Most CTOs I mentor ask a version of the same question: we bought the AI seats, is it working? The ladder has five rungs above zero. Level 0, still debating. Level 1, licenses bought. Level 2, AI code in production. Level 3, the bill climbs. Level 4, you can say whether it is worth it. Level 5, you ask whether you are the best.

Each rung has its own mistakes: the split team at level 1 (a few already paying out of pocket, most experimenting, the rest scared it replaces them), the review backlog at level 2, the $20k weekend agent at level 3. Navigara's delivery data across 500 open-source projects shows the shape of the climb: a 12% dip in engineering performance during the experiments, +104% per developer once it works, then a flat line while spend keeps rising. We go rung by rung and you place your own company on the ladder, with the two KPIs that prove it.

You walk out with a five-level KPI table: two numbers to track per level, and four vanity numbers to stop reporting (seat utilisation, acceptance rate, share of AI-written code, self-reported speed).

Full page with data and sources: https://www.marian.coach/speaker/5-levels-of-ai-adoption/

45-60 min keynote or 90-min leadership workshop. For CTOs, VPs of Engineering, Heads of AI. Delivery data from Jirka Bachel (Navigara) at ELC meetup #39, credited on the slides.