Back to Blog

AI Proof of Concept Cost - What to Budget Before You Commit

April 4, 2026•10 min read•Michael Ridland

An AI proof of concept (PoC) in Australia typically costs between $15,000 and $60,000 AUD and takes 2-6 weeks. That's a wide range, so let me explain what drives the number up or down and what you should expect to get for your investment.

The short version: a PoC is the single most important investment in any AI project. It tells you whether your idea works with your actual data, not theoretical data. It gives you real accuracy numbers, real performance metrics, and evidence to make a go/no-go decision. Skipping the PoC to save money is the most expensive mistake we see businesses make.

What an AI Proof of Concept Actually Is

A PoC is a working prototype that tests your core hypothesis with real data. It's not a demo. It's not a slide deck. It's working software that proves (or disproves) that AI can solve your specific problem at an acceptable level of accuracy and cost.

What a PoC should deliver:

  • A working prototype using your actual data (not synthetic or sample data)
  • Accuracy and performance measurements against your success criteria
  • An honest assessment of what works well and what doesn't
  • Technical architecture for how this would scale to production
  • Cost estimates for the production build and ongoing operations
  • A clear go/no-go recommendation with evidence

What a PoC should not be:

  • A polished product with a beautiful UI (functional is fine)
  • A commitment to build the full system
  • A demo using cherry-picked examples that make the technology look better than it is
  • A strategy presentation that discusses AI in general terms

PoC Cost Ranges by Type

Simple PoC ($15,000-$25,000)

Testing a single, well-defined use case with one data source and no system integrations.

Examples:

  • Can AI accurately classify customer enquiries into 10 categories using your historical email data?
  • Can AI extract key fields from your specific document types with 90%+ accuracy?
  • Can AI summarise your meeting transcripts in a way that's useful for your team?

What you get:

  • Data assessment (is your data suitable?)
  • Working prototype testing the core capability
  • Accuracy metrics on a representative sample of your data
  • Brief report with findings and recommendation

Timeline: 2-3 weeks

This is the right level for straightforward use cases where the primary question is "does this work with our data?" The scope is narrow, the data source is single, and there's no integration complexity.

Standard PoC ($25,000-$45,000)

Testing a use case that involves multiple data sources, some business logic, or a basic system integration.

Examples:

  • Can an AI agent handle your top 5 customer service scenarios by pulling information from your CRM and knowledge base?
  • Can AI process your incoming invoices, extract data, match to purchase orders, and flag exceptions?
  • Can AI analyse your sales calls, identify key topics, and produce actionable summaries?

What you get:

  • Detailed data assessment across multiple sources
  • Working prototype with basic workflow logic
  • 1-2 basic system integrations (read-only is fine for a PoC)
  • Accuracy metrics across multiple scenarios
  • Performance benchmarks (speed, throughput)
  • Technical architecture document for production
  • Production cost estimate
  • Detailed findings report with go/no-go recommendation

Timeline: 3-5 weeks

This covers most business use cases. The added complexity comes from multiple data sources, some decision logic, and basic integration work.

Complex PoC ($45,000-$60,000)

Testing a use case with significant complexity - multiple decision paths, several system integrations, compliance requirements, or challenging data quality.

Examples:

  • Can an AI agent handle a full loan application workflow - document collection, data extraction, credit assessment, and recommendation - while meeting regulatory requirements?
  • Can AI process and reconcile data across three legacy systems with inconsistent formats and incomplete records?
  • Can AI accurately triage medical reports based on urgency, extract clinical findings, and route to the correct specialist team?

What you get:

  • Everything from the standard PoC
  • Multiple system integrations
  • Complex workflow testing
  • Compliance and security assessment
  • Edge case and failure mode analysis
  • Detailed risk assessment
  • Production implementation plan with phased delivery

Timeline: 4-6 weeks

This level is for high-value, high-complexity use cases where getting it wrong is expensive. The extra budget goes into testing edge cases, understanding failure modes, and assessing compliance implications.

What Makes a PoC More Expensive?

Data Quality Issues

If your data is messy, inconsistent, or spread across multiple systems in different formats, significant time goes into data preparation before the actual AI testing can begin. We budget 20-40% of PoC time for data work, but sometimes it's more.

Warning sign: If the team running your PoC doesn't ask about your data quality early, they're either assuming it's fine (risky) or planning to use synthetic data (not useful).

Multiple Scenarios

Testing one scenario is quick. Testing ten scenarios takes longer because each one needs its own test data, evaluation criteria, and tuning. Prioritise your scenarios ruthlessly - test the two or three highest-value ones in the PoC and save the rest for the production build.

Integration Complexity

A PoC that needs to read from a well-documented modern API is straightforward. A PoC that needs to connect to a legacy system with poor documentation, no sandbox environment, and limited API support will spend significant time just getting the connection working.

For the PoC phase, consider whether you can provide data exports instead of live integrations. A CSV extract from your legacy system might be enough to prove the concept without the cost of building a full integration.

Compliance Requirements

If the PoC involves regulated data (personal information, financial records, health data), you need to handle it properly even in a prototype. This means secure environments, access controls, and data handling procedures that add to the cost.

Stakeholder Expectations

A PoC that needs a polished demo for the board costs more than one that just needs to prove the concept to a technical team. If you need a presentable interface, budget accordingly - but consider whether a screen recording of the working prototype might be sufficient.

The ROI of Running a PoC

A $30,000 PoC might seem expensive until you consider the alternatives.

Scenario 1 - PoC reveals the idea doesn't work: You've spent $30,000 to avoid spending $150,000-$300,000 on a production build that would have failed. That's a $120,000-$270,000 saving.

Scenario 2 - PoC reveals the idea works but differently than expected: The PoC shows that your original approach gets 75% accuracy, but a modified approach gets 93%. You adjust the production plan based on evidence rather than assumptions. This typically saves 20-40% of the production budget by avoiding costly mid-project pivots.

Scenario 3 - PoC confirms the idea works well: You now have evidence to secure budget for the full build. You have accurate cost and timeline estimates because they're based on real data, not guesses. And you've de-risked the project for everyone involved.

In our experience, about 70% of PoCs lead to production builds. The 30% that don't are just as valuable - they prevent bad investments.

How to Run a Good PoC

Define Success Criteria Before You Start

"Does AI work for this?" is not a success criterion. "Can AI classify customer enquiries into our 10 categories with 90%+ accuracy on a sample of 500 real emails?" is a success criterion.

Be specific about:

  • What accuracy level is acceptable?
  • What response time is required?
  • What edge cases must be handled?
  • What would make this a "no-go"?

Use Real Data

This is non-negotiable. A PoC that uses synthetic data or cherry-picked examples proves nothing. Your production data has noise, inconsistencies, edge cases, and formatting variations that synthetic data doesn't capture. If the PoC works on clean demo data but fails on your real data, you've wasted your budget.

Provide a representative sample - ideally including your hardest cases, not just your cleanest ones.

Involve the Right People

The PoC needs someone from the business side who understands the problem and can evaluate whether the AI's output is correct. Technical teams can measure accuracy, but only domain experts can judge whether the results are actually useful in practice.

Set a Time Box

A PoC should be time-boxed. If you can't prove the concept in 2-6 weeks, either the scope is too large (break it down) or the problem is too hard for current AI capabilities (which is useful to know).

Don't let a PoC drag on. If results at week 4 are poor, running for another 4 weeks rarely changes the outcome.

Accept Imperfection

A PoC is not a finished product. The code won't be production-ready. The UI won't be polished. Some edge cases won't be handled. That's fine - the purpose is to prove the concept, not deliver the final product.

Judge the PoC on whether it answers your core question, not on whether it's ready to deploy.

Red Flags in PoC Proposals

  • No mention of your data. A PoC proposal that doesn't discuss how they'll access and work with your actual data is a proposal for a demo, not a PoC.
  • Guaranteed results. No honest AI practitioner guarantees accuracy numbers before seeing your data. Estimates are fine. Guarantees are fiction.
  • No defined success criteria. If the proposal doesn't include how success will be measured, how will you know if it worked?
  • PoC and production quoted together with no decision point. A PoC should have a clear go/no-go gate. If the vendor bundles PoC and production into one engagement with no exit point, they're not giving you the option to walk away.
  • Longer than 6 weeks. If a PoC takes longer than 6 weeks, the scope is too large. Either narrow it or run it as a phased project.

After the PoC - What Happens Next?

A good PoC engagement ends with a clear recommendation:

Go: The concept works. Here's the architecture, cost estimate, and timeline for the production build. Here are the risks and how to mitigate them.

Go with modifications: The concept works, but not exactly as originally scoped. Here's what worked, what didn't, and what we'd recommend for the production approach.

No-go: The concept doesn't work well enough to justify the production investment. Here's why, and here's what would need to change (better data, different approach, wait for better models) for it to become viable.

All three outcomes are valuable. You've made an informed decision with evidence, not a gut feeling.

How We Run PoCs at Team 400

At Team 400, we've run dozens of AI proof of concept engagements for Australian businesses. Our typical PoC runs 2-4 weeks and costs $20,000-$50,000 depending on complexity.

We use your real data, define clear success criteria upfront, and deliver an honest assessment at the end - including when the answer is "this isn't the right use case for AI right now."

Our PoCs are designed to give you the evidence you need to make a confident investment decision. No sales pressure, no guaranteed outcomes, just engineering rigour applied to your specific problem.

If you're considering an AI project and want to test the concept before committing, reach out. We'll help you define the right scope for a PoC that answers your most important questions.

Learn more about our AI consulting approach or explore our AI agent development services.