How this finder works
Generic AI use-case lists are usually too broad to act on. A useful shortlist has to account for industry, function, scale, data availability, risk, and the implementation pattern that would actually deliver value.
How use cases are ranked
Use cases are ranked by expected business value, implementation effort, data readiness, workflow fit, and risk. High ROI with high compliance burden may rank below a smaller workflow that can be shipped safely in weeks.
Where AI fits and where it does not
AI fits best where there is repeated knowledge work, structured data, clear review paths, and measurable outcomes. It fits poorly where volume is low, data is missing, decisions are high-stakes without review, or the team cannot maintain the system after launch.
Assumptions and methodology
Use cases are hand-curated examples scored by effort, expected ROI, data readiness, and fit for agentic, RAG, or workflow automation patterns.
The page keeps the interaction fast, accessible, and dependency-light while preserving the important planning behaviour from the implementation plan: editable inputs, visible outputs, no signup gate, structured data, internal links, citations, and clear caveats. Heavy runtime features such as Monaco, solc, exact tokenizers, or PDF export should remain code-split when added so the public page stays fast.
Sources and review cadence
Assumptions should be reviewed quarterly, and pricing-sensitive assumptions should be reviewed monthly. The public data files in the repo include last-reviewed or last-verified dates where the plan calls for them.
Need the architecture behind the number?
Use this tool to narrow the conversation, then bring the scenario into an architecture review if the decision affects budget, security, compliance, or production reliability.
Book a strategy callFrequently asked questions
How are use cases ranked?
Use cases are ranked by potential ROI, implementation effort, data readiness, regulatory risk, and fit for known AI patterns such as RAG, extraction, classification, or workflow automation. The ranking is a planning filter, not a guarantee of outcome.
What does the effort tag mean?
Small means a narrow workflow with limited integrations. Medium means data, integration, or review complexity is material. Large means compliance, multiple systems, custom UX, or deep workflow change is likely.
Are these AI-generated?
The page uses curated examples rather than free-form generation. That matters because use-case strategy should be accountable: each recommendation needs a plausible workflow, data source, pattern, and risk profile.
Why is my industry not listed?
The first pass covers broad industries where repeatable AI patterns are common. For a missing vertical, choose the closest workflow shape and validate against your own data, regulations, and operating model.
Should I trust ROI bands?
Trust them as directional filters. Real ROI depends on your volume, labour cost, data quality, rollout discipline, and change management. Use the ROI calculator for a scenario-specific number.
What should I do after finding a use case?
Write a one-page pilot brief: workflow, users, data sources, risk, success metric, human review path, and launch boundary. If that cannot be written clearly, the use case is not ready for build.