1. Discovery & Strategy¶

1. Purpose¶

Before investing in an AI solution, you need to be certain you are tackling the right problem. This phase focuses on problem articulation, data evaluation, and risk inventory. By the end, you will have a clear picture of whether AI is the right approach — and if so, under what conditions.

Entry criteria: A business sponsor, a problem that is not trivially solvable, and willingness to share data.

2. Components¶

• Overview & Objectives — What this phase aims to achieve and when you are ready
• Activities — Problem articulation, data evaluation, risk inventory, project type classification
• Deliverables & Gate 1 — What you deliver and the Go/No-Go criteria
• Fast Lane — Accelerated track for minimal-risk projects (Mode 1–2)
• Collaboration Mode Assessment — Determine the right human–AI collaboration mode

3. Common pitfalls¶

• Jumping to a solution too quickly — invest time in problem articulation before choosing a model
• Skipping data evaluation — without quality, accessible data, any AI project is doomed
• Identifying risks late in the project — the Risk Pre-Scan belongs in week 1
• No sponsor commitment — without mandate and budget, the project dies after the first gate

Next step: After Gate 1 (Go/No-Go), proceed to Phase 2 — Validation. → See also: Phase 1 Templates for the associated templates.