Direct answer
Use this finder to shortlist AI use cases by industry, function, and scale before spending time on architecture or vendor discovery.
Fintech operations team
Input: Fintech, operations, 100 to 1,000 employees.
Output: The output should rank document intake, exception routing, support triage, and knowledge-assistant patterns.
How to use this tool
- 1. Pick your industry.
- 2. Choose business function and company scale.
- 3. Filter ranked use cases.
- 4. Shortlist one high-ROI, low-friction pilot.
Why generic AI use-case lists fail
Generic lists ignore data availability, regulatory pressure, workflow volume, and operational ownership. A use case that works in ecommerce support may be dangerous in healthcare triage.
This finder ranks use cases by industry, function, scale, expected ROI band, and implementation difficulty so the shortlist starts closer to reality.
How to validate a use case
Validate volume, data access, human review burden, downside risk, and path to deployment before investing. The best first use case is rarely the flashiest one; it is the one where quality can be measured and failure can be contained.
Assumptions and methodology
This tool uses transparent browser-side calculations and curated assumptions rather than LLM-generated recommendations. Outputs are planning estimates. They should be validated against provider pricing, production traces, engineering quotes, or domain review before money, compliance, safety, or hiring decisions are made.
Numerical defaults are dated and surfaced on the page. The methodology favours explicit assumptions over false precision: every estimate is meant to expose the variable that drives the result, not to pretend that early planning data is exact.
Turn the result into an implementation plan
Bring the scenario to a strategy call and I will pressure-test the workflow, assumptions, failure modes, and delivery path.
Book a strategy callFrequently asked questions
- How are use cases ranked?
- Use cases are ranked by fit across industry, function, scale, expected ROI, effort, and implementation pattern. The ranking is directional and should be validated with your own data.
- What does effort mean?
- Effort combines data readiness, integration complexity, governance burden, and delivery scope. Small effort means a focused pilot can be built with limited dependencies.
- What is ROI band based on?
- ROI band reflects plausible savings or revenue impact relative to implementation effort. It is not a guarantee because actual ROI depends on volume, adoption, and operating cost.
- Are these AI-generated?
- The patterns are hand-curated in code for this implementation. Future versions should move the library into a cited JSON dataset with review dates per use case.
- Why is my industry missing?
- The first version focuses on common digital and operations-heavy industries. Adjacent patterns can still apply, but regulated or niche sectors need additional review.
- Should I trust ROI bands?
- Trust them as a shortlist filter, not a business case. Use the ROI calculator and a pilot measurement plan before committing budget.