Find AI opportunities that match the work your team actually owns.
Use Case Foundry helps finance, operations, revenue, support, and people leaders separate grounded pilots from generic AI proposals — with approval gates, success metrics, and a runnable first scaffold for what's worth piloting.
Why functional AI plans miss the mark
The common failure mode is not a lack of AI ideas. It is a lack of company-specific evidence for choosing which ideas deserve attention.
- Ideas arrive disconnected from real workflows, exceptions, and bottlenecks.
- Teams are asked to fund pilots before data access and operational risk are clear.
- Generic use-case lists ignore scarce expertise, local constraints, and existing systems.
- There is no scoped pilot plan — just an idea and an expectation that someone will build it.
What functional sponsors get
Use Case Foundry keeps the conversation grounded in workflows, data, pains, constraints, feasibility, moat, and evidence quality.
A function-specific opportunity shortlist tied to pains, workflows, data, and owners.
A clear view of what can be piloted now versus what needs instrumentation first.
Discovery questions that help your team close the gaps that actually change priority.
A scoped pilot package — cohort, sample data, rollout steps, kill criteria — once an opportunity is ready.
A sponsor-ready view for one function
Start with the function's process pains and available evidence. The roadmap shows which AI bets are grounded enough to pilot and which need more data or guardrails, then produces the pilot package for the ones ready to move.
Pilot the grounded workflow
Start where pain, data access, and human review make the first experiment credible.
Invest where the company has advantage
Prioritize candidates backed by proprietary data, scarce expertise, or reusable abstractions.
Close what changes the decision
Turn missing facts into interviews, metadata checks, redacted samples, or instrumentation work.
Why the recommendations are easier to defend
The method makes the reasoning inspectable before budget, pilots, or diligence decisions depend on it.
Department lenses shape prompts without overriding the underlying company evidence.
Workflow names and linked pains unlock domain orchestration, physical, analytics, and software-stack operators.
Human review controls which facts enter the model before recommendations change.
Write-tool actions stay behind a named human-approval owner at every stage.
Built for sensitive evidence work
Use public pages, notes, metadata, aggregates, redacted samples, or controlled LLM endpoints. Human reviewers decide which facts enter the model.
Private discovery without forcing raw records into the workflow
Auto-map and evidence extraction propose changes, but scoring only changes after a reviewer accepts the facts.
Go deeper with resources
Review sample outputs, comparisons, and methodology pages to evaluate fit before a pilot.
Demand intelligence method
How public pressure sharpens a function-level discovery agenda.
Sample support roadmap
Prioritize resolution outcomes over generic chatbot ideas.
Discovery interview guide
Workflow and data prompts for function-level discovery.
FAQ
Answers on data readiness, outputs, and team fit.
Agent pilot plan
What a scoped, evidence-gated pilot package includes.
Build an AI roadmap you can defend
Use a sample company now, or reach out to discuss the assessment workflow for your team.