Generative AI Business Use Cases

The following forecast looks at how generative AI will create incremental value for companies in each different enterprise vertical. ABI Research expects each generative AI vertical market to adopt the technology at different rates depending on the business use cases that will create the most value.


Get More Free Charts

As the chart above highlights, generative AI will create US$434.4 billion in value-added annually across business sectors. The industries expected to experience the most value-added through generative AI deployment by 2030 are as follows:

  • Retail & Commerce (US$142.1 billion)
  • Marketing, Advertising & Creative (US$108.3 billion)
  • Financial Services (US$79.4 billion)
  • Energy, Utilities, and Mining (US$30.3 billion)

Generative AI Use Cases

In this section, we identify common business use cases for generative AI in various vertical markets (industries), as well as early examples of the technology.

Business Vertical

Potential Generative AI Use Cases

Early Examples of Generative AI

Financial Services

  • Stock market trend analysis.
  • Internal database management.
  • Bank account fraud protection through anomaly detection & threat intelligence.
  • Drafting client contracts.
  • Customer facing sales customization and query chatbot, e.g., insurance customer chatbot.
  • Automated credit decisions.
  • Risk management.
  • IT automation.
  • Financial literature analysis and summarization.
  • AI-supported security.
  • Bloomberg builds an LLM trained on financial data to perform Natural Language Processing (NLP) tasks.
  • Deutsche Bank embeds NVIDIA into fraud detection, intelligent avatar assistants, and speech.
  • EY modernized internal employee payroll through ChatGPT integration.
  • Zurich uses ChatGPT for claims analysis and data mining.


  • Automate contract drafting, analysis, and summarization.
  • Augment research processes.
  • Organize and summarize documents.
  • Provide intelligent legal chatbot assistant.
  • Assessment of enterprise risk against pre-determined criteria, e.g., analyze enterprise internal documents and assess known risks.
  • Allen & Overy have implemented Harvey AI for creation of and access to legal content.
  • PWC implemented Harvey AI to support due diligence, regulatory compliance, and contract analysis.
  • Clifford Chance (and many others) is working with Robin AI to speed up drafting and reviewing of contracts.


  • Monitoring production processes and product quality for anomalies.
  • IT automation.
  • Intelligent assistant for maintenance.
  • User Interface (UI) and code generation.
  • Predictive maintenance through data trend analysis.
  • Visual quality inspection.
  • Problem reporting and rectification.
  • Supply chain optimization.
  • Production planning (align demand expectations with rate of production to minimize waste).
  • Support effective product lifecycle management.
  • Code generation assistance.
  • Communication between digital twin and employees.
  • Integration of OpenAI services into Siemens Teamcenter software for product lifecycle management, e.g., generate problem reports through prompts.
  • Beckhoff has developed the TwinCAT Chat Client to support code generation for automation projects.
  • EOT’s Twin Talk GPT supports manufacturing and energy companies to train and test predictive models at the industrial edge.
  • Retrocasual announces LeanGPT service that powers Kaizen CoPilot, a tool that assists industrial engineers in designing and continuously improving manufacturing assembly processes.
  • Cognite deploys chatbot services within industrial data platforms to provide real-time advice.
  • Sinequa has added search capabilities to help manufacturers and engineers to easily discover and reuse information/insights.
  • Consumer electronics manufacturers (e.g., Foxconn, Innodisk, Pegatron) are adopting NVIDIA Generative AI to digitalize state-of-the-art factories.
  • Autodesk supports product design optimization with generative AI tools.

Automotive & Aerospace

  • Vehicle testing with generated driving scenarios.
  • Support marketing for car production.
  • Route prediction and smart traffic management systems.
  • In-car intelligent assistant.
  • Automated car and system design with improved personalization.
  • Code generation to support software-defined vehicle development.
  • Augment and optimize design process.
  • Improve manufacturing processes.
  • Haomo.AI’s DriveGPT aims to solve the cognitive decision problem of autonomous driving, i.e., decision-making.
  • GA Telesis is using Google’s Vertex AI platform to support customer orders—synthesize purchase orders and provide instant quotes, eliminating the need for sales teams to manually cross check inventory and emails.
  • Zapata and BMW are looking to optimize plant scheduling.
  • Mercedes and Microsoft have agreed to add ChatGPT to cars in the United States to enable human-like dialog between car and human (goal is to improve existing functions to make them more natural).
  • Ford establishes Latitude AI to support eyes-off, hands-free driving.
  • Volkswagen (VW) and Google Cloud AI are testing AI-informed design processes to cut weight from vehicles.
  • Toyota has built a proprietary design tool to support the creation of photorealistic model designs.
  • NASA uses generative AI to assist in the design process for spaceship parts.


  • Free tuition for children.
  • Generate new content to support class preparation.
  • Support children with learning difficulties or disabilities through text-to-sound or text-to-image generation tools.
  • Intelligent teacher and student assistant.
  • Real-time assessment feedback.
  • Generate new questions for testing.
  • Develop and organize class material, lesson plans, and assessments.
  • Build personalized teaching materials for diverse groups of children.
  • Personalize curriculum and enable adaptive learning.
  • Expand education opportunities with AI-assisted low-cost online training programs.
  • Immersive language training through “person-to-AI.”
  • Variety of applications have been built to support education processes, e.g., Gradescope, Knewton, and Cognii.
  • Edugo.AI, an AI-powered education content creator, recently acquired by Docebo.
  • Maximal Learning, a startup focused on AI-generated content.
  • Khanmigo is a generative AI powered chatbot that can support teachers and students.

Marketing, Advertising & Creative

  • Create digital artwork.
  • Develop new marketing content.
  • Integrate design tools into creative software.
  • Write scripts or story telling for videos/adverts.
  • Stock image generation.
  • Transforming Two-Dimensional (2D) into Three-Dimensional (3D) images and digital content.
  • Post-marketing surveillance and data analysis.
  • Develop shortform content from long form media.
  • Localization and personalization of marketing content.
  • Eliminate cookies through generative AI and first-party data.
  • Augment copywriting and social media posting.
  • React to end user and personalize advertisements to interests, emotions, and experiences.
  • Sports Illustrated, Anthropologie, and Transcend have integrated with Jasper AI to support copywriting, trend identification, and topic development.
  • Adobe has implemented a range of generative AI image and video editing tools through Firefly.
  • Coca-Cola is building new marketing tools using OpenAI.
  • WPP is partnering with NVIDIA to develop a generative content engine for digital advertising. They are looking to integrate additional 3D content to personalize advertising.
  • BBDO uses Stable Diffusion to augment content generation.
  • Meta Advantage+ Suite provides an AI sandbox for testing early versions of new generative AI tools, e.g., ad generator tools.
  • Google is beginning to use Bard and generative AI tools to automate ad development.
  • Code and Theory strikes up partnership with Oracle to build ad tools.
  • Snap is announcing AI-generated sponsored links that can personalize ads based on a user’s conversation with chatbots.

Entertainment & Multimedia

  • Leverage sound-to-text generation to automate subtitles.
  • Design new games/entertainment products.
  • Develop content for TV shows/films.
  • Bridge sound gaps in videos.
  • Develop short form content from long form media.
  • Develop immersive gaming experience and add greater depth to games through increasingly human-like responses.
  • Help build out metaverse.
  • Reduce game development lifecycle.
  • Automated game testing.
  • Snail Inc. is using generative AI to support game development and content generation.
  • Roblox launches Code Assist & Material Generator platforms to support game creation.
  • Ubisoft uses generative AI to support game development.
  • Creative.AI provides tools to streamline video game publishing with generative AI.
  • Artisse Interactive launches AI-powered image generator.
  • Unity’s Project Barracuda to revolutionize in-game experience.
  • Blizzard is training an image generator to support the development process.
  • NVIDIA Avatar Cloud Engine provides natural language interactions with non-playing characters to enhance in-game experience.


  • Diagnose and detect issues through medical image/Computed Tomography (CT) scan evaluation.
  • Predict patient re-admission.
  • Record patient histories through sound-to-text generation.
  • Email generation for patient communication.
  • Intelligent assistant/chatbot to answer healthcare questions.
  • Customization and personalization of treatment.
  • Health management and general wellbeing through trend assessment.
  • Google’s Med-Palm-2 answers medical questions.
  • Nuance/EPIC/Microsoft developing AI power tools to support patient records and message drafting.
  • Zepp Health uses generative AI for health management.
  • 3M in partnership with Amazon Web Services (AWS) are automating health records, e.g., real-time speech recognition to automate note taking for 250 electronic health record systems.
  • CheXNet an LLM that can predict the probability of pneumonia from a chest X-ray image.
  • NVIDIA and Shutterstock are partnering on a text-to-3D content model.
  • Getty Images is working with NVIDIA to build foundational models for content generation (custom images/videos).


  • Drug discovery and design, i.e., generating new molecules with specific properties (a particular binding protein).
  • Drug launch and commercialization (including marketing).
  • Lead discovery and optimization.
  • Optimization of clinical trials, i.e., identify genetic markers to best fit patients to certain groups, data analysis, and preclinical trials.
  • Identify disease patterns across larger datasets.
  • Project lifecycle management and reporting.
  • Insilico Medicine and Evotec are launching clinical trials with generative AI for discovery and design.
  • Pfizer has deployed generative AI for small molecule programs.
  • Google offers Med-PaLM 2./
  • Astellas Pharma is using generative AI to augment humans across the entire drug discovery and delivery chain (from target identification/validation to clinical trials and lifecycle management).

Transport, Supply Chain, Smart Cities, and Logistics

  • Face identification to streamline airport security.
  • Real-time traffic routing and smart city deployment.
  • Personalized and immersive travel applications.
  • Identify disruptive trends, such as transportation delays or weather events.
  • Demand forecasting and inventory management.
  • Supplier risk assessment.
  • Pricing automation.
  • Processing logistics documents.
  • Identify anomalies in supply chains to detect fraudulent activity.
  • Identify and assess risks in transport routes.
  • Traffic pattern optimization.
  • Real-time crowd analytics and management.
  • Traffic control and smart policing.
  • Supply chain optimization.
  • Amazon is using AI tools to plan routes/maps for deliveries and optimize inventory.
  • Deloitte is developing Quartz AI for logistics and retail customer service.
  • Flexport is leveraging Scale document AI to accelerate logistics documents processing.

Energy, Utilities, and Mining

  • Generate new routing and scheduling strategies to lower energy transportation costs.
  • Automate pricing based on market trends and historical data.
  • Identify potential hazards within the mining process.
  • Demand forecasting, energy output forecasting.
  • Grid management and optimization.
  • Customization of customer offerings.
  • Energy storage optimization.
  • Gridmatic uses AI to predict weather to inform energy supply/demand expectations.
  • Lightsource bp launches AI-generated home energy assistant.
  • Octopus has implemented generative AI in its customer service operations with 44% of emails being answered by this service.
  • BHP and Microsoft build generative AI tools to implement operational changes to impact ore recovery.
  • Shell and SparkCognition partner to support Subsurface Imaging with Generative AI.

Retail & E-Commerce

  • Develop visual search tools to support customer accessibility.
  • Build customer chatbots and intelligent assistants.
  • Personalization of product recommendations.
  • Product content generation (i.e., description, summaries, product images).
  • Personalized marketing.
  • Fraud identification (e.g., product listings, comments).
  • Product design.
  • Automated website development.
  • Generation of copy for websites.
  • Trend analysis and data synthesis for merchants and e-commerce stores.
  • Loss prevention.
  • AI customer service representatives for retail assistance.
  • Logistics & shelf restocking.
  • Shopping recommendations and merchandising.
  • Amazon rolling out review summary feature.
  • eBay is implementing automated listing description feature using image-to-text generation.
  • Carrefour launches Hopla, an online chatbot, to support description generation and purchasing.
  • Ask Instacart is an AI-powered search tool offering personalized recommendations.
  • Shopify is a tool to support merchants with generating descriptions.
  • 7 Eleven has implemented DeepBrain AI’s “AI human” and “AI kiosk” to aid customer engagement.


  • Deploy real-time voice-to-text for accessibility.
  • Enable real-time voice translation services.
  • Develop hyper-personalized services for B2B and B2C sales, including automated pricing/bundles.
  • Deploy customer service chatbot.
  • Perform proactive network maintenance through trend assessment.
  • Deploy intelligent assistants to support infield network maintenance.
  • Generate software code to support developers.
  • Began to integrate generative AI into chatbots (Orange Djingo, Telstra’s Codi, Vodafone Tobi).
  • SK Telecom built “A” on top of GPT-3.
  • NVIDIA and SoftBank integrating into 5G/6G through generative AI-ready data centers.
  • SK Telecom uses chat GPT-3 to handle customer support.
  • BT is looking to restructure the workforce through generative AI, mainly targeting customer support.
  • Amdocs’ amAlz, an enterprise-grade framework, to support telco customer service and service differentiation through generative AI.

Information Technology

  • Generate UI designs.
  • Accelerate application development through code automation.
  • Developing advanced security solutions.
  • Automated software testing processes.
  • Infrastructure optimization through improvement prompts.
  • Augment IT reporting and actions.
  • Assess system risks across IT infrastructure.
  • Automated process deployment.
  • Support metaverse development by generating dynamic virtual environments that can adapt and respond to users’ actions.
  • Siemens Teamcenter uses OpenAI to support report generation.
  • Goldman Sachs developers testing tools for code writing.
  • Slack is embedding ChatGPT to provide instant conversation summaries, research tools, and writing assistance.
  • Salesforce implements EinsteinGPT to support data analytics.
  • Crowdstrike has introduced CharlotteAI, an intelligent security analyst that will help platform users better understand threats and risks facing enterprises.
  • Google Cloud announced Cloud Security AI Workbench that uses fine-tuned models to identify and manage threats.
  • Blink Copilot offers a vendor-agnostic platform for automating enterprise security and IT operational workflows.

Now in this next table, ABI Research assesses how we envision the adoption rate for generative AI use cases in each vertical market.

Business Vertical

Adoption Expectation

Financial Services

Isolated use cases in wave 1 with a focus on augmenting employee productivity; followed by deep integration by wave 3.


Strong adoption from wave 1 due to quick intelligent assistant innovation; will become increasingly embedded within legal processes moving forward.


Some wave 1 adoption to support lifecycle process management; but most of the adoption will come to support process automation and optimization.


Quick adoption of generative AI to augment design/development process, followed by slower adoption of generative AI for advanced use cases.


Slow adoption in wave 1 given budgetary constraints and risk aversion. Increasing adoption of value-add services toward the end of the decade.

Marketing, Advertising, & Creative

Earliest adopters of generative AI services with huge value created from day 1 through low-hanging use cases.

Entertainment & Multimedia

Significant investment in wave 1, as most use cases augment employee processes.


Some early investment, but most of the market will be slow, given that available capital is limited.

Energy, Utilities, & Mining

Limited early value given complexity of use cases; huge value created through wave 3 use cases.

Retail & E-Commerce

Quick adoption in isolated use cases with majority of value being created in wave 1 and wave 2.


Traditional telco investment is limited, expect some adoption of low-hanging use cases (e.g., customer service), but this may take some time.


Huge investment now, but value creation may take time, given the scope of use cases and investment lifecycle in pharmaceuticals.

Learn more about the emerging business use cases and applications for generative AI in ABI Research's free whitepaper, Generative AI in the Enterprise Sector: How Suppliers Can Meet The Hype. In addition to identifying the use cases for enterprise generative AI, the report explores the value drivers and constraints of generative AI adoption, how fine-tuned models are key to unlocking broader adoption, what the latest AI trends are, the ethical and social dilemmas presented by generative AI, and more. Download the report today!

Related Blog Posts

Related Services