Production-First AI: The Only Way Past POC Purgatory

AI POCs and MVPs need to get out of the lab and into production, faster. Not because demos are bad, but because ROI does not come from demos. It comes from real users, in real workflows, with reliability and accountability.

Over the last 2–3 years, lab-first POCs made sense. Frontier models were improving quickly, tooling was maturing, and teams needed quick validation. So we built “minimum guardrails” prototypes to prove feasibility.

The problem is what happens after the demo: the handoff becomes a reset. Engineering inherits a POC that was never designed for production realities (security, access control, cost controls, latency targets, monitoring, testing, change management). The result is predictable: rewrite from scratch, missed momentum, and rising frustration on both sides.

Here’s the shift I’m advocating for: prod-first MVPs.

That does not mean slowing down. It means building MVPs on the intended deployment path, in consultation with engineering, so the handoff is a continuation, not a rebuild.

This also means planning for AIOps, not just “app ops.” AI systems require continuous measurement and tuning: prompt/agent drift, changing data, model swaps as capabilities and prices evolve, and rigorous regression testing before deployment.

The encouraging news: production infrastructure is getting smoother. Platforms like Azure AI Foundry, Amazon Bedrock (including Guardrails), Vertex AI Agent Builder, and OpenAI’s Agents Builder are increasingly packaging the scaffolding teams used to build themselves.

My call to action: stop measuring success by prototype completions. Measure it by production usage, operator confidence, and business outcomes.