5 Levels of AI Adoption. Which One Are You On? Prove it.

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We give you a full explanation of the KPI Framework.

Do You Measure the Most Valuable KPIs? Red/Amber/Green Thresholds for Each of Your Levels Explained.

Situation You burn $$$ on AI tokens. You run hackatons. Git repo full of AI pilots.

You’re spending more on AI than last year.

Yet, you can’t shake the feeling that you’re paying faster than you’re shipping.

📣 We give you the manual to pick the top KPIs that demonstrate the value, based on the context of how far you are in AI adoption.

3 CTOs share where they are on the AI adoption ladder, what worked, what they’d undo, and what comes next.

✅ The 3 KPIs you should be tracking at each level. And the ones to stop tracking.

The 5 levels of AI adoption, mapped from engineering orgs.

1️⃣ L1: Nothing in production. How to leave it without burning two quarters on pilots.

2️⃣ L2: Talent: upskilling, reskilling and firing.

3️⃣ L3: Proof: When the cost curve overtakes the delivery curve, and how to calculate AI efficiency proof?

4️⃣ L4: Going AI-native: What concretely changes when AI moves from IDEs into production.

5️⃣ L5: You’ve won the AI race. Where’s the new edge in 2027?

Bring your situation. By the end you’ll know which level you’re on, what is your KPI, what your next move is, and what to stop doing this quarter.

No hype, no AI gospel. Two practitioners, real experience, with answers.