7 key decisions when setting up an AI tool POC evaluation

AI vendors are leap-frogging each other every month, and many companies feel the FOMO of potentially missing out on modern tools. It’s tempting to run evaluations just to check a box in the procurement process—but a limited-scope trial should be more than that. It’s your chance to make sure a tool will actually deliver results for your organisation: improving developer experience, accelerating development, and ensuring your engineering practices still align with your foundational definition of excellence.

Right now is especially critical: companies are planning their budgets for 2026, making it the perfect moment to align AI pilots with strategic priorities and gather evidence to make smarter investment decisions that will pay off next year.

A limited-scope POC can be a playground, or it can be a decision-making tool. Structure is what makes the differences

I’ve interviewed 10+ engineering leaders running POCs at scale (across 100+ engineers) and reviewed data from hundreds of companies. I wanted to see what patterns separated the ones achieving real results: adoption at scale, measurable time savings per developer, and faster time to market.

Key decisions to structure an AI tool evaluation

These seven decisions were key for running a structured trial that produced better decisions and set them on a path to steady adoption.

• Goals: Know exactly what you’re trying to achieve. Are you consolidating vendors, finding new use cases, deciding which teams get more budget, or something else? If the goal is fuzzy, your results will be too.
• Tools: Pick what’s in scope and why. Are you trialing one tool or comparing several? Who gets to nominate candidates for trial? Are you asking devs, or is a centralised team putting forth vetted candidates?
• Cohort: Arguably the most important decision you make if you want to see how these tools will perform *across your organisation*, and not just tools to boost individual productivity. Who gets to participate in the trial? Will they volunteer or be assigned? If assigned, what’s the logic: by team, by tenure, by skillset?
• Duration: Set an end date and make it long enough to catch multiple delivery cycles. Too short and you miss the real picture; too long and you lose momentum.
• Metrics: Measure impact the same way you’ll measure it at scale. Start with the data you already have, add what you need, and be clear on how you’ll capture it. Look at the AI Measurement Framework for guidance on what to measure.
• Scoring: Impact matters most, but it’s not the only thing. Procurement alignment, security certifications, integration ease—all should be part of a standard scoring framework.
• Decision ownership: Agree now on who decides and how that decision will be communicated. Remember, this isn't just a checkbox in the procurement process. No is also a decision.

The companies that get AI adoption right don’t just stumble into it. They run disciplined experiments, make clear decisions, and build on what works. That said, structure isn’t the enemy of innovation. You still need “throw spaghetti at the wall” time to discover what’s new and surprising. But that’s for exploration. When it comes to major purchasing decisions, structure is what keeps you from buying into hype and ensures you’re investing in tools that serve both your developers and your organisation.