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Generative AI has quickly proven itself as a valuable asset to businesses’ workflows and operations. This is true whether a business uses ChatGPT Enterprise or another of the growing list of generative AI tools and apps.
Generative AI can support staffers in managing their existing task loads and, in some cases, these models can be trained to take on entirely new tasks and types of work. In all of these cases, generative AI is helping businesses streamline and automate their processes in repeatable and scalable ways that contribute to business growth goals.
Depending on your business priorities and requirements, generative AI can support your operations in a variety of ways. Read on to learn some of the most common ways generative AI is used to improve businesses today. Also important, learn about generative AI usage best practices that will help you achieve new levels of business success.
Table of Contents: How Generative AI Can Support Business Operations
- Top 7 Ways to Use Generative AI in Your Company
- 5 Benefits of Leveraging Generative AI in Your Business
- 5 Challenges of Using Generative AI
- What Are Generative AI Best Practices for Business Usage?
- Learning About Generative AI
- Bottom Line: How to Use Generative AI in Your Business
Top 7 Ways to Use Generative AI in Your Company
Generative AI models are being used for a variety of industry-specific enterprise use cases that range from simple customer service support and coaching to more complex tasks like medical imaging and drug discovery.
But generative AI can also be trained to handle more generic business tasks that are relevant to all businesses, regardless of industry or size. Below are seven of the most useful ways business leaders can incorporate generative AI into their business today:
1) Smart, Secure Data Analytics
Generative-AI-powered data analytics solutions make data analysis smarter, speedier, more scalable, and more secure. Depending on the model type and size you select, you can use generative AI to generate synthetic data for more secure and compliant data analysis and QA testing practices.
You can also use generative AI to democratize data analytics in two important ways:
- Generative AI models allow users to create narrative explanations of their data, making it easier for non-data scientists to understand the data they have in front of them.
- Generative AI assistants are being embedded and used in common data tools like Power BI, which enables less technical users to comprehend complex datasets and create accurate data visualizations and reports.
Example solutions: SparkBeyond Discovery, Dremio, Narrative BI, Power BI Copilot
2) Customer Service and User Experience
Most businesses have a customer service component that could be improved with more consistent training and customer-first communication and designs.
A variety of generative AI tools are springing up to mentor your existing customer service agents and, in some cases, replace them with chatbot, voice generation technology, and AI-powered search engines that are designed with customer queries and natural language requirements in mind.
In more specialized industries, like insurance, generative AI can also be used to better assess risk and achieve optimal outcomes for customers’ plan rates and claims.
Example solutions: Gridspace, IBM Watson Assistant, UltimateGPT, Zendesk Advanced AI, Forethought SupportGPT
3) Assistive Coding and Product Design
Regardless of your tech teams’ levels of expertise, generative AI can be incorporated to support coding and quality assurance tests for digital product design. Generative AI models like ChatGPT can fix bugs, generate test code, and write documentation for programs.
Perhaps most significant, generative AI models can help developers of all skill levels solve problems; even non-technical team players can generate code through natural language queries.
Example solutions: Code Snippets AI, ChatGPT, Google Bard, Tabnine, MOSTLY AI, Stability AI
4) Content Creation
Generative AI models have already proven their ability to quickly generate natural language content affordably and at scale, which has made these models particularly enticing for organizations that want to outsource content writing.
Several generative AI tools are able to write blog and social media content based on simple prompts. Others are designed to create more specific content, like legal documents or marketing materials. However, the area of generative AI content generation that is growing most quickly is multimedia content, with AI models used to create content like marketing videos with AI-generated voices and avatars.
Example solutions: Jasper, Notion AI, Phrasee, HubSpot Content Assistant
5) Document Summarization
A simple but effective way to use generative AI in your business is to summarize long and complex documents.
This can be a quick and easy way to identify the most important points in a long contract, legal document, conversation transcript, or other long-form content type. While some generative AI tools are standalone content summarization tools, others are embedded in everyday business operations and project management tools.
For example, Notion AI is a feature that enables Notion users to quickly summarize project notes and sales call transcripts.
Example solutions: Cohere Summarize, Anthropic Claude, PatentPal, AI21 Studio
6) Project and Workflow Management
Several of the most popular project management platforms have or are currently adding generative AI assistive capabilities to their features lists. These assistants can help with administrative tasks like note-taking during meetings, email writing, and document summarization, freeing up time for your employees to focus on more strategic tasks.
At a more technical level, these AI assistants can also automate and optimize workflow builds, make task suggestions, and support data integration efforts.
Example solutions: Process Street, Wrike, Notion AI, ClickUp, Asana
7) Cybersecurity Management
Although generative AI can pose major risks to cybersecurity postures, these AI tools are also valuable for automating business cybersecurity management workflows and handling data security requirements.
Some of the most common ways businesses are taking advantage of generative AI in cybersecurity include through smarter threat intelligence and hunting, contextualized security environment recommendations, and attack simulations. Some of these new generative AI security tools are freestanding products, but many of them are either new features or add-on products for existing cybersecurity platforms.
Example solutions: Google Cloud Security AI Workbench, Microsoft Security Copilot, CrowdStrike Charlotte AI, Airgap Networks ThreatGPT
Also see: Top Generative AI Apps and Tools
5 Benefits of Leveraging Generative AI in Your Business
Depending on how (and how much) you choose to leverage AI capabilities in your organization, expect the following generative AI benefits from your efforts:
- Quicker delivery times on projects: Generative AI models can quickly generate responses, new content, useful data, and other value your organization may need at scale. This leads to more efficient production and allows your team to complete projects on tighter timelines, which is ultimately more cost-effective.
- Fewer hands-on employee requirements: Whether you lack the in-house staff expertise to complete a certain task or find that certain tasks are too tedious to hold employee attention, generative AI tools can pick up the slack and deliver consistent, accurate, and repeatable results.
- Support and QA for employee-driven tasks: Particularly with coding and product development tasks, but also with data analytics and other content forms, generative AI tools can act as quality assurance analysts, checking employee work for errors and sometimes correcting those errors without human intervention.
- Enhanced user experiences: Many generative AI tools are embedded in software that your employees or customers already use. With the help of generative AI, search engines, enterprise knowledge bases, project management platforms, and other user-facing tools are optimized for natural language inputs and an overall better user experience.
- Industry-specific solutions: Several generative AI models have been fine-tuned to address industry- and function-specific requirements across sectors like healthcare and pharmaceuticals, insurance, manufacturing, and more. If you’re operating in a specialized industry — even with strict compliance requirements — there’s more than likely a generative AI model that will address your needs and simplify your workflow.
Also see: Best Artificial Intelligence Software
5 Challenges of Using Generative AI
Many risks come with using generative AI, and especially because this technology is still so new, not all ethical use dilemmas have been worked out. If you choose to use generative AI in your business, be on the lookout for these possible challenges you’ll face:
- Limited regulatory requirements and guidance: Generative AI is not currently regulated on a larger scale, and at this time, there are few comprehensive usage frameworks that cover how your organization can use generative AI compliantly and effectively.
- Lack of data and training transparency: Although many generative AI vendors are beginning to improve in this area, most are still not transparent with the types and sources of the training data they use, which leads to additional compliance and usage concerns.
- Cybersecurity concerns: In the wrong hands or in the hands of an ill-trained employee, sensitive data may be exposed to generative models and become part of the training set. Bad-faith actors can also train these models to work around their rules to complete unauthorized tasks.
- Identifying inaccurate outputs: Generative AI models use natural language so well that it’s sometimes difficult to determine if a model has generated an inaccurate response. Users will need to have the skills and nuanced knowledge to check their own work when using this type of technology.
- Employee usage errors: It’s difficult to regulate and control how employees use generative AI in their work. Coming up with an AI use policy is a good step toward mitigating these errors, but there’s still no consistent way to enforce usage rules, control what data employees use as inputs, or monitor multiple employees’ actions at once.
Also read: Generative AI’s Drawbacks: IP to Ethics
What Are Generative AI Best Practices for Business Usage?
Establishing best practices and procedures for generative AI use is the best way to manage internal business use of these models. Follow these best practices for better generative AI outcomes in your business:
- Use data from verified, credible, and approved sources as inputs.
- Develop an AI use policy and training plan that clearly outlines how and when employees can use generative AI tools.
- Partner with a generative AI vendor building models that can scale or be fine-tuned as your business requirements evolve; it’s also beneficial to identify vendors with products that integrate with your existing tool stack.
- Do your due diligence and research generative AI vendors’ policies, terms, and reputations before committing.
- Consider your budget and what tools fit into that budget; many foundation models are not that expensive but can quickly go up in price depending on your usage volume.
- Consider any generative AI models you use as part of your cybersecurity threat landscape and protect accordingly.
For more on this topic, read these comprehensive guides:
- Risks of Generative AI: 6 Risk Management Tips
- Generative AI Ethics: Concerns and Solutions
- Generative AI and Data Analytics: Best Practices
Learning About Generative AI
Generative AI is revolutionizing industries like art, gaming, music, and even healthcare — acquiring knowledge about this technology can therefore be very valuable. The importance of learning about generative AI stems from its transformative impact on fields such as computer vision, natural language processing, and creative arts.
For instance, in the domain of image synthesis, generative adversarial networks (GANs) have enabled the creation of realistic images, transforming industries like graphic design, gaming, and even medical imaging. In natural language processing, large language models using generative techniques have pushed the boundaries of what AI can achieve in tasks like text completion, translation, and summarization.
Taking classes and pursuing education on generative AI is an effective way to understand these technologies comprehensively. Many reputable massive open online courses (MOOCs) platforms offer courses taught by experts in the field, allowing you to learn at your own pace. These courses often provide hands-on projects, real-world applications, and opportunities to interact with a global community of learners, fostering a dynamic and collaborative learning environment.
As industries continue to adopt generative AI solutions, professionals well-versed in this technology will be in high demand. Whether you’re a software developer, data scientist, designer, or business professional, incorporating generative AI knowledge into your skill set can open up new career opportunities and make you a valuable asset to your organization.
Bottom Line: Generative AI in Your Business
Generative AI technology is a powerful resource that can be leveraged in businesses of all sizes and backgrounds, especially since so many models come in affordable limited versions that still have extensive capabilities.
The most important thing to remember when using generative AI in your business is that these tools are only as effective as the users, inputs, and procedures that surround them. Make sure all employees are trained and given the resources they need to use generative AI in their work effectively, and you’ll achieve new levels of automation, smart assistance, and productivity in your organization.
Read next: ChatGPT Enterprise: AI for Business