Usage of GenAI is across industries, across various business functions within each industry and at an individual level. To get a comprehensive view of the usecases, this page gives a summary of the AI usage in Business context and then specifically GenAI usecase scenarios. Few illustrations are also provided.
1. Typical usage of AI in Business Functions
Managing Operations
Usage of knowledge extracted from structured and unstructured organization data and creation of models for decision making.
Production and Logistics
Prescriptive analytic models like optimisation and simulation models to help quick, efficient, and smart decisions on factory layout, inventory management, warehouses, transportation, and logistics scenarios.
Marketing
Quick analysis of massive amounts of market data covering end to end business processes, Predictive lead management and all Marketing decisions. In Digital Marketing the following are some of the usecases:
- Understand customer behaviour at an individual level –> target marketing efforts at the right customers with the right product or service offering.
- Usage of data from variety of sources to improve digital marketing outcomes.
- Marketing tasks like forecasting, automation, and recommending decisions
Finance
- Products and services in the areas of Wealth Management, Algorithmic and High-frequency trading, Risk Assessment and Management, Business Security, Financial Fraud, and Smart Customer Service.
- Text mining leading to the prediction of Forex rates and Stock prices
Human Resources Management
🔍 Talent Acquisition & Recruitment
- Resume screening & shortlisting using NLP to match candidates to job descriptions.
- AI-assisted job description creation for clarity, inclusiveness, and skill alignment.
- Candidate sourcing from job boards and social networks using AI search agents.
- Interview scheduling automation and smart coordination.
- Video interview analysis (sentiment, behavioral cues, skill inference).
📈 Talent Development & Learning
- Personalized learning paths based on role, skills, and performance data.
- Skills gap analysis integrated with competency frameworks.
- AI-powered coaching bots for career guidance and micro-learning.
- Training effectiveness analytics using engagement and performance data.
🏢 Employee Experience & Engagement
- HR chatbots for policy queries, leave requests, and routine HR support.
- Sentiment analysis from surveys, emails, or collaboration tools.
- Employee lifecycle nudges (onboarding tasks, probation reminders, IDP progress).
📊 Workforce Analytics & Insights
- Predictive attrition modeling to identify flight-risk employees.
- Workforce planning (headcount forecasting, skill demand prediction).
- Compensation benchmarking using market data and internal patterns.
📝 Performance & Productivity
- AI-supported performance review insights from goals, OKRs, and project data.
- Workload and burnout risk prediction using activity patterns.
- Intelligent goal setting aligned with business priorities.
🧾 HR Operations & Compliance
- Document automation (offers, contracts, letters).
- Policy compliance monitoring via automated audit checks.
- RPA + AI workflows for payroll, reimbursements, onboarding tasks.
2. AI usecases across various Industries
Healthcare Industry (for an illustration)
- AI for Drug Discovery (Primary and Secondary Drug Screening, Drug Design, Planning, and Automation of Chemical synthesis)
- AI for Clinical trials,
- Patient Care (AI-based tools),
- Medical Diagnostic procedures
- Hospital Management
3. Generative AI usecases
While the above sections covered usage of AI (including ML models) in various business functions in an organization and across various industries, Generative AI has a set of unique usecases which were not imaginable prior to its arrival.
The various ways in which we can use Gen AI are grouped into logical categories below to give a comprehensive set of possibilities.
a) Generation from a Prompt (Text-to-X)
This is the most well-known type of generative AI, where a text prompt is used to create a completely new piece of content.
- Text to Text: The model takes a text prompt and generates a new text-based response. This is the core function of most chatbots and large language models (LLMs).
- Text to Data: The AI can generate structured data like tables or JSON from a text prompt. For instance, you could ask, “Create a table of the top 5 largest cities in the world with their population,” and it would generate a formatted table.
- Text to Image: A user provides a text description (e.g., “a cat wearing a spacesuit”) and the model generates a unique, original image.
- Text to 3D: This is a rapidly developing area where a text prompt is used to generate 3D models, scenes, or textures. This is revolutionizing fields like video game development and virtual reality.
- Text to Video: Similar to text-to-image, this generates short video clips or animations from a text description.
- Text to Audio: This can generate music, sound effects, or a synthetic voice reciting a script based on a text prompt.
- Text to Code: This is a very common use case. A user provides a natural language description (e.g., “Write a Python function to sort a list of numbers”), and the model generates the corresponding code.
b) Understanding and Description (X-to-Text)
In these cases, the AI analyzes a non-text input and describes it in text.
- Image to Text: The AI can describe the contents of an image, such as generating a caption or identifying objects within it.
- Video to Text: This involves analyzing a video to provide a summary of the events, transcribe the dialogue, or describe the visual elements.
- Audio to Text: This is a transcription service, converting spoken words in an audio file into written text.
- Code to Text: The reverse is also possible. The AI can analyze a block of code and generate documentation, comments, or a natural language explanation of what the code does. This is helpful for understanding complex or legacy code.
c) Transformation (X-to-Y)
This type of generative AI transforms existing content from one format to another.
- Image to Image: A model can take an image and apply a new style or filter, such as turning a photograph into a painting or changing the appearance of a character in a video game.
- Video to Video: This can involve altering the style of a video (e.g., turning a regular video into an animated cartoon) or performing more advanced manipulations like deepfakes.
- Audio to Audio: This can be used for tasks like voice synthesis, where a model learns a person’s voice and can then generate new speech in that voice.
- Code to Code: This involves refactoring, translating, or optimizing code. For example, a model could convert code from one programming language to another (e.g., Python to Java) or improve a piece of code for better performance.
- Image to 3D: A model can take a 2D image and generate a 3D model of the object shown in the image.
Illustrations of few GenAI Usecases
The types of GenAI usecases are shown to be many. More types can also emerge with technology. The usage of these types of GenAI usecases, is ever increasing and being adopted by various industries in various ways. Adding to that the tools are simplifying the usage of GenAI usecases on an ongoing basis. Hence, it is a daunting task to illustrate GenAI Usecases comprehensively. The page GenAI Usecases Illustration shows a partial list of illustrations and will be updated regularly.
Dr. Selvaraj Vadivelu 