AI Agent Use Case Discovery & Prioritization Workbook

📋 What It Is

A 6-tab decision workbook with 197 live formulas and 31 guided dropdowns that takes you from "AI agents could probably help somewhere" to a specific, risk-adjusted, ROI-justified decision about what to build first. This isn't a brainstorming template. It's a structured evaluation instrument that scores use cases across 10 feasibility and impact dimensions, assesses 7 risk dimensions (including 3 agent-specific failure modes), and produces a ranked recommendation with a preliminary ROI estimate.

Most agent projects don't fail because the team picked bad technology. They fail because the team picked the wrong problem — one that was too risky for the autonomy level, too dependent on integrations that didn't exist, or too low-impact to justify the investment. This workbook forces that confrontation early, when changing direction is free.

The Discovery tab uses 10 agent-specific opportunity patterns to surface problems that specifically need the Sense → Think → Plan → Act loop. The Scoring tab uses descriptive dropdowns so you know exactly what a "3" means. The Prioritization Matrix applies a risk multiplier so a high-scoring use case with high risk doesn't blindly get a "Pursue" recommendation. And the Selection Brief auto-calculates ROI including error costs, with a health check that flags when the math doesn't support building.

👥 Who It's For

• Founders and solopreneurs deciding where AI agents can give them leverage — and whether the ROI justifies the build-vs-buy decision
• Product managers and team leads evaluating multiple candidates and needing a defensible prioritization, not just gut feel
• AI engineers and architects assessing technical feasibility, integration complexity, and autonomy fit before committing
• Hackathon teams selecting a project that's both impressive and achievable within the time constraint
• Enterprise strategists building a pipeline of agent initiatives ranked by value, feasibility, and risk
• Anyone reading the book who wants to move from concepts to action — this bridges "I understand agents" and "I know what to build"

⏱ When to Use It

• Before your first agent project — when you know agents could help but haven't identified the specific use case
• When evaluating multiple ideas — 5–10 candidates and need to pick the one balancing impact, feasibility, and risk
• At a hackathon kickoff — go from "what should we build?" to "here's exactly what and why" in 30–45 minutes
• During quarterly planning — re-prioritize your agent initiative pipeline as conditions change
• After a failed project — diagnose whether the problem was use case selection, risk assessment, or ROI assumption
• When presenting to stakeholders — the Selection Brief produces a one-page decision document

📦 What It Produces

• Candidate Pipeline — 5–10 use cases surfaced through 10 agent-specific discovery patterns, each tagged P1–P10
• Weighted Feasibility & Impact Scores — scored across 10 dimensions with auto-calculated composites and rank ordering
• Risk Profile with Autonomy Mapping — 7 risk dimensions including Hallucination Risk, MCP Fragility, Context Drift. Auto-maps to: Full Autonomy, Supervised, or Human-Required
• Risk-Adjusted Prioritization Matrix — risk multiplier (Low=1.0×, Medium=0.85×, High=0.70×) with risk-aware recommendations
• Selection Brief with ROI — problem statement, agent approach, tool chain, error-inclusive cost, payback period, ROI multiple, GO/NO-GO decision
• ROI Health Check — 5x+ compelling, 3x–5x solid, 2x–3x marginal, below 2x not supported

🚀 How to Use It — Quickstart

• Step 1. Start with How to Use. Read the scoring direction guide and 17 dimension definitions. 5 minutes prevents all confusion.
• Step 2. Switch to Discovery. Work through 10 agent-specific opportunity patterns. Consolidate best answers into the candidate summary.
• Step 3. Open Scoring. Names auto-populate. Select scores from descriptive dropdowns across 10 dimensions. Composites and ranks auto-calculate.
• Step 4. Move to Risk Assessment. Top 5 auto-populate by rank. Score 7 risk dimensions. Risk Level and Autonomy Recommendation auto-calculate.
• Step 5. Review the Prioritization Matrix. Risk-adjusted scores, adjusted ranks, and risk-aware recommendations — all auto-generated.
• Step 6. Complete the Selection Brief. Fill problem statement and ROI inputs. Annual cost, payback, ROI multiple, and health check auto-calculate.

👁 Preview — What's Inside

6 Tabs, 197 Live Formulas, 31 Guided Dropdowns

10 Agent-Specific Discovery Patterns

Risk-Adjusted Recommendation Engine

📝 Version History