Implementation handoff clarity

Use Case Foundry before Azure AI Foundry

Azure AI Foundry helps build and operate AI systems. Use Case Foundry helps decide what deserves to be built first, then turns workshop evidence into a roadmap, evaluation plan, and handoff package your delivery team can use.

1

Run the discovery and prioritization work before committing engineering capacity.

2

Export a build-ready handoff with workflow, evidence, evaluation, and human-control boundaries.

3

Keep the deployment boundary explicit: Foundry does not create credentials or push to your systems.

These tools belong in sequence, not in conflict

Use Case Foundry is for the decision work before implementation: which workflow matters, what evidence supports it, what makes it defensible, what approvals apply, and what evaluation or pilot boundary should exist. Azure AI Foundry is for the build and runtime work after that decision is made.

A practical sequence

  1. Run a use-case workshop or assessment in Use Case Foundry.
  2. Review the evidence-backed roadmap and reject generic ideas early.
  3. Choose one candidate and define the blueprint, evaluation plan, and pilot boundary.
  4. Hand the package to the Azure delivery team for architecture, model, and environment work.

What the handoff should already contain

  • The workflow and business objective.
  • The evidence behind the priority.
  • The data and system assumptions.
  • The evaluation cases and pass thresholds.
  • The human-approval boundary and unsafe-action rules.

What this page is not claiming

Use Case Foundry is not Azure AI Foundry, and it does not replace implementation tooling. It helps teams arrive at Azure with a better problem definition and a more credible handoff.

Side-by-side comparison

How evidence-backed roadmap assessment differs on the dimensions buyers care about.

Criterion Use Case Foundry Azure AI Foundry
Primary job Choose which AI use case deserves a build and what must be true first Build, orchestrate, and evaluate the chosen AI system
Best starting point Strategy workshop, diligence, transformation review, or advisor-led assessment After a use case, architecture, and evaluation approach are chosen
Output Discovery brief, ranked roadmap, eval plan, and build handoff Implementation environment, model/runtime tooling, and deployment workflows
Human control boundary Approval, evidence review, and pilot readiness are explicit from the start Execution tooling assumes the team has already defined the use case and controls

Related resources

Continue exploring methodology, samples, and practical assessment assets.

AI discovery workshop

See how the use-case workshop is structured before implementation starts.

Transformation roadmap

Review how candidate use cases are sequenced and selected.

Agent deployment readiness

See the gates between blueprint, eval, pilot, and production handoff.

Ready to apply this to your own AI roadmap?

Use a sample workspace now, or contact us to discuss your assessment workflow.

See the workshop flow Review deployment readiness