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Generative AI is a type of artificial intelligence that is trained to generate original content, and is rapidly find a growing number of enterprise use cases. In the enterprise across several business sectors, generative AI is:

• Quickly automating and simplifying project workflows.
• Taking repetitive tasks off the plates of busy employees.
• Helping businesses maintain high-quality and high-volume production standards.

In this guide, learn how generative AI is being optimized for enterprise use cases across a variety of industries and tasks.

Generative AI Enterprise Use Cases

Some enterprises, like marketing and sales-driven companies, have quickly added generative AI to their workflows because of the speed and scale AI tools can bring to content production and customer relationship management efforts. Other industries that have more legal and compliance hoops to jump through — such as healthcare, insurance, and education — have been more hesitant to add generative AI, which is growing quickly but without much transparency or regulation.

Enterprise companies across these industries and more are looking to today’s top AI companies for assistance — most enterprises cannot produce or support artificial intelligence without external support. Below, learn about some of the top ways AI companies are fulfilling enterprise use cases that take maximum advantage of generative AI capabilities.

To learn more about the growth of this powerful emerging tech, read our guide: Generative AI Landscape: Current and Future Trends

Code Generation, Documentation, and Quality Assurance

For software developers and programmers, generative AI can write, complete, and vet sets of software code. Quality assurance is perhaps the most important emerging use case in this area, with generative AI models handling bug fixes, test generation, and various types of documentation.

As they mature, generative AI coding tools are increasingly assisting non-developers by creating code from their natural language scenario-based queries. This particular feature of AI coding tools is a promising development in a business world that’s pushing for greater tech democratization and accessibility.

But what many people don’t realize is that these tools can also supplement the work of experienced developers and coders. Particularly if developers are looking to move through a coding project more quickly or with fewer errors, generative AI tools can make well-informed recommendations to support their efforts. For example, many generative AI coding tools provide code autocompletion, error detection and correction, and even code refactoring to make complex code more readable and efficient.

Example solutions

Product Development and Management

Generative AI is now being used to code various apps, write product documentation, and manage user experience and new iterations throughout the product lifecycle. Generative AI is also increasingly being implemented for generative design, or the act of using AI to generate different design concepts at scale so product designers can more effectively determine their optimal design.

Developers and product managers can use generative AI tools to autocomplete, improve, and consolidate data for their various requirements. For example, a product developer or designer might use a generative AI tool to brainstorm new code and designs that they wouldn’t have considered otherwise. A product manager, on the other hand, may use generative AI tools that consolidate and summarize user feedback from various data sources so products can be improved with relevant data.

Most commonly today, generative AI foundation models and APIs are being used to develop new and fine-tuned generative AI models and products. Several enterprises have built customer service and chatbot generative AI tools based on OpenAI foundation models.

Example solutions

Blog and Social Media Content Writing

With the right prompts and inputs, large language models (LLMs) are capable of creating appropriate and creative content for blogs, social media posts, product pages, and business websites. Existing content can be modified, shortened, or expanded upon with generative tools, and many of these tools can also generate entirely new content with the right user prompts and contextual information.

Generative AI models that focus on this type of content generation enable users to give instructions on article tone and voice, input past written content from the brand, and add other specifications so new content is written in a way that sounds human and relevant to the brand’s audience. However, users need to remember that generated content could contain inaccuracies or unsubstantiated claims; particularly for content that is journalistic or fact-driven, writers, editors, and/or QA analysts should take the time to personally fact-check AI outputs before publishing them.

Example solutions

Inbound and Outbound Marketing and Sales

Inbound and outbound marketing campaigns frequently require employees to send contextualized emails and chat threads to prospective and current customers on a daily basis. Generative AI solutions can create and send the content for these communications to lessen the load. In some cases, they can also automate the process of moving these contacts to the next stage of the customer lifecycle on a CRM platform.

These types of assistive generative AI tools are increasingly popping up in both CRM and project management platforms, but there are also many independent generative AI tools for sales and marketing workflows. If your team of sales development representatives, marketers, salespeople, or other members who focus on outreach are getting minimal results with their current outreach strategies, the right tool can help them generate better content at a greater scale. Even better, a growing number of these sales and marketing tools include AI-powered analytics so you can develop a deeper understanding of what is and isn’t working with your current tactics.

Example solutions

Project Management and Operations

Project management platforms are in the early stages of incorporating generative AI into their toolkits, but many have already released public betas or full versions of AI suites to their users. These tools can support users with everything from task and subtask generation and recommendations to note-taking to project risk prediction, and use cases continue to expand, particularly for automation workflows. Project management AI tools also help users manage and summarize documents, datasets, and other assets so both internal resources and client-submitted information can be processed and applied to projects more efficiently.

Several generative AI tools have also emerged for assistive and secretarial tasks, both within project management platforms and as standalone solutions. With these tools, users can use a voice assistant to take notes and jot down ideas on their mobile devices, create smart and quick email replies, complete smart searches and summaries of important business documents, and automate certain communication workflows. The goal of this type of technology is to save time, giving users the ability to focus their efforts on higher-level strategy rather than day-to-day business and data management.

Example solutions

Graphic Design and Video Marketing

Generative AI is capable of generating realistic images, animation, and audio that can be used for graphic design and video marketing projects. Some generative AI vendors also offer voice synthesis and AI avatars so you can create marketing videos without actors, video equipment, or video editing expertise.

Generative AI video marketing tools are some of the earliest pioneers in multilingual content generated by artificial intelligence. While video avatars in particular will need some work before they can believably replace human speakers, this development is an exciting one, especially for global enterprises that need to send out video marketing or communication messages in languages they don’t speak.

In general, this type of generative AI lowers the barrier to entry for accessing technologies and products that have traditionally been cost-prohibitive. For example, generative AI tools can be used to not only edit headshots but also create believable background imagery and other edits so companies don’t need to pay for professional headshots. As another example, several generative AI tools are emerging that can create a relevant social media graphic or image based on user inputs.

Example solutions

Entertainment Media Generation

As AI-generated imagery, animation, and audio become more and more realistic, this type of technology is being used to create the graphics for movies and video games, the audio for music and podcast generation, and the characters for virtual storytelling and virtual reality experiences. With many of these tools, an actual human does not need to go on camera, edit footage, or even speak in order to create believable content.

Some tech experts predict that generative AI will constitute the majority of future film content and script writing, though creatives are understandably pushing back on that assumption. Right now, these tools are primarily used to supplement existing scripts and create more interactive non-player characters (NPCs) in the entertainment industry.

Example solutions

Performance Management and Coaching

Generative AI can be used in several business and employee coaching scenarios. For example, contact center call documentation and summarization, when combined with sentiment analysis, gives managers the information they need to assess current customer service rep outreach calls and coach employees on ways to improve.

Generative AI coaching and performance management tools can support both managers and their direct reports. Business leaders can use generative AI tools to inform — and even structure — performance reviews for their employees, while employees can use conversational AI tools to get feedback on their performance and areas for improvement independently.

However, while these kinds of AI solutions are trained to give thorough feedback and utilize ample data to back that feedback, not all employees will be comfortable with or trust this performance management approach. It’s important to supplement any generative AI you use in business performance management with direct manager feedback, catered training, and other personal touches that earn and maintain employee trust.

Example solutions

Business Performance Reporting and Data Analytics

Because generative AI can work through massive amounts of text and data to quickly summarize the main points, it is becoming an important tool for business intelligence and performance reporting. It’s especially useful for unstructured and qualitative data analytics, as these types of data usually require more processing before insights can be drawn.

Generative AI data analytics tools may be standalone products or embedded features within well-established data analytics platforms, such as Power BI. Generative AI enables traditional data analytics platforms to go beyond manual workflows and visualizations by supplementing the data scientist’s ideas with suggestions for improved visuals, easier-to-read reports, and cleaner data.

One of the most interesting areas being explored with this technology is data narratives, which are highly contextualized AI explanations of datasets. This goes beyond typical visualizations and dashboards into explainable data, which is particularly helpful for less technical business stakeholders and other key players who need straightforward information about business performance.

Learn more about how generative AI can improve and extend an enterprise analytics practice: Generative AI and Data Analytics: Best Practices

Example solutions

Customer Support and Customer Service

For many of the most straightforward customer service engagements, generative AI chatbots and virtual assistants can handle customer service questions at all hours of the day. Chatbots have been used for customer service for many years, but generative AI advancements are giving them additional resources to provide comprehensive and more human answers without the help of a human customer support representative.

Many early adopters of this technology are building custom service solutions with the help of OpenAI’s API and ChatGPT Enterprise. Customer service generative AI solutions are dramatically changing the chatbot landscape by offering longer service hours at far cheaper costs while also offering users higher-quality, real-time responses to more complicated queries than ever before.

Similar to AI-driven performance coaching, enterprise users must strike the right balance between AI transparency and a human touch with customer service. For example, you may want to state upfront if a generative-AI-informed chatbot is handling a customer’s initial questions, but to keep them from getting frustrated, make it clear that a human is available to support them if the questions go beyond the chatbot’s frame of knowledge. Being transparent and aware of what customers want still allows you to save time with repeated or simple questions but simplifies the handoff if a human support representative needs to step in.

Example solutions

Medical Diagnostics and Pharmaceutical Drug Discovery

Generative AI in medicine and pharmaceuticals is still nascent, but that is changing quickly. Image generation and editing tools are now increasingly used to optimize and zoom into medical images, allowing medical professionals to get a better and more realistic look at certain areas of the human body. Some tools even perform medical image analysis and basic diagnostics on their own, though it’s important to view this success cautiously and bolster results with actual medical professionals who can check results for quality.

On the pharmaceutical side, generative AI technology is being used to make drug discovery and design processes more efficient for new drugs. With this new development, scientists are beginning to generate novel molecules, more effectively discover disordered proteins, and design and predict clinical trial results.

AI-driven drug discovery is one of the areas of generative AI that is receiving the most funding right now, so expect this particular enterprise use case to grow significantly in the coming months and years. Few tools have been officially brought to market, but a number are in beta and early adopter trials. However, there has also been fairly significant churn among both medical and pharmaceutical AI companies, so it’s unclear who the leading players are in this segment or who the leaders will be in the coming months and years.

Example solutions

For a fuller understanding of how generative AI is used in healthcare, read our guide: Generative AI in Healthcare

Consumer-Friendly Synthetic Data Generation

Although generative AI poses some crucial security concerns, it can also be used to heighten data and consumer privacy when used strategically for synthetic data generation.

For example, generative AI can be used to create synthetic data copies of actual sensitive data, allowing analysts to analyze and derive insights from the copies without compromising data privacy or compliance. With these accurate data copies, data analysts and other members of an enterprise team can develop AI models and score those models without exposing actual business or consumer data to their efforts.

A range of different tools are available for generating synthetic data, but one of the most interesting tool formats is the generative adversarial network (GAN). With this type of generative tool, two different neural networks compete against each other to learn intricate data patterns and structures for duplication. This competition-driven learning framework enables generative models to develop a more complex understanding of source data so they can effectively create synthetic data for a variety of enterprise use cases, including data augmentation, image generation, drug discovery, anomaly detection, and voice generation.

Example solutions

Smart Manufacturing and Predictive Maintenance

Generative AI is quickly becoming a staple in modern manufacturing, helping workers create more innovative designs and meet other production goals. In the realm of predictive maintenance, generative models can generate to-do lists and timelines, make workflow and repair suggestions, and simplify the process of assessing complex data from sensors and other parts of the assembly line.

Generative AI copilots are taking off in diverse enterprise settings and use cases, including manufacturing. With these types of copilots, both engineers and frontline workers are given real-time data about system performance issues and optimizations, how current assembly lines and tasks can be optimized, and what data needs to be considered for more efficient outcomes in the future.

In medicine, manufacturing, and other materials-based industries, generative AI is also being used in a process called inverse design. With inverse design, generative AI assesses missing materials in a process and generates new materials that fulfill the required properties for that environment. With inverse design and other innovations in the background, generative AI is going into projects like semiconductor chip development and design, which makes it a helpful solution for creating the materials generative AI models themselves need to operate effectively.

Example solutions

Fraud Detection and Risk Management

AI-powered fraud detection and risk management technology can analyze large amounts of transaction or claims data, quickly summarizing and identifying any patterns or anomalies in that data. With these capabilities, generative AI is a great supporting tool for fraud detection, underwriting, and risk management in finance and insurance scenarios.

For insurance in particular, generative AI can benefit both insurers and their customers. Both underwriters and claims adjusters can use generative AI tools to scour previous policies and claims — both their own and third-party data sources — to optimize coverage and claim outcomes for their customers. This process gives customers more reasonable and well-fitted plans for their needs and also saves insurers time, as they can quickly pull together the right plans and settlements because of how generative AI collects and summarizes massive amounts of relevant data.

The flip side of this solution is a potential concern for enterprises, as bad actors can take advantage of generative AI tools to commit fraud and other crimes more effectively. At this phase in generative AI development, companies need to invest in fraud and threat detection solutions to mitigate this risk.

Example solutions

Optimized Enterprise Search and Knowledge Base

Both internal and external search are benefitting from generative AI technology. For employees and other internal users of business tools, generative AI models can be used to scour, identify, and/or summarize enterprise resources when users are searching for certain information about their jobs or projects. These tools are designed for not only searching typical sources, like company files, but also company applications, messaging tools, and web properties.

Similarly, generative AI models can be embedded into company websites and other customer-facing properties, giving visitors a self-service solution for finding answers to their brand questions. Many companies have long invested in chatbot support tools, but with generative AI-powered search, these chatbots now have a much larger library of resources to reference when answering user questions.

Creating smarter versions of self-service search tools can save time for every member of your team. Leaders can shift their focus to higher-value projects and tasks rather than focusing their energy on routine employee training and information sharing. On the flip side, employees still receive the information and training they need. If the same technology is applied to customer service, customers can independently find the answers to their most important questions so your team can focus on other areas of business growth and management.

Example solutions

Leading Examples: Generative AI Enterprise Use Cases

Clearly, generative AI is being used to support many enterprise use cases and creative initiatives today. Some enterprises are sticking with conventional subscription-based generative AI models, while others are building their own models and versions of these tools into an existing tool stack.

Here are a few examples of how major enterprises are adding generative AI to their processes today:

Professional Services and Business Operations: Accenture Use Case

Accenture, a major consulting firm, is using generative AI to help its clients create smarter business strategies, roadmaps, and operations. Accenture’s clients span across banking, sales, customer service, legal, and other industries and are using the firm’s generative AI services for enhanced search, document summarization, and automated communications.

Here are a few specific examples of how client companies are partnering with Accenture for generative AI solutions:

• A major oil and gas company: Accenture is helping the client implement tools from Microsoft Azure and OpenAI while instilling best practices for big data management. The firm is helping with multimodal data handling, cognitive search, and semantic modeling.
• Spain’s Ministry of Justice: Accenture designed an AI-powered search engine for legal professionals — called Delfos — that makes it easier for judges, lawyers, and regular citizens to look up judicial processes and other information in a massive dataset and document collection. Accenture is also helping the organization use large language models to more efficiently manage how users access judicial information.
• A multinational bank: Accenture is using generative AI to automatically review and triage emails to the right employees and departments.
• A European banking group: The company is primarily using Microsoft Azure architecture and a GPT-3 LLM to create and manage a more searchable employee knowledge database.

To learn more about the enterprise leaders in AI, see our comprehensive guide: 150+ Top Artificial Intelligence Companies 2024

Life Sciences: Nvidia Use Case

Nvidia recently released its BioNeMo Drug Discovery Cloud Service, which uses large language modeling to advance and speed up drug discovery, protein engineering, and research in genomics, chemistry, biology, and molecular dynamics. Researchers who use BioNeMO with Nvidia cloud APIs can create custom biomolecular AI models for their specific research purposes.

BioNeMo has recently shifted from limited early access to full public access. Part of what makes this solution highly scalable and attractive to potential customers in its early stages is its on-demand supercomputing infrastructure, which is available to support early drug discovery pipelines.

Travel and Hospitality: Expedia Use Case

Expedia’s ChatGPT-powered travel planner lets users ask questions and get recommendations on travel, lodging, and activities. It also saves suggested hotels and venues through an intelligent shopping feature, so users can recall and easily book recommended lodging.

Although this solution is more catered to individual consumers than enterprise users, it certainly can be used in a business context. For employees who frequently travel or book travel for their colleagues, Expedia’s travel planner can smooth out the planning process and free up time for more important tasks.

E-commerce and Retail: Shopify Use Case

Shopify now offers Shopify Magic to help retailers generate product descriptions and other product-related content with artificial intelligence. Users can input a verbal tone and a handful of keywords that they want to be represented in the product description. From there, Shopify generates a description that matches those parameters.

Shopify is one of the few retail and e-commerce enterprises that has tapped into generative AI at this time. Although Shopify Magic is still fairly new, it is being integrated with other Shopify features so Shopify customers can better incorporate AI assistance at every stage of product development and selling.

Fintech and Software Development: Stripe Use Case

Stripe, a financial services SaaS company, is using OpenAI’s GPT-4 to power better documentation, summarization, and query management for developers that use Stripe Docs. Stripe Docs is set up so users can input natural language queries about documentation. Stripe Docs then responds by summarizing and extracting important pieces of that content in a user-friendly format.

Stripe is also helping OpenAI and several other generative AI companies better monetize their products with Stripe Billing, Stripe Checkout, Stripe Tax, Revenue Recognition, and Link. These tools help OpenAI, Runway, Diagram, Moonbeam, and other generative AI companies create a smoother subscription and checkout process for customers, all while managing compliance and finances for these AI companies.

Also read: Generative AI Companies: Top 12 Leaders

Generative AI Use Cases: Ethics and Compliance

Generative AI is an emerging technology, and there are still many unknowns that significantly impact its use in the enterprise. For example, most users aren’t familiar with how the models are trained or what data goes into their training.

Additionally, these models have wide-ranging capabilities that can both help and harm cybersecurity postures. And finally, generative AI models are quickly growing in their skill sets, posing a threatening alternative for many skilled workers’ careers.

So what can enterprises do to make sure they’re using generative AI responsibly, ethically, and in compliance with security and privacy regulations? We believe best practices for using generative AI ethically will evolve rapidly as the technology matures, but these tips are a good way to get started with responsible use:

• Only input depersonalized and nonsensitive data into large language models. Otherwise, your most sensitive data could become part of the tool’s training dataset and be exposed to third-party users and companies.
• Stay current with generative AI news and trends. The generative AI space is changing daily, and with that change comes regular news about companies that are getting the technology right and others that are taking dangerous and/or unethical steps in their AI development efforts. Staying updated on all of these changes will ensure you only use the most credible tools and work with the most ethical AI providers.
• Create an AI usage and ethics policy for your business. Your policy should cover how internal users in your organization are allowed to use AI tools and also how your business is allowed to invest in third-party tools. For reference, several AI policy templates are already publicly available.
• Offer career training to all employees. Employees are understandably afraid that parts of their jobs will soon be outsourced to AI. To combat this fear and build up their career prospects, offer training and certifications that will help employees use AI in their jobs and build skills that cannot easily be replicated by AI models.

To learn more about how AI is changing the growth of cybersecurity, see: Generative AI and Cybersecurity: Advantages and Challenges

Bottom Line: Generative AI Enterprise Use Cases

Because generative AI capabilities are changing on a near-daily basis, enterprise use cases for this new technology are evolving just as quickly. With this change comes new opportunities for enterprises to enhance their current operations.

Already, enterprises are leveraging generative AI for everything from writing marketing copy to discovering new pharmaceuticals. For enterprise leaders who want to incorporate generative AI into their business, the key is to consider what model works best for your business, what you’re trying to achieve, and how this new business factor will impact your employees and your customers.

For a fuller understanding of the generative AI sector, read our guide: Top 9 Generative AI Applications and Tools