Why jumping straight to an AI proof of concept is costing you

Most AI projects stall between a promising pilot and a working system. The fix is not a better model. It is spending the budget in the right order.

The Pilot trap

Most AI projects stall between a promising pilot and a working system. The fix is not a better model. It is spending the budget in the right order.

Australian organisations are not short of AI ideas. Boards are asking for them, vendors are demonstrating them, and most leadership teams can name a handful of use cases they would like to pursue. What is in short supply is AI that runs in production, every day, and moves a number the business cares about.

The research is clear. RAND Corporation estimates that more than 80 per cent of AI projects fail, roughly twice the failure rate of IT projects that do not involve AI. A 2025 S&P Global Market Intelligence survey of more than 1,000 organisations found that 42 per cent had abandoned most of their AI initiatives, up from 17 per cent the year before. The average organisation scrapped 46 per cent of its AI proofs of concept before they reached production.

Those numbers are easy to read as a verdict on the technology. They are not. In our experience, AI projects rarely fail because the model was wrong. They fail because of decisions made long before anyone trained one.

Why AI projects fail

RAND interviewed 65 experienced data scientists and engineers to find out what goes wrong. Their answers fall into five patterns, and only one of them is really about the limits of AI.

• The wrong problem. The most common cause is a misunderstanding about what the project is meant to achieve. Data teams optimise the metric they were given, not the outcome the business actually needed.
• Not enough of the right data. An idea can be sound and still fail because the data to train a reliable model does not exist, or is not good enough.
• Technology first. Projects chase the newest technique rather than the simplest thing that solves the user's problem.
• No path to production. Organisations lack the infrastructure to manage data and deploy models, so a working prototype has nowhere to go.

• Problems AI cannot solve. Some projects ask the technology to do something it simply cannot do yet.

  To that list we would add two patterns we see repeatedly in Australian organisations.

The first is spending in the wrong order. Teams build heavy data pipelines and integrations before they know the idea works, or keep tuning a model for accuracy long after the returns have flattened. Either way, the budget is gone before the value is proven.

The second is underestimating what production takes. A model that performs well in a pilot still has to be fed live data, monitored for drift, connected to the systems that act on its outputs, and adopted by the people whose work it changes. That is where most of the effort sits, and it is the part most business cases leave out.

Prove value before you scale

A better approach splits every AI and machine learning project into four stages, each with a clear decision at the end. The first three typically use 20 to 40 per cent of the total investment, by which we mean the time and budget a project uses from idea to production. Their job is to prove the idea is worth the remaining 60 to 80 per cent.

1. Strategy and ideation (typically 5 to 10 per cent). Define the problem in business terms, the uplift you expect, and the budget available. If nobody can say what success is worth, the project stops here, cheaply.

2. Data analysis and feasibility (5 to 10 per cent). This is the stage most projects skip. It tests whether the data can support the idea. That means data statistics and correlations, the external variables that matter, the interpretations of the people who know the operation best, and often a site visit. At this stage, we also test the critical components, map the integrations and the change the business will need, and produce a cost, feasibility and ROI estimate.

3. Proof of concept (10 to 20 per cent). Build a model that directionally predicts the outcome, using the simplest data collection possible and a small, auditable set of features. No heavy pipelines yet. Once the evidence is convincing, we stop tuning. Chasing extra accuracy at this stage is one of the fastest ways to burn a budget.
4. Implement, test and productionise (60 to 80 per cent). Now the investment goes in, with confidence. Data is ingested directly from source, pipelines and automations are made production grade, alerts watch for drift, and the model's outputs are connected to the systems and people who act on them. MLOps keeps models tested, retrained and redeployed so performance holds over time.

The shape matters more than the exact numbers. Most failed projects spend heavily in stages three and four before stages one and two have earned it. Reversing that order means a weak idea costs weeks, not a year, and a strong idea arrives at production with the evidence to back it.

Why stage two can't be skipped

Most organisations jump straight to a proof of concept. It feels like progress: something gets built, and there is a demo to show the board. But a proof of concept only answers one question, whether a model can work. It does not test whether the data, the foundations, the running costs or the business case will hold up in production, which is why nearly half of proofs of concept are scrapped before they get there.

A readiness and feasibility assessment asks those questions first, and it is the one stage not worth compromising on.

• Readiness and feasibility, together. Feasibility asks whether a specific idea can deliver. Readiness asks whether your data, platforms, access controls, governance and people can support it. An idea can be feasible in theory and still fail on weak foundations, which is where many of the failures RAND describes begin.
• The cost of running it, not just building it. We estimate model consumption based on expected usage and recommend design choices that keep running costs predictable. As AI running costs become a visible line item, boards want this number before they approve the build.
• "Don't proceed" is a good result. Catching a flawed assumption early is far cheaper than discovering it after months of implementation. The study is designed to test a decision, not to justify one that has already been made.

Three questions to ask before you fund the next AI project

• What is it worth if it works? If the expected uplift and the available budget are not written down, the project has no way to know when to stop.
• Have we tested the data, or assumed it? A short feasibility phase is cheap insurance against the most common technical cause of failure.
• Who owns it in production? Someone needs to own the pipelines, the monitoring and the change to how people work, from day one, not after the pilot succeeds.

AI does not need to be a gamble. The organisations getting real returns are not the ones with the most ambitious pilots. They are the ones that prove value early, decide with evidence, and then commit properly to the hard work of production.