What a pilot actually is

Think about what goes into a successful pilot. Small scope. A motivated team. Usually someone technical who cares deeply about the outcome, watching it closely. The data going in has been cleaned. The edge cases have been handled manually. When something looks wrong, someone fixes it before it surfaces. It’s a carefully curated workflow — which is exactly why it works.

Then it goes into production. The motivated team moves on to the next thing. The close attention loosens. The data coming in is messier. The edge cases that were handled manually are now being handled by the system. And the outputs are going to people who will act on them — people who weren’t involved in building it.

This is the moment most AI projects actually fail. Not in the pilot. After it.

Deloitte’s 2026 State of AI in the Enterprise report puts a number on it. Globally, only 25% of organisations have moved 40% or more of their AI experiments into production. And the Australian picture is worse — the report specifically calls out that Australian organisations are falling further behind global peers on scaling. Not in the middle of the pack. At the back of it.

Why AI governance Australia needs to solve for agentic AI

Here’s what makes this technology cycle different from the ones before it. The thing being piloted isn’t a dashboard or a report. Increasingly, it’s an AI agent — a system that doesn’t just produce outputs, it takes actions.

When that agent is well-supervised in a pilot environment, it performs. When it goes into production with real data and less oversight, it fails in different and more dramatic ways.

The cost isn’t just the investment. It’s the credibility of the team that championed it. And it’s the increased scepticism that greets the next AI proposal.

Three questions to ask before you approve the next stage

Most AI pilots are designed to succeed. Small scope, curated data, engaged team. That’s the right way to test something. It’s also why the result only tells you half the story. The pilot tells you the technology can work in ideal conditions. It doesn’t tell you whether it will work in yours.

So before you approve the investment to scale, ask three things.

First — what does success realistically look like in twelve months? Not tool adoption rates. A business metric that would mean something to your CFO or your board. One you could actually use to demonstrate ROI.

Second — what did the pilot not test? There is almost always something. A messier data source that wasn’t included. A workflow edge case that got handled manually. A scenario the team knew was coming but parked for later. You don’t need to understand the technical detail. But you do need to know what those gaps are — and whether the business can tolerate them. Some gaps are acceptable risks. Others aren’t. That’s your call to make.

Third — treat the scale decision as a different decision from approving the pilot. The pilot told you the technology can work. The question in front of you now is harder: will it work in your environment, on your real data, without the close attention it had during testing? If you’re being shown an agentic AI pilot right now, ask: what does this system do when it gets it wrong? Is there a mechanism to catch it before the wrong action becomes a problem?

The organisations that get AI governance right don’t treat scaling as simply extending the pilot to the rest of the business — because the conditions that made it work in one part won’t automatically hold in the next.

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