In corporate settings, even great ideas can stall. You see a clear AI opportunity, but then run into:
- Long IT backlogs
- Executive hesitation
- ROI expectations before experimentation
Recently, I came across a case that felt especially practical. A product manager, facing those same barriers, partnered with an external AI team. In 90 days, they built a proof-of-concept that quietly delivered real results: 42% of Tier-1 requests handled, and a projected $1.2M in savings.
It’s not just the outcome that stood out; it’s how she worked around the system without fighting it.
The Corporate Innovator’s Guide to AI Prototyping breaks this down well. A few things resonated:
- Work Around, Not Against — External teams can move fast, as long as you stay aligned on compliance and data needs. It’s not rebellion, it’s pragmatism.
- Keep the Scope Narrow — The guide’s take on MVPs is refreshingly grounded: one core AI function, tight feedback loops, and a focus on solving one real problem.
- Show the Work Internally — Frequent demos and updates help avoid surprises and build trust. You don’t need everyone on board, just a few key allies early.
- Plan the Handoff Early — The handoff to internal teams isn’t a final step; it’s part of the prototype. Clear docs, shared context, and a short shadow period go a long way.
The broader takeaway?
You don’t have to wait for perfect conditions. Sometimes building alongside the system, respectfully, transparently, is what unlocks the next step.
One thought
Progress doesn’t always start loud. Sometimes the best path forward is small, well-scoped, and quietly effective.
Would be curious how others have made headway in complex environments, especially when the path wasn’t obvious.
