How AI projects can avoid the POC graveyard

Many artificial intelligence (AI) initiatives never make it past the proof-of-concept stage. Despite the early enthusiasm, they often stall and are quietly shelved. That’s not always a reflection of the technology: it’s usually a sign that the surrounding business context wasn’t ready to support the next steps.

In Australia, most organisations are still in the ‘explore and experiment’ phase with AI. That’s perfectly reasonable — while AI has been around for decades, it’s only in the last few years that we’ve seen a real acceleration in practical interest. With the introduction of ChatGPT, Claude, Llama and others, generative AI has re-energised the conversation, pushing many businesses to test the waters with new use cases.

And that’s a good thing: experimentation helps us figure out where AI can genuinely add value. Targeted proof-of-concept trials in the form of small, scoped projects that explore how AI might solve specific problems or improve business processes are a good place to start.

But not every trial leads to something bigger — in fact, many don’t progress beyond the initial test. Often it’s not because the AI didn’t work, but due to factors around it: legacy infrastructure, unclear business ownership, or cultural resistance to change.

If you want an AI project to succeed beyond the pilot phase, you need more than just a good model. Below are three key strategies that can make a difference when it comes to turning a proof of concept into a scalable, production-ready solution.

1. Get your data house in order

Before you even think about launching an AI proof of concept, you need to understand your data. You need to understand what you have, where it lives, and how well it’s documented. Without that, you’re setting yourself up for a lot of frustration down the track.

One of the most common stumbling blocks is when businesses want to trial AI but can’t confidently say what data they’re working with. That’s a problem, because AI models such as large language models (LLMs) need a large amount of data, and they need it to be clean, structured and accessible. If your data is spread across different systems, poorly labelled or missing context, the AI simply won’t deliver useful results.

This doesn’t mean you need to build a perfect enterprise data warehouse before you do anything. But you do need enough data hygiene to make the project feasible. That usually starts with strong metadata — descriptions of your data in plain language that both people and AI systems can understand.

Whether you’re building a customer-facing chatbot or an internal assistant to summarise sales performance, good data governance is the foundation. If your data is in a mess, the AI will be too.

2. Think beyond the trial

Proof-of-concept projects are meant to be small: that’s the point. But if the goal is to take something into production, you need to design with scale in mind from day one.

One common trap is building an AI solution that works well in a test environment but doesn’t hold up when you try to scale it. Maybe it only handles a small subset of your data, or it’s stitched together in a way that’s hard to maintain. That might be fine for a demo, but it becomes a bottleneck when you want to expand access, increase data volume or integrate with core systems.

This doesn’t mean you need to over-engineer the entire thing upfront. But it does mean making some smart architectural decisions early on, such as thinking about how data flows, how application programming interfaces (APIs) are managed, and whether the platform you’re using can grow with you.

If you want to avoid having to rebuild the whole thing later, treat the proof of concept like a stepping stone, not a throwaway. Keep one eye on the pilot, and the other on production.

3. Build an appetite for risk

Even the smartest AI solution won’t help if nobody’s willing to put it in front of real users. Success depends on a culture that’s comfortable taking measured bets and iterating fast.

AI moves quickly: models evolve, APIs change, frameworks are deprecated, and a clever workaround today can look ancient in six months. If your organisation isn’t ready to experiment, learn and rebuild on short cycles, the momentum stalls and the project fades.

That doesn’t mean being reckless; it means treating AI like any other R&D portfolio. Set clear guardrails such as privacy, security and cost limits, but give teams the freedom to try something new, push to production and measure impact. If the results are good, double down; if not, pivot without drama.

The companies that win with AI aren’t the ones chasing perfection on version 1.0. They’re the ones shipping, collecting feedback and refining in tight loops. Make that your default mindset, and your AI initiatives have a real shot at thriving well beyond the pilot phase.

Image credit: iStock.com/CHOLTICHA KRANJUMNONG