LinkedIn·Tuesday, 18 August 2026·8d ago

AI transformation harsh truth Proof of concepts never work: → You build in a fake environment with curated data. You get great benchmarks.…

Assem Chammah

CEO @ Nexus | AI transformation for enterprises | Clients inc. Orange Group & Lambda

AI transformation harsh truth Proof of concepts never work: → You build in a fake environment with curated data. You get great benchmarks. Then someone asks "will it work in production?" and nobody knows. → POCs create endless loops. You run one POC, get decent numbers, leadership wants another POC with more realistic data. → You're measuring the wrong thing. POC metrics are invented scenarios. They don't reflect real user behavior or edge cases. I've worked on 100+ transformation projects at McKinsey And now 20+ AI projects totalling over $50M impact. Better way: 1. Pick one workflow → Not the whole operation. Focus on one use case / team. → Choose something with clear inputs and outputs → Avoid processes that need lots of integrations to test → Ideally something that interacts with customers 2. Deploy to production immediately → Yes, it will make mistakes. Accept that upfront. → Start with human-in-the-loop if needed → AI handles 80% of cases, humans handle exceptions → You're augmenting the team, not replacing them → Set expectations that v1 will be imperfect 3. Measure real outcomes from day one → Did handle time decrease? → Did throughput increase? → Did error rates change? → Did customer satisfaction move? → Did the humans using it actually adopt it? 4. Fix issues as they appear → Production surfaces problems fast. → Track every failure mode → Prioritize fixes by business impact (not frequency) → Let the team report issues directly 5. Expand scope incrementally → Week 1: One workflow, one team → Week 4: Same workflow, two more teams → Week 8: Add a second workflow → Week 12: Full department rollout → Every week add a little bit more 6. Build trust through transparency → Show them exactly what the AI is doing → Let them override decisions easily → Celebrate when they catch AI mistakes → Track their feedback and show them it's being used → The humans using this system need to trust it Bottom line: → Traditional POC approach takes 6-12 months to potentially ship something. → This approach ships in 30 days and improves from there. One group protects themselves from failure. The other builds something that works.