A diagnostic that scores your org on five independent dimensions, names the anti-stages most maturity models hide, and ends with a 30-minute artifact review you can run without a consulting engagement.
Most first AI picks fail because the workflow was wrong, not the model. Score risk, value, and signal quality as separate axes. Treat your first three pilots as three different questions about the organization. Pick boring. Pick measurable. Pick diverse.
A week-by-week operating plan for the new VP of AI, CAIO, or CTO who just inherited a transformation mandate. Stakeholder map, named failure modes, the quick-win shortlist, and the board brief that earns a second 90 days.
Most AI use case selection is workshop theater. Process mining reads the actual event logs and ranks workflows by volume, variance, and structure — so you find out whether you need an LLM, an RPA bot, or nothing before spending a dollar.
Push automation onto an absent substrate and you get usage numbers without capability. Four layers — Literacy, Sandbox, Playbooks, Feedback Loops — a scored readiness rubric, and the sequencing rhythm that holds after the mandate memo fades.
$3.48M vs $610K revenue per employee. The gap is not measuring AI cleverness — it is measuring how much of traditional engineering headcount was scaffolding for slow handoffs. A role-by-role rebuild for the math you cannot escape.
Most enterprise AI lives between pilot and replacement. Five patterns for the 12-18 months it actually takes — strangler fig, sidecar, parallel run, dual-write, eval-based rollback — with the rollback signals that catch silent quality drift.
Agentic tools push engineering past 2–3x velocity and product definition becomes the binding constraint. Hiring more PMs makes it worse. The fix is a three-tier decision rights model that moves authority to where the information actually lives.