Your product and engineering teams speak different languages about AI.
Your engineers are three model releases ahead. Your PMs are still writing user stories. The gap is not talent. It is operating model. We close it in 8 weeks.
Does this sound familiar?
The patterns we see in every engagement
- 01Your PMs write user stories. Your engineers build AI features. Nobody has written an executable spec in 18 months.
- 02The team has shipped AI demos your leadership loves. None of them scaled past 100 users.
- 03Product and engineering disagree on what the problem is. One side wants better tools, the other wants better specs. Both are right.
- 04DORA shows individual productivity up 21% and organizational delivery flat. Code review time is up 91%. Nobody on the business side understands why.
- 05You have 8 to 12 disconnected AI tools across the org. No two teams use the same stack.
- 06Every AI feature lifecycle is a surprise: the model works, then it doesn't, and nobody has an eval framework to explain why.
What's at stake
The cost of the product-engineering AI gap
Every AI feature that ships and rolls back costs roughly $240K in rework. Most of that cost is not engineering time. It is the spec work that was never done, the evals that were never built, the context design that was never considered. The CPO or CTO who closes this gap becomes the strategic owner of AI in the business. The one who does not is, within a year, no longer running AI.
How we work with you
How we close the gap
Two paths, both designed to bring product and engineering into the same operating model. The 2-week AI Delivery Diagnostic gives you a shared diagnosis: where the gap actually is, which layers of the AI Failure Stack are breaking, and what to fix first. The 8-week AI PM Ladder Sprint puts your product team through 10 modules on the Kairos platform, each producing a reusable artifact. Your engineers stop re-explaining RAG. Your PMs start writing specs engineers can ship from.
- →Shared diagnosis in 2 weeks: AI Failure Stack mapping, Maturity Model placement, 90-day roadmap
- →8-week Sprint: 10 modules, 3-4 hours/week per participant, production-ready artifact
- →Three tracks: Team (4-12 PMs), Executive (CPO/CTO half-day workshops), Embedded (co-work on a real feature)
- →Kairos platform: Prompt Lab, Eval Suite, RAG Builder, Orchestrator across Gemini, GPT-4, Claude, Grok
- →Spec-Driven Development methodology designed for probabilistic systems
The framework that anchors this work
The AI Failure Stack
Six layers. Most teams diagnose at Layer 3-4 (model and output). The actual root causes are almost always at Layers 0-2 (operating model, spec, context). When product and engineering share this diagnostic, they stop arguing and start fixing.
See the full framework →“When your AI investment underperforms, the fix is a leadership decision, not a vendor decision.”
What people tell us
Proof, not claims.
We were able to achieve real progression very quickly in just one day. Many things we learned are being used in day-to-day collaboration now.
The session was practical and inspiring, showing how generative AI tools can support product work with clear takeaways I will apply right away.
The session helped me reflect on the most repetitive tasks in my daily work and gave me clear tips on which generative AI tools I can use. We worked hands-on and also addressed security concerns.
It was an inspiring session about generative AI tools, how, when, and which to use. Swisscom is investing in education about AI that will shape our working landscape fundamentally.