RESOURCE · AI EVALUATION
Should we scale it?
A practical guide to deciding whether an AI proof of concept is worth further investment.
It’s easier than ever to build something impressive with AI. But demonstrating that AI can do a task isn’t the same as proving that changing the way people work is worthwhile.
The Avian AI PoC Evaluation Framework brings together five questions, seven principles and practical models to help organisations assess the value of AI-enabled workflows — and decide whether to scale, iterate or stop.
A proof of concept should end in a decision, not a demo.
AI PoCs should be designed as experiments. Their purpose isn’t simply to prove that the technology works, but to generate enough evidence to make an informed investment decision.
That means understanding how the work performs today, defining what success would look like, and measuring the complete workflow — including the human work that remains.
Our framework provides a practical way to do that.
Five questions before you scale AI
- Is it the right PoC?
Start with what’s not working today. Is there a meaningful opportunity to improve the work, and is it worth testing? - What does it have to beat?
Establish a baseline. Understand current outcomes, quality, effort, costs and failures before claiming improvement. - What would make this genuinely better?
Define the standards that cannot be compromised and the improvements that would justify changing how people work. - Does it hold up in real work?
Test representative work, consequential failures and the full human + AI workflow — not just a convincing demonstration. - Is the evidence good enough to change how we work?
Weigh the demonstrated benefits against the costs, risks and effort of adoption. Then decide whether to scale, iterate or stop.
Look beyond the tools.
The framework includes practical models for evaluating things that are easy to miss when a proof of concept looks successful.
Evaluate the work, not just the model.
Start with outcomes for users and the organisation, then assess outputs, workflows and the underlying technology.
Count the work AI shifts downstream, not just what it removes.
AI might eliminate drafting while introducing new work in checking, editing, verification and oversight. Measure total human effort, not just generation time.
Don’t optimise away useful friction.
Having a human in the loop doesn’t guarantee meaningful oversight. The evaluation needs to test whether people can actually identify and challenge failures.
Grounded in real delivery.
We’ve applied this approach in evaluating an AI-assisted content workflow for an Australian Government regulator.
In a small proof of concept that tested the impact of AI on content development, AI assistance reduced the observed time to a draft ready for subject matter expert review from around six hours to 30 minutes, including content-designer review and editing.
The evaluation also revealed where quality needed further work and where human expertise remained essential.
Get the framework
The Avian AI PoC Evaluation Framework
Five questions. Seven guiding principles. Practical models to help you evaluate whether AI-enabled work is worth the investment.
The framework is an investment decision tool. It complements, rather than replaces, technical evaluation, AI assurance, security, privacy and regulatory requirements.
Need evidence before you invest?
Avian helps organisations make better decisions about technology and new ways of working.
Whether you’re choosing an AI use case, designing a PoC or deciding what to do with an existing experiment, we can help you establish the baseline, define success, evaluate the complete workflow and make sense of the evidence.