An AI use case library answers the most common question leaders ask: “where would AI actually help us?” Rather than abstract hype, this tool lists concrete applications of AI organised by industry and business function, each tagged as proven or emerging, with a suggested tool category. It is a fast way to move from “we should do something with AI” to a shortlist of pilots worth scoping.
How it works
The library is a curated dataset of use cases spanning industries such as healthcare, finance, retail, and logistics, and functions such as customer service, operations, marketing, and HR. You filter by industry, function, and maturity (proven or emerging), and the list narrows instantly. Each entry includes a one-line description and a suggested tool category — for example “document extraction” or “RAG chatbot” — chosen so the guidance does not go stale as specific products change. All filtering happens in your browser; nothing is sent anywhere.
How to read the maturity tags
Proven means the use case is in widespread production across many organisations, with established tooling, documented ROI patterns, and a mature vendor ecosystem. Pilots here are low-risk. Implementation complexity varies, but there is no fundamental uncertainty about whether the technology works.
Emerging means the use case is real and credible but less established — either the tooling is maturing, the ROI evidence is thin, or the implementation requires capabilities most teams do not yet have. Worth exploring with a small, time-boxed pilot rather than a production commitment.
The tool category field
Each use case lists a tool category rather than specific vendor names. This is deliberate: vendor names go stale as the market consolidates and new entrants appear. “RAG chatbot” remains a useful category even when the leader in that space has changed three times. Use the category to guide your vendor search rather than treating it as a recommendation.
Common categories you will encounter:
| Tool category | What it does |
|---|---|
| RAG chatbot | Answers questions from your documents using retrieval-augmented generation |
| Document extraction | Reads structured data from unstructured documents (PDFs, invoices, forms) |
| Code assistant | Suggests, reviews, and explains code inline in a developer IDE |
| Classification | Labels or routes items — tickets, emails, transactions — at scale |
| Summarisation | Condenses long documents or call transcripts to key points |
| Generative content | Produces first drafts of copy, descriptions, or reports from a brief |
| Anomaly detection | Flags unusual patterns in time-series data — fraud, failures, drift |
Tips and examples
Begin with proven use cases in a function where you already have clean, accessible data and a cost you can measure — that is where AI pays back fastest and the implementation risk is lowest. Treat emerging entries as experiments: worth a small, time-boxed pilot, not a year-one bet-the-company programme. Once you have a shortlist of two or three, run each through the AI ROI business case builder to put numbers behind the idea before you ask for budget. Revisit the library each quarter — the line between “emerging” and “proven” moves quickly, and use cases that were risky last year are often standard practice now.