AI Use-Case Discovery Interview Loop

By Juan Beltrán — personal website on AI and digital growth for complex B2B industries.

What AI use cases are hidden inside this business process? Use this after interviewing a process owner or reviewing a workflow to identify practical AI opportunities without jumping straight to tools. AI Use-Case Discovery Interview Loop Task: What AI use cases are hidden inside this business process? Context: [Paste your notes, excerpts, draft, meeting transcript, CRM fields, proposal text, public research, or examples here.] Context I should provide: - Interview notes - Workflow steps - Pain points - Repeated decisions - Documents used - Handoffs - Known constraints Useful setup: Paste interview notes, workflow steps, pain points, repeated decisions, handoffs, documents used, and known constraints. Why this matters: Use this after interviewing a process owner or reviewing a workflow to identify practical AI opportunities without jumping straight to tools. Business problem: AI use-case lists are weak when they start with technology instead of repeated friction, decisions, handoffs, and evidence gaps. Instructions: Act as an AI use-case discovery partner. Review the process notes below. Extract repeated friction, decisions, handoffs, and documents. Propose practical AI use cases with user, input, output, value, risk, data needed, and first test. Reject ideas that are too vague. Workflow: 1. Find repeated friction: Identify steps where people repeatedly search, summarize, decide, route, draft, reconcile, or check. 2. Translate friction into user jobs: Name who needs help and what decision or output they need. 3. Draft use cases: Write practical use cases as user, task, input, output, value, and risk. 4. Score realism: Classify each idea by data availability, workflow fit, risk, and first-test effort. 5. Pick first tests: Recommend two or three safe experiments that create learning quickly. Quality bar: - Use only the context in this chat. - If important information is missing, ask for the minimum missing context before giving a final recommendation. - Separate facts from assumptions. - Do not invent customer facts, benchmarks, financial numbers, policy approvals, or system access. - Keep the answer useful for Transformation Lead. Output: A shortlist of use cases mapped to friction, user, value, data needed, risk, and first test. - BLUF recommendation or draft. - Evidence from my context. - Assumptions and missing information. - Risks, objections, or failure modes. - Recommended next action, owner, and stop condition. Evidence checklist: - Process step - User - Repeated pain - Input material - Expected output - Risk - First test Stopping condition: Stop when each shortlisted use case has a user, input, output, and first test.

Canonical URL: https://juanbeltran.ch/operating-loops/ai-use-case-discovery-interview-loop