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What to Look for in an AI Proof of Concept

April 13, 2026•11 min read•Michael Ridland

An AI proof of concept is where theory meets reality. It's the first time you see whether AI can actually solve your problem with your data, in your context. Get the PoC right and you have a clear path to production. Get it wrong and you either kill a viable project or, worse, green-light one that should have been stopped.

We've built and evaluated dozens of AI proofs of concept across Australian businesses. Here's what to look for, what questions to ask, and how to make a good go/no-go decision.

What a Proof of Concept Is - and Isn't

Let's start with definitions, because the term "PoC" is used loosely in the AI industry.

A proof of concept answers the question: "Can AI solve this problem well enough to justify further investment?"

It is not:

  • A demo built on sample data (that's a demonstration)
  • A production-ready system (that comes later)
  • A pilot running with real users (that's a pilot, which usually follows the PoC)
  • A research project with no defined success criteria (that's exploration)

A good PoC uses your actual data, addresses your specific problem, and produces measurable results against predefined criteria. It takes weeks, not days, and costs real money. But it's a fraction of the cost of a full production build, and it gives you the information you need to make an informed investment decision.

The Five Elements of a Good AI PoC

1. Clear Success Criteria Defined Before You Start

This is non-negotiable. Before any development begins, write down what "success" looks like and what "failure" looks like. Get agreement from all stakeholders.

Good success criteria are:

  • Measurable: "The model correctly categorises 85% of test documents" not "the model is accurate"
  • Relevant: Connected to the business outcome you care about, not a technical metric that sounds impressive but doesn't matter
  • Achievable: Based on realistic expectations for a PoC (production performance is usually better due to more data and tuning)
  • Documented: Written down and agreed before development starts

Example success criteria for a document processing PoC:

  • The model correctly extracts key fields (name, date, amount, reference number) from at least 80% of test invoices
  • Processing time per document is under 5 seconds
  • The model handles at least 3 different invoice formats
  • False positive rate (confident but wrong) is under 5%

Example criteria for a customer service AI PoC:

  • The AI correctly answers at least 70% of test questions from the knowledge base
  • Responses are factually accurate (no hallucination of incorrect information)
  • The AI appropriately escalates questions it can't answer at least 90% of the time
  • Average response time is under 3 seconds

2. Your Actual Data, Not Synthetic or Sample Data

A PoC built on cleaned-up sample data proves nothing about production viability. It proves the AI works when everything is neat and tidy. The real question is whether it works when things are messy - which they always are.

Your PoC should use:

  • Real data from your systems: Actual invoices, actual customer enquiries, actual records
  • A representative sample: Not just the easy cases. Include edge cases, poor quality examples, and the kinds of inputs that cause problems today
  • Sufficient volume: Enough data to train and test the model meaningfully. The exact number varies by problem, but generally hundreds to thousands of examples
  • A held-out test set: Data that the model hasn't seen during development, used to evaluate performance. This prevents overfitting to the training data

If your vendor proposes building the PoC on synthetic data, push back. If there's a legitimate reason (data access restrictions, privacy concerns), make sure the synthetic data closely matches the characteristics of your real data - and understand that results may not transfer perfectly.

3. A Realistic Technical Approach

The PoC should use an approach that can scale to production. A PoC built with manual processes, hardcoded rules for your test data, or approaches that won't work at volume gives misleading results.

Ask your vendor:

  • "Is this the same approach you'd use in production, or is this a simplified version?"
  • "What would need to change to move this from PoC to production?"
  • "Are there any shortcuts in the PoC that would need to be replaced for production?"

Some simplification in a PoC is normal and expected. The user interface might be basic. The integration might be manual. The infrastructure might be temporary. But the core AI approach - the model, the data pipeline, the inference logic - should be representative of what production would look like.

4. Transparent Evaluation

You should be able to see exactly how the PoC was evaluated, including the cases it got wrong.

Look for:

  • A clear test dataset that you can review
  • Per-example results showing what the model predicted vs. the correct answer
  • Error analysis explaining why the model made mistakes and what could be done about them
  • Confidence scores showing how certain the model was about each prediction
  • Edge case analysis showing how the model handles unusual or difficult inputs

Avoid vendors who only show you the aggregate accuracy number. "87% accuracy" sounds good, but you need to understand the 13% it got wrong. Are those errors random and distributed, or are they concentrated in a specific document type or scenario that represents a large portion of your real-world volume?

5. An Honest Assessment of Production Viability

The PoC report should include a clear, honest assessment of whether the results justify moving to production. This includes:

  • Performance gap analysis: How far are the PoC results from the production success criteria? Is the gap closable with more data, better data, or model tuning?
  • Data requirements for production: What data improvements are needed? More volume? Better quality? Additional labelling?
  • Technical requirements for production: What needs to be built to turn this into a production system? APIs, integrations, monitoring, security?
  • Estimated effort and cost: A realistic estimate of what the production build will require
  • Risks and mitigations: What could go wrong and how would you address it?
  • Recommendation: Should you proceed? Proceed with modifications? Or stop?

A vendor who always recommends proceeding to production, regardless of the PoC results, isn't being objective. Sometimes the right answer is "the data doesn't support this use case" or "the accuracy isn't high enough to justify the investment."

Red Flags in an AI PoC

The Results Are Too Good

If the PoC delivers 99% accuracy on a complex problem, be suspicious. Either the test set is too easy, the model has seen the test data during training (data leakage), or the evaluation criteria are too lenient.

Ask: "What does the hardest 10% of the test set look like? How does the model perform on just those cases?"

The Vendor Won't Show You the Errors

If the vendor presents accuracy numbers but is reluctant to walk you through specific errors, something is wrong. Understanding errors is more informative than understanding successes. The error patterns tell you where the model struggles and whether those struggles matter for your use case.

The Test Set Is Too Small

Evaluating a model on 20 examples isn't statistically meaningful. A small test set can produce wildly different accuracy numbers depending on which examples are included. For most business AI applications, you want at least 200-500 test examples to get a reliable accuracy estimate.

The PoC Was Built in a Few Days

If the vendor claims to have built a meaningful PoC in two or three days, they've either used your problem as a thin wrapper around an existing demo or they've skipped important steps. A proper PoC involves data analysis, preprocessing, model development, testing, evaluation, and reporting. That takes weeks, not days.

No Error Analysis or Failure Mode Discussion

A PoC report that only talks about what worked is incomplete. You need to understand how the system fails, how often it fails, and what the consequences of failure are. If the vendor doesn't include this analysis, ask for it explicitly.

The Demo Uses Cherry-Picked Examples

Watch for this during live demonstrations. If the vendor shows you five examples and they all work perfectly, ask to try some random examples from your data. A system that works on curated examples but fails on random ones is not ready.

How to Structure the PoC Engagement

Define Scope Tightly

A PoC should be narrow in scope but deep in evaluation. Don't try to prove five things at once. Pick the most important question and answer it thoroughly.

Good scope: "Can we automatically extract the 10 key fields from standard supplier invoices with 85% accuracy?"

Bad scope: "Can AI improve our entire accounts payable process?"

Set a Fixed Timeline

PoCs should have a hard deadline. Four to eight weeks is typical for most business AI problems. A longer PoC isn't necessarily a better PoC - it often means scope creep or insufficient focus.

Agree on Deliverables

At minimum, the PoC should deliver:

  1. A working prototype demonstrating the AI approach
  2. Quantitative results against the predefined success criteria
  3. A test dataset with per-example results
  4. An error analysis explaining failure modes
  5. A written assessment of production viability
  6. An estimate of production build effort and cost

Budget Appropriately

In the Australian market, AI PoC engagements typically range from $30,000 to $100,000 depending on complexity. This might seem significant for a "proof" exercise, but it's a small fraction of a production build that could cost $200,000-$500,000+. Better to spend $50K proving the approach works than $300K building something that doesn't.

Evaluating PoC Results - The Decision Framework

When the PoC is complete, you need to make a decision. Here's how to think about it.

Green Light - Proceed to Production

The PoC results meet or exceed the success criteria. The error analysis shows manageable failure modes. The production estimate is within budget. The vendor's assessment is positive with a clear plan.

Questions to confirm:

  • Do the results translate to meaningful business value?
  • Is the production estimate credible given the PoC experience?
  • Are there any risks that could derail the production build?
  • Does the team have capacity and budget for production?

Yellow Light - Proceed With Modifications

The PoC results are promising but below the success criteria. The error analysis suggests specific improvements that could close the gap (more data, better preprocessing, a different model architecture). The vendor has a credible plan to improve performance.

Questions to ask:

  • What specifically needs to change to reach the success criteria?
  • How confident is the vendor that the improvements will work?
  • What's the additional cost and time to implement the improvements?
  • Should you run an extended PoC before committing to production?

Red Light - Do Not Proceed

The PoC results are significantly below the success criteria. The data doesn't support the use case. The error analysis reveals fundamental problems that can't be easily fixed. Or the production estimate far exceeds the business value.

This isn't a failure - it's a success. You've spent a modest amount to learn that this approach doesn't work, saving yourself from a much larger investment that would have failed.

Questions to ask:

  • Is the problem unsolvable with AI, or is it unsolvable with current data?
  • Would a different AI approach work better?
  • Is there a simpler version of this problem that AI could solve?
  • Should you invest in data improvement and revisit later?

After the PoC - Bridging to Production

If you green-light the project, don't assume the production system will just be a bigger version of the PoC. The gap between PoC and production includes:

  • Infrastructure: Production-grade hosting, monitoring, and scaling
  • Integration: Connecting to your real business systems via APIs and data pipelines
  • Security: Authentication, authorisation, data encryption, audit logging
  • Error handling: What happens when the AI fails, times out, or receives unexpected input
  • User interface: A proper interface for end users, not the basic UI from the PoC
  • Monitoring: Tracking model performance over time to detect degradation
  • Retraining pipeline: A process for updating the model as new data becomes available
  • Testing: Automated tests for the model, the integrations, and the overall system
  • Documentation: Technical documentation, user guides, and operational runbooks

A good vendor will outline all of these requirements as part of the PoC deliverables, so you go into the production phase with clear expectations.

Getting Started

If you're considering an AI PoC, the most important thing you can do is define clear success criteria before engaging a vendor. Know what you're testing, how you'll measure it, and what result would justify the next step.

At Team 400, we run structured AI proof of concept engagements for Australian businesses. Every PoC we deliver includes the five elements outlined in this article - clear criteria, real data, a realistic approach, transparent evaluation, and an honest assessment of production viability.

Explore our AI consulting services, learn about our AI development approach, or contact us to discuss a PoC for your use case.