I’ve lost count of how many AI proofs of concept I’ve seen die in the last two years. Not because the technology didn’t work. Not because the business case was bad. They died because the gap between “this demo looks great” and “this runs in production at scale” is wider than most organisations realise, and almost nobody plans for it properly.

If you’re an IT leader in 2026 and you haven’t already learned this lesson the expensive way, here’s what you need to know.

The POC Trap

The typical pattern goes like this. A business unit identifies an opportunity — maybe automating document processing, maybe improving demand forecasting, maybe enhancing customer support. They get approval for a proof of concept. A vendor or internal team builds something in six to eight weeks that demonstrates the concept works.

Everyone’s excited. The demo shows impressive results. The presentation to the steering committee goes well. Then someone asks: “So when can we go live?”

And that’s when things fall apart.

The POC was built on a curated dataset, running on a single developer’s laptop or a managed notebook environment. It doesn’t connect to your production data sources. It has no monitoring or alerting. It doesn’t handle edge cases. It wasn’t built with security review, data privacy compliance, or your organisation’s access control framework in mind. It has no error handling because during the demo nobody entered anything unexpected.

Going from that to a production system isn’t a small step. It’s essentially a rebuild.

The Numbers Are Stark

Gartner’s research suggests that fewer than 30% of AI POCs make it to production. In my experience with Australian enterprises specifically, I’d put the number closer to 20%. The other 80% either die outright or enter a zombie state — technically still funded, technically still being worked on, but never actually delivering business value.

That’s a staggering waste of money and organisational attention. If you ran four POCs at $150K each and only one made it to production, you’ve spent $600K to get one working system. And the one that survived probably cost another $300K to productionise. Your actual cost of delivery is $900K for what the original business case estimated at $150K.

Where Things Go Wrong

After watching dozens of these projects, the failure points are predictable.

Data quality hits late. The POC works on clean, curated data. Production data is messy, inconsistent, has gaps, and changes format over time. The team discovers this after the POC is approved, not before. Cleaning and standardising production data often takes longer than building the AI model itself.

Infrastructure wasn’t scoped. The POC runs in a cloud notebook. Production requires model serving infrastructure, monitoring, retraining pipelines, data pipelines, and integration with existing systems. None of this was budgeted because nobody asked about it during the POC phase.

The ML engineer isn’t a production engineer. Data scientists who build great POCs often don’t have the skills to build production-grade systems. You need MLOps capability — people who understand model deployment, containerisation, CI/CD for ML, and production monitoring. Most Australian enterprises don’t have these people on staff, and they didn’t budget for external help.

Security and compliance review takes months. In regulated industries — financial services, healthcare, government — getting an AI system through security review, privacy impact assessment, and regulatory compliance can take three to six months. The POC timeline never accounts for this.

The business unit moved on. By the time the POC is ready for productionisation (if it ever is), the executive sponsor has changed roles, the business priority has shifted, or the budget has been reallocated. Organisational attention spans are shorter than AI project timelines.

What IT Leaders Should Do Differently

I’ve seen enough failures to know what works. Here’s the approach I recommend.

Start with Production in Mind

Before approving any AI POC, require the team to submit a production architecture alongside the POC plan. Not a detailed design — a conceptual architecture that answers: Where will this run? How will it access production data? Who will operate it? What monitoring will it need? What compliance requirements apply?

If the team can’t answer these questions at the outset, the POC is premature. You’re not ready to build; you’re still in discovery.

Budget for the Full Journey

A useful rule of thumb: the POC is 20% of the total cost. Productionisation is 80%. If you can’t fund the full journey, don’t start the POC. You’ll just be adding to the graveyard.

This means AI projects need proper business cases with realistic total cost of ownership, not optimistic demos followed by a request for more money.

Build MLOps Capability

You need people who can bridge the gap between data science and production engineering. In Australia, this talent is scarce and expensive, but it’s non-negotiable. If you don’t have it internally, you need a partner who does.

I’ve seen organisations work effectively with AI consultants in Melbourne and other cities who specifically focus on productionising AI, not just building prototypes. The key is finding partners who’ve actually shipped production systems, not just built demos.

Set Kill Criteria Early

Define upfront what constitutes failure, and have the discipline to kill projects that meet those criteria. “The model’s accuracy dropped below our threshold” is a valid kill reason. “We’ve spent six months in compliance review with no end in sight” is another.

Sunk cost fallacy kills more AI projects than bad technology. Organisations keep funding zombie POCs because nobody wants to admit the investment was a mistake.

Use Phased Rollouts

Don’t try to go from POC to full production in one step. Deploy to a limited scope first — one team, one region, one product line. Validate that it works with real users and real data before expanding. This reduces risk and gives you real performance data to justify broader investment.

The Governance Dimension

This connects to broader AI governance, which I wrote about recently. If your organisation doesn’t have a clear process for evaluating, approving, and monitoring AI deployments, then even successful POCs will stall in the approval process.

The organisations that are successfully getting AI into production have a few things in common: executive sponsorship that persists beyond the demo phase, dedicated MLOps capability, realistic budgets, and governance processes that enable deployment rather than blocking it.

The Uncomfortable Truth

Most organisations don’t have an AI technology problem. They have an AI execution problem. The technology works. The models are good enough. The cloud infrastructure is mature.

What’s missing is the organisational capability to take a working prototype and turn it into a production system that delivers sustained business value. That’s an engineering challenge, a governance challenge, and a change management challenge — all at once.

If you’re planning AI initiatives in 2026, spend less time on the demo and more time on what happens after. That’s where the real work is, and it’s where most projects die.