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Generative AI, a type of AI that is trained to generate original content, is growing in both its consumer and business use cases. Particularly in the enterprise, 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 in the Enterprise: Table of Contents
- Generative AI Enterprise Use Cases
- How Enterprises Are Using Generative AI Today
- Generative AI Use Cases: Ethics and Compliance
- Bottom Line: Generative AI Enterprise Use Cases
Generative AI Enterprise Use Cases
Some enterprises, like marketing and sales-driven companies, have quickly added generative AI use cases into 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 firms aren’t able to produce or support artificial intelligence without external support. Below, learn about some of the top ways AI companies are enabling enterprise use cases to take maximum advantage of generative AI capabilities.
Also read more on this topic: Generative AI Landscape: Current and Future Trends
Code Generation, Documentation, and QA
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.
Example solutions: Code Snippets AI, ChatGPT, Google Bard, Tabnine
Product and App Development
Generative AI is now being used to code various kinds of apps and write product documentation for these apps. While applications are probably the most common use cases that generative AI tools are supporting today, generative AI is also going into projects like semiconductor chip development and design.
Generative AI foundation models and APIs are also being used to develop new and fine-tuned generative AI models and products. For example, a number of customer service and chatbot generative AI tools have been built based on OpenAI foundation models.
Example solutions: MOSTLY AI, Stability AI, AI21 Labs, GPT-4
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 accounts, product pages, and business websites.
Many of these models 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.
Example solutions: Jasper, Notion AI, Phrasee, HubSpot Content Assistant
Inbound and Outbound Marketing Communication Workflows
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. In some cases, they can also automate the process of moving these contacts to the next stage of the customer lifecycle in a CRM platform.
These types of assistive generative AI tools are increasingly popping up in both CRM and project management platforms. At this point it appears that every month, more enterprise tools are launching for leveraging generative AI for communication and workflow automation.
Example solutions: Twain, Salesforce Einstein GPT, HubSpot ChatSpot
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. This sector is a rapidly growing source of generative AI enterprise use cases.
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 messages in multiple languages they don’t speak.
Example solutions: Diagram, Synthesia, Lightricks, Rephrase.ai
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).
Example solutions: Stability AI’s Stable Diffusion, Plask, Charisma, Latitude Voyage
Performance Management and Coaching
Generative AI can be used in several business and employee coaching scenarios. As an example, contact center call documentation and summarization, when combined with sentiment analysis, gives managers the information they need to assess current customer service rep performance 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.
Example solutions: Anthropic Claude, Gong, CoachHub AIMY
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 piece of 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.
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.
Example solutions: SparkBeyond Discovery, Dremio, Narrative BI
Customer Support and Customer Experience
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 customer service solutions with the help of OpenAI’s API and ChatGPT. Examples of customer service generative AI solutions are dramatically changing the chatbot landscape by offers longer service hours at far cheaper cost.
Example solutions: Gridspace, IBM Watson Assistant, UltimateGPT, Zendesk Advanced AI, Forethought SupportGPT
Pharmaceutical Drug Discovery and Design
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.
Example solutions: Insilico Medicine, Entos, Aqemia, New Equilibrium Biosciences
Medical Diagnostics and Imaging
Generative AI in medicine is still nascent, but that is changing quickly. Image generation and editing tools are increasingly being 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.
Beyond purpose-built AI tools for medicine, ChatGPT and GPT-4 have also been tested in the pathology space. ChatGPT recently passed the U.S. Medical Licensing Exam and has proven fairly effective in identifying diseases in submitted pathology images. However, as is the case with ChatGPT’s many other use cases, this success should be viewed cautiously and bolstered with actual medical professionals who can check results for quality.
Example solutions: Paige.ai, Google Med-PaLM 2, ChatGPT and GPT-4
More on this topic: 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 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 compromising actual business or consumer data.
Example solutions: Syntho Engine, Synthesis AI, MOSTLY AI, Infinity AI
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.
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.
Example solutions: Biomatter, Clarifai, C3 Generative AI Product Suite
Fraud Detection and Risk Management
This type of 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.
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’s development, it’s important for companies to invest in fraud and threat detection solutions in order to mitigate this risk.
Example solutions: Simplifai InsuranceGPT, Docugami, ChatGPT
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 job or project. These tools are designed to not only search 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.
Example solutions: Glean, Coveo Relevance Generative Answering, Elasticsearch Relevance Engine
Learn about other generative AI examples
How Enterprises Are Using Generative AI Today
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 their 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:
- Major oil and gas company: Helping the client implement tools from Microsoft Azure and OpenAI while instilling best practices for big data management. Accenture is specifically 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: Using generative AI to automatically review and triage emails to the right employees and departments.
- A European banking group: Primarily using Microsoft Azure architecture and a GPT-3 LLM to create and manage a more searchable employee knowledge database.
Also see the enterprise leaders in AI: 100+ Top Artificial Intelligence Companies 2023
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 are able to create custom biomolecular AI models for their specific research purposes.
BioNeMo is currently only available through limited early access, so it’s unclear who current customers are and how they are using the service. Part of what will make this solution highly scalable and attractive to potential customers is its on-demand supercomputing infrastructure, which is available to support early drug discovery pipelines.
Travel and Hospitality: Expedia Use Case
Expedia’s beta 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 are able to 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 the 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. 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 rightfully afraid that parts of their job will soon be outsourced to AI; to combat this fear and build up their career prospects, offer training and certifications that will help them to use AI in their jobs and build skills that cannot be easily replicated by AI models.
Learn more: 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 affect your employees and your customers.
Read next: Top 9 Generative AI Applications and Tools