A few days ago, a CXO asked me,
“Where do we even start with AI?
Every team is running pilots… nothing is reaching production.”
This is the real story inside most enterprises today.
AI isn’t failing because of capability.
It’s failing because there’s no sequence.
Here’s the 10-step order we’ve seen work repeatedly inside large organizations:
1. Define the decision, not the model.
Start with what decision needs improving and not what algorithm you want to try.
2. Map the data you actually have.
Not the ideal dataset. The real one.
3. Run a shadow evaluation.
Let AI make silent decisions beside humans for 2–4 weeks.
4. Build your internal “trust criteria.”
Decide when a model is safe enough to act.
5. Create small, modular components.
Tiny models beat architectural overkill in v1.
6. Add human handoff boundaries.
Mark the questions AI can answer and the ones it must not.
7. Put monitoring before deployment.
Drift, latency, cost: instrument everything early.
8. Start with 10% traffic.
Scale only when your eval gates stay green.
9. Document every assumption.
Your future teams will thank you.
10. Treat AI like infrastructure, not magic.
Iterate, upgrade, audit, repeat.
Most enterprises don’t need more AI pilots.
They need a predictable way to turn pilots into production systems.
And once that sequence clicks, AI stops feeling like an experiment… and starts behaving like an advantage.
