From POC to Production: Why AI Success Depends on Operational Discipline, Not Just Models
Last week, I had the pleasure of hosting a genuinely candid and insightful discussion with Andy Peddar (Deazy) and Marko Ivanovski (Aveni) on one of the biggest challenges organisations are facing right now: getting AI out of proof‑of‑concept and into reliable, scalable production.
What struck me most was how consistent the blockers are across industries — whether you’re an enterprise scaling AI, a regulated financial services firm, or an AI‑native scale‑up.
A few reflections that really stayed with me:
The model is rarely the bottleneck anymore — the system around it is.
Teams can spin up impressive POCs in days. But building something that can run safely, predictably and economically under real‑world conditions is where things get difficult.
Evaluation and observability are no longer optional.
Drift, prompt changes, model deprecations — they all happen faster than traditional governance can react. Without proper evaluation frameworks and guardrails, trust collapses quickly.
Cost dynamics only get real at scale.
Several participants shared painful stories of token consumption surprises and unexpected cloud bills. “Good enough + cheaper” models increasingly win at scale.
Operating model clarity is critical.
AI fails to scale not because of technical limits but because organisations don’t know who owns it — product, engineering, data, or the business? That ambiguity creates long delays.
POC-to-production is an entirely different operating environment — not a scaled‑up POC.
A point Andy made powerfully. Production AI demands reliability, safety, governance, and user trust — not experimentation.
It’s clear that while the hype is loud, the operational reality requires a very different set of muscles. Huge thanks to everyone who contributed so openly under Chatham House rules — the shared challenges made for a genuinely valuable conversation.
If anyone wants to explore the production-readiness framework we discussed, or wants to explore accelerating POCs without creating downstream operational debt, request to join the session group.
Great discussion, you’re describing exactly the transition many organisations are now hitting. The industry spent the last two years proving that AI can work. Now the real question is whether it can run reliably inside an operating business – a completely different challenge.
The moment AI moves from demo to production the conversation shifts from models to systems. Governance. Cost control. Observability. Ownership. In other words the boring but essential things that make technology dependable.
It reminds me a lot of the early SaaS era. The product innovation was exciting but the real maturity came when companies built the operating models around it.
AI feels like it’s entering that same phase now.
Thanks Jason, completely agree. We’re at that crucial inflection point where the conversation is less about “can we build a great model?” and more about “can we run this sustainably, safely, and at scale?”
What we’re seeing across our community mirrors exactly what you’ve described: the proof‑of‑concept phase was energising, but production demands a very different muscle group. Governance, lifecycle management, observability, cost discipline — none of it is glamorous, but it’s the foundation that separates isolated wins from repeatable value.
Your SaaS analogy is spot on. The breakthrough wasn’t just the software, it was the operating model that allowed organisations to trust it and rely on it. AI is moving into that same maturity curve now, and those who invest in operational discipline are the ones who will actually capture the upside.