Stop piloting AI. Put it on the critical path
Most GCC AI programs stall the same way. A demo impresses HQ. A sandbox stays busy. Production work barely changes.
The research wave talks about AI as operating infrastructure, not a side lab. You do not need another slide to act on that.
Pick one workflow that already hurts. Put AI on it for real. Measure the outcome.
Why pilots die
- No owner with P&L or product authority
- Data quality too weak for production use
- Security review starts after the demo, not before
- Success defined as “people tried the tool,” not “work got better”
Three shifts that work
- One production bet. Support deflection, code review assist, or invoice exception handling. Not five half-pilots.
- Human in the loop by design. Agents propose. Named people approve until trust is earned.
- Kill criteria written on day one. If the metric does not move in ninety days, stop or redesign.
30 / 60 / 90
- 30: Name the workflow, owner, and success metric.
- 60: Access, data, and model risk cleared for that one path.
- 90: Live in production with a before/after story HQ can believe.
Takeaway
Pilots are cheap. Credibility is expensive.
Make AI part of how the center runs, or stop pretending the lab is a strategy.
If you want help scoping a first production bet for a Bengaluru team, book a discovery call.