Most AI initiatives die between proof-of-concept and production.
Not because the model doesn’t work,
but because the organization doesn’t have a structured delivery lifecycle.

AI needs a different pipeline than traditional IT.
Software ships features.
AI ships intelligence, and intelligence needs ongoing care.

The 6 Stages of the AI Lifecycle

1) Problem Framing

Start with a validated business problem.
Not “let’s use GenAI,” but “we want to reduce claim cycle time by 40%.”

2) Data Feasibility Check

Check if you have data that’s: available + labeled + accessible + compliant.

3) Prototyping / POC

Build a lightweight version to test feasibility.
This is where most companies stop. The mature ones continue.

4) Pilot Deployment

Deploy in one use-case or one region.
Measure impact, cost, and risk.

5) Full Productionization

Integrate into workflows, CRM, underwriting systems, LOS, claims tech, etc.

6) Continuous Monitoring + Optimization

Model drift is real. AI improves only if you keep retraining + tuning.

Executive Guidance

Don’t approve AI work unless a project team can clearly answer:

• What business outcome are we improving?
• Who owns the model after deployment?
• How will we measure success monthly?
• What happens when the model drifts?

Most AI failures are governance failures, not model failures.

Final Thought

AI is not “deliver it once and forget.”
It’s a living product that must be continuously maintained.

Phase 3 is where many organizations will either plateau…
or start compounding advantage.