I’ve watched dozens of Australian companies run AI proofs of concept. The majority fail, and they fail for the same reasons every time. Not because the technology doesn’t work, but because the PoC was designed to produce a demo rather than to answer a genuine business question.

Here’s how to run one that actually proves something useful.

Define What You’re Testing Before You Start

This is where most PoCs go wrong. The typical brief is something like: “Let’s see if AI can help with customer service.” That’s not a testable hypothesis. It’s a vibe.

A good PoC tests a specific, measurable hypothesis. “AI-powered email triage can reduce average first-response time for customer inquiries from 4 hours to under 1 hour, with accuracy comparable to human triage.” That’s testable. You’ll know at the end whether it’s true.

Before you start, agree on three things with all stakeholders.

Success criteria. What specific metrics will you measure? What thresholds constitute success? Write these down and get sign-off before the PoC begins. Changing the goalposts mid-PoC is the most common way to turn a failed experiment into a false success.

Failure criteria. What would convince you that this isn’t worth pursuing? Be explicit. If accuracy falls below 85%, if integration takes more than 40 hours, if user satisfaction drops below baseline, the PoC has failed. Knowing when to stop is as important as knowing when to proceed.

Scope boundaries. What’s in scope and what isn’t? A PoC that keeps expanding scope is a project, not an experiment. Be disciplined.

Use Real Data, Not Sample Data

This is non-negotiable. AI systems that perform beautifully on sample data and collapse on real data are so common they should be a meme.

Your PoC must use representative data from your actual operations. Not cleaned-up data. Not a curated subset. Data that reflects the messiness, inconsistency, and edge cases that your production environment contains.

If data privacy concerns prevent using real customer data, use properly anonymised data that preserves the statistical properties of the original. But be aware that anonymisation can change data distributions in ways that affect AI performance. Note this as a limitation.

Real data will expose problems that sample data hides. Missing fields that the model needs. Inconsistent formatting that breaks ingestion pipelines. Edge cases that cause incorrect outputs. Better to find these problems in a two-week PoC than six months into a production deployment.

Run It for Long Enough

Two weeks is the minimum for any meaningful AI PoC. Four to six weeks is better for most business applications. Anything shorter isn’t capturing enough variability in your operations to be reliable.

AI performance can vary significantly with temporal patterns. A customer service AI tested during a quiet week may look brilliant. Test it during your peak period and performance might drop because query types and volumes are different.

If your business has seasonal patterns, make sure your PoC captures enough of that variation to be representative. An AI forecasting tool tested during a stable period tells you nothing about how it handles volatility.

Measure What Matters, Not What’s Easy

The temptation in any PoC is to measure technical metrics: model accuracy, processing speed, API response times. These matter, but they’re not the metrics your business decision depends on.

Business metrics should take priority. Did customer satisfaction change? Did processing time decrease? Did error rates improve? Did the team actually use the tool, or did they revert to manual processes?

Track adoption metrics too. How often did users interact with the AI system? Did usage increase or decrease over the PoC period? What feedback did users provide? An AI system that’s technically excellent but that users avoid is a failed experiment, regardless of its accuracy metrics.

And measure the hidden costs. How much time did IT spend on integration? How much time did the vendor spend on configuration and troubleshooting? How much time did the business team spend learning the system? These costs scale with deployment and need to be factored into ROI calculations.

Involve the Actual Users

Don’t run your PoC in a lab with your most technical people. Run it with the people who would actually use the system in production. Their feedback is infinitely more valuable than a technical team’s assessment.

If you’re testing an AI tool for customer service, have your real customer service team use it for real customer interactions. If you’re testing document processing AI, have your real admin team process real documents.

Real users will find problems that technical evaluators miss. They’ll identify workflow friction that no requirements document captured. They’ll tell you whether the tool makes their job easier or just adds another step.

Document Everything

At the end of the PoC, you need a clear recommendation backed by evidence. That requires documentation throughout.

Record the setup: data used, configuration choices, integration approach. Record the results: all metrics, including the ones that didn’t look good. Record the issues: every problem encountered, how it was resolved, and the time it took. Record user feedback: both structured surveys and informal observations.

This documentation serves two purposes. It supports the go/no-go decision immediately. And it provides a baseline if you proceed to a full implementation, so you can measure whether production performance matches PoC performance.

The Decision Framework

At the end of your PoC, you should be able to answer four questions.

Does the AI system deliver measurable value against your success criteria? Can it integrate into your existing workflows at acceptable cost and complexity? Will your team actually use it? Does the projected ROI justify the full implementation investment?

If all four answers are yes, proceed to implementation. If any answer is no, either address the specific gap or redirect your investment.

What you should never do: proceed to implementation despite a failed PoC because you’ve already committed politically or financially. That path leads to much larger losses than admitting the PoC didn’t deliver.

If you’re running your first PoC and want structured guidance, working with a business AI solutions firm who’ve run dozens of these can help you avoid the common pitfalls and design an experiment that produces genuinely useful results.

A well-run PoC saves you from bad investments and gives you confidence in good ones. It’s the most valuable few weeks you’ll spend on any AI initiative.