Introduction
Most organizations already have AI activity before they start a formal transformation. Scattered pilots, shadow ChatGPT usage, vendor tools bolted onto legacy systems, an "AI suite" that nobody uses. Without a clear inventory, transformation programs duplicate effort, underestimate complexity, and miss signals about what already works.
This exercise produces the evidence base that feeds the Current State Canvas and the Transformation Vision Canvas. It is also often the single most useful conversation opener with a new client: before we discuss where we are going, let us agree on what you already have.
Phase | Map |
Lens | Architecture |
Purpose | Inventory existing AI activity, classify maturity, decide what to continue, pivot, or kill |
Output | Initiative inventory with ladder classification, lens scores, and continue/pivot/kill decisions |
Who's Involved | Transformation lead, architecture lead, one representative per function running AI |
Duration | Half-day workshop plus one to two weeks of interviews and data collection |
Steps
Collect every AI-related initiative: official projects, pilots, shadow usage, vendor tools, internal experiments. Do not filter; collect first.
Place each initiative on the AI Maturity Ladder (Thought Partner, Assistant, Teammate, System).
Score each initiative on the three lenses (1 to 5): architecture readiness, operations integration, experience quality.
Cross-check claimed adoption against evidence (logs, interview notes, workflow data). Flag initiatives with no evidence.
Make a Continue, Pivot, or Kill decision for each initiative, following the BOI rationalization logic.
Summarize findings on a single page ready to feed the Current State Canvas.
Template
Coming Soon