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Read More →Download the generative AI use cases PDF from uncodemy: uses by business function, risks and controls, and a framework for a first project.
This page offers uncodemy's generative AI use cases guide as a PDF download. It lists practical uses by business function, explains what generative AI is good and poor at, and gives a simple framework for choosing and measuring a first use case.
Start with the main use cases PDF. The extra cards below are for any companion files uncodemy publishes on this topic — download only what you need.
Uses by business function — support, marketing, sales, HR, finance, engineering, legal, education and analytics — with the human role for each. Includes the recurring patterns, risks and controls, a framework for picking a first use case, example scoring tables, how to measure success and change-management notes.
An optional second file on the same topic. Download this only if uncodemy publishes an additional companion PDF to the main use cases; otherwise skip it and use the use-case tables on this page instead.
An optional third file on the same topic. Same rule: download it only if uncodemy publishes an extra PDF that genuinely fits this topic; otherwise use the use cases and framework on this page.
Each download is a separate file. Show only as many cards as there are real PDFs — each with its own title, description, page count, size and file.
Generative AI systems produce new text, images, code, audio or structured output from instructions. They are strong at drafting, summarising, rewriting, translating, classifying messy text, extracting fields from documents and answering questions in natural language.
They are weaker at tasks that need guaranteed accuracy, precise arithmetic without tools, up-to-date facts they were not given and decisions with legal or safety consequences. The most successful business uses keep a person responsible for the result and use the model to save time on the first draft, the search or the routine step.
Treat the examples below as patterns to adapt, not guarantees of savings.
| Function | Example use case | Human role |
|---|---|---|
| Customer support | Draft replies from a knowledge base; summarise long ticket threads; route tickets | Agent reviews and sends |
| Marketing | Draft campaign variants, product descriptions and social posts in a defined style | Marketer edits, checks facts and brand rules |
| Sales | Summarise calls; draft follow-up emails; prepare account briefings | Salesperson verifies details |
| HR and recruiting | Draft job descriptions; summarise policy questions | HR checks compliance and avoids automated decisions on candidates |
| Finance and operations | Extract fields from invoices; summarise reports; explain variances | Analyst validates numbers |
| Software engineering | Code completion, test generation, documentation, explaining legacy code | Developer reviews and tests |
| Legal and compliance | Summarise contracts; compare clauses; first-pass document review | Lawyer reviews all outputs |
| Education and training | Create practice questions, explanations, personalised study plans | Instructor checks accuracy |
| Data and analytics | Natural-language questions over data; explanation of charts; SQL drafting | Analyst checks queries and results |
| Pattern | How it works | Example |
|---|---|---|
| Draft and review | Model writes a first draft; a person edits | Support replies, marketing copy |
| Summarise | Model condenses long text | Meeting notes, long reports |
| Extract | Model pulls structured fields from messy text | Invoices, resumes, forms |
| Classify and route | Model labels text into categories | Ticket triage |
| Search and answer | Model answers questions from your documents with citations | Internal knowledge assistant |
| Transform | Model converts format, language or tone | Translation, rewriting |
| Act with tools | Model calls functions under supervision | Look up an order and draft a response |
| Risk | What can happen | Control |
|---|---|---|
| Hallucination | Confident but wrong statements | Ground answers in sources, show citations, human review for important output |
| Data privacy | Sensitive data sent to a provider or leaked in outputs | Data classification, redaction, vendor terms, access control |
| Bias | Unfair or skewed outputs | Test across groups, keep humans in decisions about people |
| Prompt injection | Documents or users override instructions | Treat external text as untrusted, limit tool permissions |
| Intellectual property | Unclear rights over inputs or outputs | Policy and legal review |
| Cost growth | Usage grows beyond the budget | Budgets, caching, smaller models for simple tasks |
| Task | Value | Risk if wrong | Data ready? | Start here? |
|---|---|---|---|---|
| Summarise internal meeting notes | Medium | Low | Yes | Yes |
| Draft support replies from the help centre | High | Medium | Yes | Yes, with review |
| Decide loan approvals | High | Very high | Partly | No, not as an automated decision |
| Generate legal advice for customers | High | Very high | No | No |
| Measure | Before (baseline) | After (pilot) | How measured |
|---|---|---|---|
| Time to draft a support reply | measure | measure | Timed sample of 50 tickets |
| Edit rate of drafts | Not applicable | measure | Share of drafts changed before sending |
| Customer satisfaction on pilot tickets | measure | measure | Existing survey |
| Cost per ticket | measure | measure | Finance data plus model cost |
Many pilots fail not because of the model but because people are not trained, do not trust the output or find the tool disconnected from their workflow. Involve the people who do the work from the start, explain what the tool can and cannot do, show how to check results quickly and collect their feedback. Agree who owns the tool, who answers questions and what happens when it is wrong.
Ready to apply generative AI inside a real business workflow? Explore uncodemy's Generative AI / Agentic AI course and learn from industry trainers.
