I’ve lost count of how many AI proof-of-concepts I’ve watched die quietly. A team spends three months building something clever in a Jupyter notebook. The demo goes well. Leadership nods approvingly. And then… nothing. Six months later the data scientist has left, the notebook hasn’t been touched, and the business is still running the same manual process it always was.

It’s an industry-wide pattern. From what I’ve seen across Australian enterprises, the failure rate is worse than the global average. Not because our people aren’t talented. But because the gap between “interesting demo” and “reliable production system” is much wider than most organisations expect.

The Demo Trap

Here’s what typically happens. A vendor or internal data team builds a model that performs well on a clean dataset. They present it to stakeholders using carefully chosen examples. Everyone gets excited. Budget gets allocated for a POC.

The POC proves the concept. It shows that yes, a machine learning model can predict customer churn, or classify support tickets, or extract data from invoices. The accuracy looks impressive.

But proving a concept is the easy part. The hard part is everything that comes after.

Where It Falls Apart

Data quality in production is nothing like data quality in a lab. The POC used a curated dataset. Production data is messy, inconsistent, and full of edge cases. I worked with a logistics company whose document extraction model went from 94% accuracy in testing to 67% on real invoices because suppliers formatted things differently than expected.

Nobody planned for integration. The model sits in isolation. Getting it connected to the ERP or CRM requires API work, error handling, monitoring, and workflow changes. This integration work is often two to three times more effort than building the model itself.

There’s no MLOps capability. Models drift. Data distributions change. You need retraining pipelines, model versioning, performance monitoring, and rollback procedures. Most organisations doing their first AI project have none of this.

The business process wasn’t redesigned. You can’t bolt AI onto an existing process and expect transformation. If you’ve built a model that predicts equipment failure but nobody’s changed the maintenance schedule to act on it, you’ve built an expensive dashboard that gets ignored.

Ownership is unclear. Is this an IT project? A data team project? A business unit initiative? When nobody owns the path to production, nobody’s accountable for getting there.

What Actually Works

The organisations I’ve seen succeed share a few traits.

They start with the deployment model, not the data model. Before anyone trains anything, they work backwards from production. What system consumes the output? How will it be monitored? What happens when it’s wrong? I spoke with the team at Team400 about this recently, and they made a point I fully agree with: the deployment architecture should be designed before anyone writes a single line of training code.

They pick boring problems first. The companies succeeding with AI aren’t chasing moonshots. They’re automating document classification. Improving search relevance. Flagging transaction anomalies. Small, well-defined problems where the cost of errors is low.

They staff for production, not just experimentation. A data scientist alone won’t get you there. You need ML engineers, DevOps capability, and someone who understands the target business process intimately.

They set honest timelines. A realistic path from concept to production is 9 to 18 months. Not the 6 weeks the vendor quoted. If your timeline doesn’t include integration testing, UAT, monitoring setup, and parallel running, it’s fiction.

The Cultural Problem

Many organisations treat AI projects like software projects. They’re not. Software is deterministic. AI is probabilistic. Software either works or it doesn’t. AI works sometimes, mostly, or usually. That fundamental difference changes how you test, deploy, and set expectations.

If your organisation hasn’t grappled with what it means to run a system that will be wrong some percentage of the time, you’re not ready for production AI.

Moving Forward

My advice to any IT leader sitting on a pile of promising POCs: don’t start another one. Take the most promising existing POC and invest properly in getting it to production. Build the MLOps muscle. Learn the integration lessons.

One AI system in production is worth fifty successful demos. The organisations that figure this out first will have a genuine advantage. The rest will keep funding POCs that go nowhere.

It is delivering. Just not for companies that treat production as an afterthought.