Prepare
Which foundations have to be in place before scale?
Most organizations believe they are more AI-ready than they are. The gap between leadership confidence and operating reality is where programs stall, and AI does not close that gap; it exposes weak data, fragmented processes and thin talent, then amplifies them. Four foundations have to hold before a portfolio is viable: data that is governed, accessible and traceable; processes documented and stable enough for a system to learn from; a talent portfolio that covers program, governance and translation roles as well as data science; and leaders who carry AI outcomes rather than sponsor pilots.
Readiness is half the discipline; the other half is choosing what to build. Where AI gets deployed and where value is generated usually differ, so prioritize by value, concentrate on a few use cases, and put production funding, change management and operations in place before a successful proof of concept is left stranded. What holds most organizations at the experimenting stage is organizational, not technical.
The Readiness Diagnostic lives here: five dimensions, twenty-five statements, a score and a focus area in five minutes.
AI Readiness
A four-dimension readiness assessment with scoring bands, red flags, and what to do with the result before you scale.
Data Readiness
How to assess data quality, accessibility, governance and lineage per use case, and the three investments that move the needle.
Process and Talent Readiness
The two readiness dimensions most assessments skip: whether processes are stable enough for AI, and whether the talent portfolio goes beyond data science.
Maturity Model
Five levels from Exploring to AI-Native, with indicators per level and the transition triggers that actually move an organization forward.
Use Case Prioritization
A weighted scoring framework, kill criteria and a three-horizon portfolio structure for choosing which AI use cases to fund, sequence or stop.
Value Concentration
Why a few deeply resourced use cases outperform broad experimentation, where AI value sits by function, and how to make the concentration decision.
Pilot to Production
A stage-gate framework with decision criteria, artifacts and approvers, and the funding, change and operations capability pilots need to reach production.
Readiness Diagnostic
Five dimensions, twenty-five statements, one scorecard you can put in front of the leadership team.
Assessment Checklists
Five checklists with named assessors and evidence: readiness, pilot launch, production gate, agent deployment and board reporting.
Decision Artifacts
Worked board memo, investment scorecard, model inventory, phase-gate review and risk classification worksheet, each with a downloadable template.