AI POC vs MVP: Which Do You Actually Need?

"Should we do a POC or an MVP?" sounds like a small distinction—but conflating them wastes real money. They answer different questions. Here's which you need.

The core difference

	Proof of concept (POC)	Minimum viable product (MVP)
Answers	"Can this work at all?"	"Will people use this?"
Tests	Technical feasibility	Business value
Environment	Narrow test	Production, real users
Output	A yes/no on feasibility	A validated bet + foundation

A POC tests feasibility; an MVP tests value and adoption. See the AI POC guide.

When you need a POC

Use a POC when technical feasibility is genuinely uncertain—a novel problem where you're not sure AI can do it at all. The POC answers that one question before you invest further.

When you need an MVP (and can skip the POC)

With modern AI, feasibility is often clear—the model can obviously do the task. Then a separate POC is wasted; go straight to a small MVP that proves both feasibility and value in production. The real risk isn't "can it work" but "will it ship and get adopted"—see why enterprise AI stalls.

Build either toward production

Whichever you run, build it on real data and toward production—not a throwaway on clean data. A POC or MVP that proves the wrong thing, or can't become the real system, wastes the investment. This is the discipline that separates a shipping project from a stalled one.

The practical answer for most teams

For most AI projects today, the answer is a small production MVP, not a lab POC—because the risk is deployment, not feasibility. Start there unless feasibility is genuinely in doubt.

Why FISTA

FISTA Solutions runs POCs and MVPs that become production—real data, real value, a build path—through its Applied Division, backed by 150+ projects across 12+ countries.

POC or MVP for your idea? Talk to FISTA.