PoC purgatory: why AU AI pilots don't reach production

70% of Australian mid-market AI pilots never reach production. Here's the honest diagnosis of why — and the specific decisions that separate the ones that ship from the ones that stall.

title: "PoC purgatory: why AU AI pilots don't reach production" dek: "70% of Australian mid-market AI pilots never reach production. Here's the honest diagnosis of why — and the specific decisions that separate the ones that ship from the ones that stall." category: "STRATEGY" publishedAt: "2026-05-16" readTime: "9 min read" author: "EasiraAI editorial team" keywords:

AI pilot production
PoC purgatory
Australian mid-market AI strategy

I have a shorthand for a specific conversation that happens in Australian mid-market firms: "PoC purgatory." It is the state a firm enters when it has run one or more AI pilots, spent $30K–$80K, accumulated a collection of demos and proof-of-concept outputs, and has nothing in production doing real work.

The pilots were not failures in any objective sense. The models worked. The use cases made sense. The demonstrations were convincing. But something happened between "this looks promising" and "this is running in production," and the firm is now in a cycle of continued exploration without any of the business value the exploration was supposed to generate.

The 70% figure for AI pilots that never reach production is not a precise statistic — I have seen it cited from several different research sources with varying methodology. The pattern it describes is real and consistent across the firms I talk to.

This article is a direct account of why PoC purgatory happens and what the decisions that break the cycle look like.

Why pilots stall: the honest diagnosis

Reason 1: The pilot was designed to prove feasibility, not to solve a business problem

The most common root cause. A firm wants to "explore AI" or "test what's possible," so they run a pilot that demonstrates that a technology works — that an LLM can summarise documents, that a classification model can sort emails, that an agent can retrieve relevant policies.

The problem is that "the technology works" is not a business outcome. Every pilot built on this mandate will end with a demonstration that the technology works — and then stall, because "the technology works" doesn't tell anyone what to do next. There is no production system because there was no production problem to solve.

Pilots that reach production are defined around a specific operational problem: a process that takes too long, a cost that is too high, a risk that is inadequately managed. The pilot is designed to test whether an AI approach can solve that specific problem, measured against specific criteria. If the criteria are met, the path to production is already visible in the pilot design.

Reason 2: The data was not ready and nobody admitted it early enough

A pilot that starts with insufficient data quality will produce results that look promising in controlled conditions and fail in production conditions. The model is fine — it is doing the best it can with the data it has. The data is the constraint.

This is frequently identified during the pilot but not acted on because addressing it requires a data engineering project that wasn't in the original scope, and nobody wants to pause a pilot that is generating demos. So the pilot continues, the demos are presented, and the conclusion is "promising but needs more work" — which is the first step into PoC purgatory.

The right response when data quality is identified as a constraint is to stop the AI build and do the data work first. Not to continue with "good enough" data and hope production will be different.

Reason 3: No internal owner

A pilot run by an external vendor, with an internal "project sponsor" who attends fortnightly updates but does not own the work, will not transition to production without a specific person inside the organisation who wants it in production and is responsible for making that happen.

The internal owner needs to be someone who:

Understands the business process the AI is automating or supporting
Has the authority to change that process
Has a direct stake in the outcome (their team's time, their budget, their reporting)
Is involved in the pilot design, not just the demo presentations

Without this person, the pilot is a vendor-led exercise. When the engagement ends, the output is a report and a demo. There is no momentum toward production because there is no internal owner to carry it.

Reason 4: Governance concern without governance solution

In 2025–26, boards and senior leadership have become more aware of AI risk. This is generally positive. It becomes a problem for AI pilots when the concern is surfaced in the final stages of a pilot — "have we thought about Privacy Act implications?" or "what does our board need to know about this before we deploy it?" — and there is no prepared answer.

The result is that deployment is deferred pending governance review, the governance review is assigned to someone who has limited AI context, the timeline extends, the pilot team moves on to other priorities, and the AI project dies of governance inertia.

The solution is not to skip the governance step. It is to integrate it into the pilot from the start, so that the governance case is made alongside the technical case, and the board conversation is a readout of work already done, not an obstacle discovered at the end.

Reason 5: The commercial model makes production economics unattractive

Some AI pilots demonstrate technical feasibility at a cost that makes business sense at pilot scale and falls apart at production scale. API costs, infrastructure costs, and ongoing maintenance costs that were invisible during a pilot become significant when the system is processing real volumes.

This is less common than the other reasons, but it is a real one — particularly for firms that built pilots on third-party API models at pricing tiers that change, or that didn't model the ongoing maintenance cost of a system that needs monitoring, retraining, and support.

The difference between a pilot and a production system is not technical sophistication. It is whether the business case is clear, the data is ready, there is an internal owner, governance is integrated, and the economics work at scale. Most pilots fail on at least two of these.

What the pilots that reach production have in common

Looking at the AI deployments that have made it into production in AU mid-market firms, the pattern is consistent:

They started with a cost or process problem that someone senior wanted solved. Not "explore AI" — "reduce the time our finance team spends on invoice processing from 4 days to 1 day," or "get contract review turnaround from 48 hours to 4 hours for standard agreements."

The success criteria were defined before the build started. Exception rate below X%, processing time below Y, accuracy measured against a test set with known outcomes. When the pilot met the criteria, production was the obvious next step.

The data work happened first. The three to six weeks of data preparation, cleaning, and pipeline work that most pilots skip was done before the model work started. The pilot ran on production-representative data, not a curated sample.

There was a named internal owner who was accountable for the outcome. That person was involved in defining the success criteria, participated in the build, and was the one who made the production deployment decision.

Governance was handled in parallel, not at the end. The Privacy Act assessment, the automated-decision disclosure (where applicable), the acceptable use policy — these were produced during the build, not after the demo.

The 12-week pivot out of PoC purgatory

If you are currently in PoC purgatory — you have pilots that haven't reached production — here is the decision sequence:

Week 1–2: Audit what you have. For each pilot that hasn't reached production, honestly assess: what was the business problem it was supposed to solve? What did it actually demonstrate? What is the specific obstacle to production?

Most pilots will fall into one of three categories:

Worth progressing, with a specific fix (data work, internal owner appointment, governance documentation)
Not worth progressing in its current form (wrong use case, wrong approach, wrong data)
Already irrelevant because the business context has changed

Week 3–4: Pick one and close it. Select the highest-value stalled pilot and design the path to production specifically. Not another phase of exploration — a production deployment plan with a go-live date, a named owner, and specific success criteria.

Week 5–12: Ship it. Build, stabilise, and deploy. Four to six weeks of focused build work will determine whether the deployment works. If you have been "exploring" for 12 months, you do not need more exploration. You need a decision.

In parallel: fix the process for the next one. The structural fixes — data engineering before AI build, internal owner before external vendor, governance in the build not at the end — need to be built into how you commission the next AI project, not retrofitted onto the current one.

The role of the AI Readiness Audit in breaking the cycle

The AI Readiness Audit is designed partly for firms that have run pilots and want to understand why they stalled and what to do about them. It covers:

Use case prioritisation: which of your current or planned use cases has the highest probability of reaching production, and in what order should they be addressed
Data readiness assessment: is the data infrastructure that a production deployment requires actually in place
Governance gap analysis: what governance documentation is needed before a production deployment can be defended to the board
R&DTI eligibility assessment: if further development work qualifies for the 43.5% offset

The output is a sequenced 12-month roadmap that tells you what to build, in what order, with what data prerequisites — not more options to evaluate.

What to stop doing

PoC purgatory is partly a symptom of AI FOMO — the pressure to demonstrate that the organisation is "doing AI" before the conditions for successful AI deployment are in place. The pilots get funded because being seen to explore AI is better than being seen to do nothing. The production deployments don't happen because the conditions for them were never properly established.

The way out of PoC purgatory is not more pilots. It is a different question: "what problem do we need to solve, and is AI the right tool for it?" That question, asked honestly, will either produce a well-defined production deployment or a decision not to use AI for that problem — both of which are more useful than another demo.

The firms that are getting real value from AI in 2026 are not the ones that have explored the most. They are the ones that have shipped the most — which often means they have explored less, and built more carefully.

Stuck in PoC purgatory?

The AI Readiness Audit is the structured reset that tells you which of your stalled pilots are worth progressing, in what order, and what needs to be fixed before you build again. Contact us to discuss what you currently have and where it should go.