I’m going to say something that would’ve gotten me laughed out of a Big 4 boardroom five years ago: stop running proofs of concept for your AI projects.

Not slow them down. Not improve them. Stop.

The AI POC, as we’ve known it for the past decade, is dying. And honestly? It should’ve died sooner.

The POC Was Built for a Different Era

The traditional proof of concept made sense when AI implementations cost millions upfront and required custom infrastructure. You’d spend three months and $200K proving the concept worked in a sandbox before committing $3 million to production. The risk calculus was straightforward.

But the landscape has shifted dramatically. Cloud-native AI services, pre-trained foundation models, and API-first tooling have collapsed the cost of getting something into production. A team of three engineers can stand up a functional AI pipeline in weeks, not months. The argument for spending a quarter of the year proving something works in isolation has gotten very thin.

McKinsey’s 2025 State of AI report found that organisations moving directly to limited production deployments achieved positive ROI 40% faster than those following the traditional POC-to-pilot-to-production pathway. That’s not a marginal difference. That’s a structural advantage.

Why POCs Fail on Their Own Terms

I’ve watched dozens of POCs across Australian enterprises, and they fail in remarkably consistent ways.

They prove the wrong thing. A POC demonstrates that the technology can work under ideal conditions. What you actually need to know is whether it works with your data, your infrastructure, your team’s skills, and your customers’ tolerance for imperfection. None of these things exist in a sandbox.

They attract the wrong attention. POCs become executive showcases. The moment a senior stakeholder sees a polished demo, expectations crystallise around that experience. When production reality looks messier (and it always does), the project gets labelled a disappointment, even if it’s delivering genuine value.

They create a false finish line. I sat with a logistics team at a major Melbourne distributor last year. They’d completed a POC for an AI-driven demand forecasting tool. Everyone was celebrating. Then someone asked: “So, how do we connect this to our actual ERP?” The room went quiet. The POC had proven the model worked. It hadn’t proven the model could work inside their business.

The team disbands. POC teams are temporary. The engineers who built the prototype move on to other projects. When it’s time to build the production system, institutional knowledge walks out the door.

What’s Replacing the POC

The companies I’m seeing get the best outcomes are running what I’d call “constrained production pilots.” The difference isn’t just semantic.

A constrained production pilot uses real data, real infrastructure, and real users from day one. But it’s scoped tightly. Maybe it processes invoices from the top five suppliers, not all 400. Maybe it runs in one warehouse, not twelve.

The critical distinction: it’s production code from the start. There’s no second build. The thing you deploy on day one is the thing you scale on day 90.

A Tier 1 bank here in Australia tried this approach with their anti-money laundering alerts last year. Instead of building a POC to prove an AI model could triage alerts, they deployed a production model on a single alert category, about 8% of total volume. Real analysts used it. Real compliance officers reviewed the outputs. Within six weeks, they had data on false positive rates, analyst trust levels, and processing time savings that no POC could have generated.

They scaled to full volume in four months. A traditional POC-to-production path would have taken at least nine.

The Objections (And Why They Don’t Hold Up)

“But we need to prove feasibility before committing resources.” Fair. But a two-week spike with real data proves feasibility better than a twelve-week POC with synthetic data. If your team can’t determine feasibility in two weeks, the problem isn’t the timeline. It’s the problem definition.

“Our governance requires a POC phase.” This is the most common objection I hear from regulated industries. But governance frameworks can evolve. Gartner’s research on AI governance increasingly recommends risk-based approaches that focus on deployment scope rather than rigid phase gates. A constrained production pilot with proper monitoring actually gives your risk committee more useful data than a sandbox POC.

“We can’t put untested AI in front of customers.” Then don’t. Run the pilot with internal users, or in shadow mode where the AI makes recommendations but a human acts on them. These are deployment constraints, not arguments for building throwaway prototypes.

What This Means Practically

If you’re planning an AI initiative this year, here’s what I’d recommend:

1. Skip the POC. Go straight to defining the smallest useful production deployment.
2. Budget for production from day one. This means infrastructure, monitoring, support, and training. Not just model development.
3. Set a 90-day decision point. At the end of the pilot, you should have enough production data to decide: scale, pivot, or stop.
4. Staff it permanently. The people who build the pilot should be the people who scale it.

The POC model gave us permission to experiment without commitment. That was valuable when AI was exotic and expensive. It’s neither of those things anymore.

The POC isn’t being killed by a better methodology. It’s being killed by the economics of modern AI infrastructure. And that’s a good thing.