ AI avatars
• Auto-generated closed captions
• Lets you create professional-looking product explainers

Sprout Social: Best for Performance Analytics

Overall rating: 4.1/5

Sprout Social is commonly used by marketing and social media teams to streamline their social media marketing efforts. It allows them to schedule posts, track performance metrics, monitor mentions, and engage with their audience across various social media platforms.

Sprout Social incorporates AI and automation features to help businesses streamline their social media management tasks. These capabilities include sentiment and textual analysis, which can help identify trends and patterns in social media conversations. Sprout’s integration with OpenAI enables the automation of manual tasks, allowing teams to focus on high-impact work that requires human decision-making.

Sprout Social stands out for its robust performance analytics capabilities. It provides detailed insights into the performance of your social media campaigns, helping you track key metrics such as engagement, reach, and conversion rates.

Pros and Cons

Pricing

A 30-day free trial is available.

• Standard: $199 per seat/month billed annually or $249 per seat/month billed monthly.
• Professional: $299 per seat/month billed annually or $399 per seat/month billed monthly.
• Advanced: $399 per seat/month billed annually or $499 per seat/month billed monthly.
• Enterprise: Custom quote.

Features

• Social reporting for Facebook, Instagram, X, and LinkedIn
• Digital asset and content library
• Support influencer marketing
• Sentiment in the smart inbox and reviews
• Provides competitive reports for Instagram, Facebook, and X

Jasper AI: Best for AI-Assisted Content Generation

Overall rating: 4/5

If you need an advanced tool to generate AI-assisted social media posts, Jasper AI could be a suitable solution. Jasper AI is an AI-powered content creation platform that helps businesses and individuals generate high-quality written content. It uses natural language processing (NLP) technology to assist in creating blog posts, articles, social media posts, and other written content.

Jasper AI offers AI-charged social media tools that can help you create captions, repurpose content at scale, generate assets for marketing campaigns, remix content, access over 50 templates for various skills like writing headlines and blog posts, and utilize an AI chatbot for tasks, research, and answering questions through conversational interfaces.

Pros and Cons

Pricing

A 7-day free trial is available.

• Creator: $39 per month/seat billed annually or $49 per month/seat billed monthly
• Pro: $59 per month/seat billed annually or $69 per month/seat billed monthly
• Business: Custom quote

Features

• Support over 30 languages
• Company knowledge hub
• Document collaboration
• Includes plagiarism checker
• Offers 50+ templates

Flick: Best for Instagram Users

Overall rating: 3.7/5

While Flick supports and works with various social media platforms, it is primarily known as a powerful tool for Instagram users.

From data analytics and hashtag performance tracking to audience insights and content scheduling, Flick provides users with the resources to improve their social media strategy and engagement. The tool offers advanced hashtag research and management tools, including hashtag collections, suggested hashtags, hashtag search, and smart auditing. You can access hashtag metrics, analyze hashtag performance, and use the randomizer feature to discover new hashtags for your content.

In terms of scheduling, Flick supports Instagram (Posts & Reels), Facebook, TikTok, and LinkedIn, allowing you to plan and publish content across multiple platforms from one centralized dashboard. Flick provides insights into the best times to post on Instagram, a feed preview for visualizing how posts will appear on the platform, and the ability to create draft posts for future scheduling.

Pros and Cons

Pricing

A 7-day free trial is available.

• Solo: £11 per month when billed yearly or £14 per month when billed month-to-month (limited to one user)
• Pro: £24 per month when billed yearly or £30 per month when billed month-to-month (limited to two users)
• Agency: £55 per month when billed yearly or £68 per month when billed month-to-month (up to 20 users)

Features

• Content scheduling
• Offers analytics tools to track account performance, content engagement (including Feed, Stories, and Reels), audience activity, and hashtag performance for Instagram users
• The media library feature enables you to store and organize your visual assets for easy access when creating posts

Upgrow: Best for Growing Instagram Account

Overall rating: 3.7/5

UpGrow is an AI-powered social media growth service that automatically helps users gain real Instagram followers. It uses AI models, paid advertising, micro-interactions, and real-human engagement to attract and engage followers to your Instagram account. UpGrow uses advanced AI algorithms and data-driven insights to target the right audience, optimize profiles, and create engaging content to attract followers.

UpGrow also offers features like location targeting, niche and interest targeting, age and gender filters, language targeting, and a viral content library to help users enhance their Instagram presence.

Pros and cons

Pricing

• Basic Plan: $59 per month for one year or $69 per month for the first month
• Pro Plan: $99 per month for one year or $99 per month for the first month
• Turbo Plan: $179 per month for one year or $199 per month for the first month

Features

• The monthly follower count depends on your subscription plan
• Real-time analytics
• Language targeting
• Smart auto-targeting
• AI profile optimization
• Niche and interest targeting

Circleboom: Best for Managing X Accounts

Overall rating: 4.5/5

Circleboom is a social media management AI tool that helps users manage their X accounts by providing features such as scheduling posts, monitoring analytics, and managing followers. It also offers tools for finding new followers, cleaning up inactive accounts, and analyzing engagement on X.

Circleboom primarily focuses on managing X accounts, providing analytics, follower insights, and tools to find and eliminate fake or inactive accounts, and helping users find new peers on the platform. Circleboom Publish allows users to schedule and publish content across various social media platforms, including Instagram, Facebook, LinkedIn, and Google My Business.

Pros and Cons

Pricing

Twitter (X) management:

• Free forever: No cost for one profile
• Limited Plan: $9.99 per month billed annually or $27.99 month-to-month
• Pro: $16.99 per month billed annually or $35.99 month-to-month
• Plus: $23.99 per month billed annually or $45.99 month-to-month
• Premium: $29.99 per month billed annually or $55.99 month-to-month

Overall social media publishing:

• Pro: $24.99 per month billed annually or $29.99 month-to-month
• Premium: $34.99 per month billed annually or $44.99 month-to-month
• Business: $79.99 per month billed annually or $89.99 month-to-month
• Enterprise: $209.99 per month billed annually or $249.99 month-to-month

Features

• Chat GPT-4 integration
• X followers insights
• Account analytics
• Advanced X search
• User analytics

Canva: Best for Creating Social Media Assets

Overall rating: 3.5/5

Canva is an online graphic design platform that allows you to create visual content, such as flyers, posters, social media posts, and presentations. It offers a user-friendly interface with pre-designed templates, drag-and-drop functionality, and a wide range of design elements and customization options.

Canva offers templates for popular social media platforms like Facebook, Instagram, X, and Pinterest. With these templates, you can easily create content for your social media profiles, such as posts, stories, cover photos, and ads.

Canva’s social media templates help you ensure your content meets each platform’s specific size and layout requirements, making it easier to create professional-looking posts. Canva offers a library of images, icons, fonts, and other design elements that can be used to customize social media graphics and make them eye-catching.

Pros and Cons

Pricing

• Canva free: No cost for one person
• Canva Pro: $119.99 per year for one person or $14.99 month-to-month for one person
• Canva for teams: $119.90 per year for one person or $11.99 month-to-month for one person (requires a minimum of two people)
• Canva enterprise: Custom quote

Features

• Generate on-brand copy with AI
• Offers over 100 million photos, videos, graphics, and audio
• Plan and schedule social content
• Provides over 20 AI-powered design tools
• Real-time collaboration
• Offers up to 1TB of cloud storage

How to Choose the Best AI Social Media Software for Your Business

To discover the best AI social media software for your business, you must first identify your business needs, as the right tool will significantly depend on your specific social media strategy.

Our analysis found that Canva is ideal for those looking for affordable, DIY software to create their digital assets. Synthesia is ideal for video generation, while Jasper can help you generate AI-assisted content. Those looking to manage and grow their social media followers should consider Upgrow, Flick, and Circleboom.

Note that some of these tools can be used in combination, meaning their capacity can be boosted further when used in tandem with other top AI social media tools.

How We Evaluated AI Social Media Software

To evaluate AI social media software, we looked at several key factors.

Feature set – 35%

The feature set of the AI social media software is crucial in determining its capabilities and effectiveness. Hence, it was given the highest weight of 35%. The key capabilities we considered include:

• Assists with problem solving
• Automates scheduling and publishing
• Processes data to understand audience response
• Uses AL/ML to predict performance

Pricing – 25%

Pricing was given a 25% weighting, as we wanted to ensure that the software was affordable and provided good value for its features.

Ease of use – 25%

Ease of use is another key aspect when evaluating AI social media software. We assessed the software’s user-friendliness, including the interface design, navigation, and customization options.

Support – 15%

Good customer support is essential when using any software, including AI social media software. We evaluated the level of support provided by the software vendor, including the availability of support channels, response times, and helpfulness of support staff.

AI Social Media Tools: Frequently Asked Questions (FAQs)

What are AI social media tools?

AI social media tools are AI-powered software built to automate and optimize social media marketing tasks and strategies. These tools can help businesses analyze data, plan and schedule posts, engage with followers, and optimize a business’s social media strategy.

AI social media tools can also help automate responses to comments and messages, track sentiment, and identify potential influencers or brand advocates.

Are AI tools capable of analyzing social media engagement?

Yes, AI tools are fully capable of analyzing social media engagement. They can track metrics such as likes, comments, shares, and followers and provide insights on user engagement, sentiment, and trends. AI tools can also analyze audience demographics and behaviors to help businesses better understand and target their audience.

What features do social media AI tools typically offer?

Some standard features of social media AI tools include sentiment analysis, influencer identification, automated posting and scheduling, audience segmentation, social listening, and performance analytics.

Bottom Line: AI Social Media Tools Are Reshaping Social Media 

The right AI software can transform the way businesses interact with their audience online, leading to increased engagement, brand loyalty, and success. Moreover, AI social media apps are reshaping social media by enabling users of these tools to become exponentially more effective at leveraging the power of social media.

Clearly, by leveraging the power of AI, organizations can greatly improve their social media reach and achieve far better results. AI social media software can help companies maintain consistent online presence, from creating personalized content to analyzing data and optimizing campaigns. Choosing the best AI social media software can give companies a competitive edge, drive engagement, and increase ROI.

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 10 Best AI Social Media Tools 2024 appeared first on eWEEK.

Furthermore, organizations will certainly continue to invest in generative AI in the years ahead. The reason? Generative AI uses large language models to generate highly realistic and convincing outputs that can mimic human creativity and intelligence to a remarkable extent.

This review of the best generative AI certifications compiled the top-rated and most recognized courses across various sectors to help you find the best learning and certification path.

Generative AI Certification Course Comparison

Generative AI for Data Scientists Specialization

Taught by four experts from IBM, the Generative AI for Data Scientists Specialization offered on Coursera is designed to help data professionals understand and implement generative AI in their data science projects. It is a three-course specialization that covers the basics of generative AI, prompt engineering concepts, tools, and techniques, and how to apply generative AI throughout the data science methodology.

The specialization includes hands-on projects and activities to reinforce learning and practical application of generative AI skills. Participants will learn how to use generative AI models for text, code, image, audio, and video generation, as well as data augmentation, feature engineering, and model development and refinement.

Why We Picked It

The specialization includes hands-on projects and activities that allow learners to apply the concepts they learn in real-world scenarios. This practical experience will help students solidify their understanding of generative AI and its applications.

Course Summary

Skills Acquired

• Artificial Intelligence.
• Data Science.
• Machine Learning.
• Prompt Engineering
• Generative AI.

NVIDIA-Certified Associate Generative AI LLMs

The NVIDIA-Certified Associate Generative AI LLMs (NCA Generative AI LLMs) certification is a foundational credential for individuals involved in developing, integrating, and maintaining AI-driven applications using generative AI and large language models (LLMs) with NVIDIA solutions. This certification validates the fundamental concepts required for working with generative AI and LLMs, making it suitable for professionals such as AI DevOps engineers, data scientists, machine learning engineers, software engineers, and more.

The exam covers essential topics, including machine learning fundamentals, neural networks, prompt engineering, data analysis, experimentation, software development, Python libraries for LLMs, integration, and deployment.

Why We Picked It

We selected the course because NVIDIA is a leading provider of AI solutions, and its certifications are highly regarded in the industry. Being certified by NVIDIA demonstrates your expertise in generative AI and LLMs, making you a valuable asset to potential employers.

Course Summary

Skills Acquired

By earning the NCA Generative AI LLMs certification, you will acquire a range of foundational skills and knowledge in the following areas:

• Fundamentals of ML and neural networks.
• Prompt engineering.
• Data analysis and visualization.
• Data preprocessing and feature engineering.
• Experiment design.
• Python libraries for LLMs.
• LLM integration and deployment.

Generative AI with Large Language Models

This course is a product of combined efforts between AWS and DeepLearning.ai, two prominent organizations in the fields of cloud computing and artificial intelligence. Through this collaboration, learners can benefit from the expertise and resources of AWS and DeepLearning.ai in the domain of generative AI with large language models.

In this course, learners will have the opportunity to dive into the latest research on Generative AI, particularly focusing on LLMs. You will gain foundational knowledge, practical skills, and a functional understanding of how LLMs work and how they can be deployed effectively in real-world applications.

​​Why We Picked It

Understanding how to train, fine-tune, and deploy LLMs is a crucial skill for AI developers. This course will provide you with the knowledge and tools needed to deploy generative AI models in various applications.

Course Summary

Skills Acquired

• Python programming.
• Machine Learning.
• Large Language Models.
• Generative AI.

Introduction to Generative AI

This introduction to generative AI course, offered by Google Cloud Training Instructors on Coursera, provides an overview of the fundamental concepts of generative AI. The one-module course is designed to span from the basics of generative AI to its applications. By the end of the course, learners will be able to define generative AI, explain how it works, understand different generative AI model types, and explore various applications of generative AI.

Why We Picked It

We chose this course because it offers a concise and informative introduction to generative AI. This course will teach you the basics of generative AI, how it works, and its applications. Whether you are a student, a professional looking to expand your skill set, or simply curious about the topic, this course can provide you with a solid foundation in generative AI.

Course Summary

Skills Acquired

Basic knowledge of generative AI and its real-world applications.

Generative AI: Implications and Opportunities for Business

This four-week course, offered by RMIT University via FutureLearn, is designed for business professionals who want to understand how generative AI technology shapes the future of business and the economy. By delving into the dynamics and implications of generative AI, participants will gain insights into how this technology can be used in various industries and the ethical and regulatory considerations that come with its adoption.

The course covers various topics, including the anatomy of generative AI models, the economic environment in which they operate, and the basic business models of generative AI applications.

Why We Picked It

With AI technology raising ethical and regulatory concerns, this course will help you understand the ethical implications of using generative AI in business. This knowledge can help you make informed decisions and navigate potential ethical dilemmas related to AI adoption.

Course Summary

Skills Acquired

• Understanding of generative AI.
• Business implications.
• Industry applications.
• Ethical and regulatory considerations.

Applied Generative AI for Digital Transformation

Business and technology leaders looking to delve into the expanding field of generative AI can benefit significantly from the Applied Generative AI for Digital Transformation course offered by MIT Professional Education. This intensive two-week program is designed to provide a deep understanding of Generative AI technologies and their practical applications across various organizational contexts.

Participants will explore the history and language of AI, specifically Generative AI, and learn how to employ it for domain-specific tasks such as video summarization, automated research, and AI conversational data insights.

Why We Picked It

Whether you are a senior leader, technology head, manager, or professional in innovation, sales or customer experience, this course will equip you with the necessary skills to leverage generative AI in your organization.

Course Summary

Skills Acquired

• Automation of workflows using Generative AI.
• Application of prompt engineering.
• Automation of workflows using Generative AI.
• Employing Generative AI in domain-specific tasks.
• Understanding and managing Generative AI’s organizational implications, both positive and negative.

Microsoft Azure AI Fundamentals: Generative AI

Azure AI Fundamentals Generative AI is a course introducing generative AI technology focusing on Microsoft Azure services. This course covers the fundamentals of generative AI, how large language models form the foundation of generative AI, the capabilities of Azure OpenAI Service, the efficiency gains provided by generative AI applications like copilots, the importance of fine-tuning prompts and responses, and how Microsoft’s responsible AI principles drive ethical AI advancements in the field of generative AI.

Why We Picked It

We chose this course because it covers fundamental concepts and practical applications of generative AI technology, focusing on Azure services. The course can help you learn how to leverage Azure OpenAI Service and grasp the importance of responsible AI principles in the field.

Course Summary

Skills Acquired

By taking the Azure AI Fundamentals: Generative AI course, you will acquire the following skills:

• Understanding of the fundamentals of generative AI technology.
• Knowledge of how large language models form the foundation of generative AI.
• Familiarity with Azure OpenAI Service and its capabilities.
• Ability to leverage generative AI applications like copilots for improved efficiency.
• Skills in fine-tuning prompts and responses for better outcomes.
• Awareness of ethical considerations and responsible AI principles in developing and deploying generative AI solutions.

Generative AI Overview for Project Managers

The “Generative AI Overview for Project Managers” is an online course offered by the Project Management Institute (PMI) as part of its professional development offerings. It is designed to give project managers a comprehensive understanding of how Generative AI can be applied in project management.

Through this course, project managers will learn the definitions of Artificial Intelligence (AI) and Generative AI and explore the various applications of Generative AI in project management. They will also earn 5 PDUs for taking the course (4 for Ways of Working and 1 for Business Acumen).

Why We Picked It

As a course offered by PMI, a globally recognized organization in project management, project managers can trust the quality and credibility of the content. We picked the course that caters to project managers, allowing them to enhance their understanding and application of Generative AI within the project management domain.

Course Summary

Skills Acquired

• Definitions of Artificial Intelligence (AI) and Generative AI.
• Understand Generative AI applications in project management.
• Discover the AI project manager tools that can be used to maximize efficiency.
• Learn best practices for project management in AI as a way of working.

Generative AI Business Sprint

The Generative AI Business Sprint offered by MIT Sloan School of Management Executive Education is a self-directed course designed to help participants understand and harness the transformational power of generative artificial intelligence in business. The course is structured around the sprint cycle approach used in agile product development. It allows participants to explore critical questions related to gen AI, such as its relevance to business, how it works, when to apply it, and where it is heading.

Why We Picked It

Participants are guided through creating a gen AI playbook, allowing them to apply what they have learned directly to real-world business scenarios. This hands-on approach ensures that participants can translate theoretical knowledge into actionable strategies.

Course Summary

Skills Acquired

• Understanding of Generative AI.
• AI strategy development.
• Critical thinking and analysis —participants will enhance their critical thinking and analytical skills through interactive exercises and real-world case studies.

Generative AI for Software Developers Specialization

Whether you’re already a seasoned developer or just starting, this specialization will help you enhance your programming capabilities by incorporating generative AI techniques into your projects.

Throughout this specialization’s three self-paced course series, you will learn the basics of generative AI, including its applications, models, and tools for generating text, code, images, audio, and video. You will also delve into prompt engineering, exploring approaches and tools such as Prompt Lab, Spellbook, and Dust to enhance your generative AI skills.

Why We Picked It

This specialization introduces you to popular generative AI models and tools used in software development, such as GitHub Co-pilot, Open AI ChatGPT, and Google Gemini. Working with these tools will teach you how to incorporate AI into your development workflow.

Course Summary

Skills Acquired

• Prompt Engineering.
• Prompt patterns
• Generative AI.

Generative AI for Business Leaders

The Generative AI for Business Leaders course by edX is designed to provide business leaders with a comprehensive understanding of generative artificial intelligence and how it can impact various industries. The course covers the basics of generative AI and its potential impacts on businesses. It also offers guidance on implementing AI strategies within an organization.

Why We Picked It

Choosing this course allows business leaders and teams to gain a foundational understanding of generative artificial intelligence. This course offers insights into the potential impacts of this technology on industries and helps develop internal policies and tools for successful implementation.

Course Summary

Skills Acquired

• Understanding of generative artificial intelligence.
• Insight into the impacts of generative AI on industries.
• Ability to develop internal policies and tools for AI implementation in organizations.

Generative AI Nanodegree Program

Udacity offers a Generative AI Nanodegree Program that provides comprehensive training in the field of Generative AI. This program covers various topics, including generative models, deep learning, transfer learning, training neural networks, prompt engineering, and ethical AI.

The program also delves into Large Language Models (LLMs), text generation, computer vision, and building generative AI solutions. Students will learn how to work with popular tools and frameworks such as PyTorch, OpenAI API, and LangChain, as well as develop practical skills in image processing, natural language processing, and model evaluation.

Why We Picked It

The program’s focus on building real-world applications, such as chatbots and image generation models, ensures that students gain hands-on experience and are well-prepared for industry roles in AI development.

Course Summary

Skills Acquired

• Generative AI fundamentals.
• LLMs & text generation.
• Computer vision and generative AI
• Building generative AI solutions.

Is A Generative AI Certification Course Worth The Investment?

If you are interested in building a career in AI, the answer is yes, an AI cert is well worth the time and money involved. Certifications can make you stand out to potential employers and demonstrate your commitment to continuous learning and development.

Or, if you are interested in learning more about generative AI and its applications in various industries, a certification course may be worth the investment. It can help you gain valuable skills and knowledge to apply to your current job or transition into a new career.

How To Choose The Right Generative AI Certification Course For You

The best generative AI certification course for you will depend on your current knowledge and experience with generative AI and your specific goals and interests. If you are new to generative AI, look for beginner-friendly courses that provide a solid foundation in the basics. If you are more experienced, consider more advanced courses that dive deeper into complex concepts and techniques.

Ensure that the course covers the topics and skills you are interested in learning. Also, consider taking a course from a reputable institution or organization that is well-known in AI. A certification from a recognized entity can boost your credibility and help you stand out to potential employers. Take into account your schedule and preferred learning style. Look for courses that offer flexible timing, online options, and self-paced learning and that are within your budget.

Top 3 Companies to Work For Post-Certification

After completing a generative AI certification, you may be interested in working for top companies that are leaders in AI and technology.

Google

Google is known for its innovative use of AI and machine learning in various products and services, such as Google Photos, Google Search, Google Assistant, and Gemini. Working at Google can allow you to work on advanced AI projects and develop your skills on the cutting edge of generative AI.

NVIDIA

NVIDIA is a leading technology company specializing in graphics processing units (GPUs) and AI computing. It is heavily involved in research and development in AI, including generative AI for applications such as image generation and style transfer. Working at NVIDIA can allow you to be included on advanced projects at a company that employs the world’s leading AI experts.

OpenAI

OpenAI is a research organization focused on artificial intelligence research and development. It is known for its work in creating advanced AI models, including generative models such as GPT-4, Dall-E, and more.

Bottom Line: Best Generative AI Certifications

​​Generative AI certification is a hot topic because generative AI itself is revolutionizing tech, as it gives you leverage to achieve uniqueness and creativity in AI. With many certification options available, the best generative AI certification offers a comprehensive curriculum, hands-on experience, and industry-recognized credentials that fits your needs.

Our review above outlines the top generative AI certification programs that meet these criteria so you can choose the best one for your career goals. Whichever program you choose, investing in a generative AI certification will undoubtedly enhance your skills and open up new opportunities for you.

For a full portrait of the AI vendors who hire generative AI experts, read our in-depth guide: 150+ Top AI Companies 2024

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