Takeaways: Running data-driven evaluations of AI engineering tools

AI engineering tools now change faster than most procurement cycles. That creates a real problem for leaders: decisions get locked in long after the tools and their impact have shifted.

In a live discussion, DX CEO Abi Noda and CTO Laura Tacho walked through how they’ve seen teams successfully evaluate AI tools in practice: when to re-evaluate, how to design cohorts that actually hold up, and which signals matter when separating hype from real impact. What follows is a practical distillation of that conversation, focusing on how to run evaluations systematically. You can rewatch the full session here, or listen to it on your favorite podcast platform.

How often should teams re-evaluate AI engineering tools?

Abi: A good cadence is every 8 to 14 months. If you are on one-year contracts, plan for a re-evaluation about four months before renewal so you have time to collect baselines and run a true comparison. Outside of the renewal cycle, budget cycles, private previews, and developers organically adopting new tools should prompt re-evaluations. These tools change fast, so most organizations are only a few months away from re-evaluations at any given moment.

Laura: Most leaders should be re-evaluating their incumbent tools more often, but in practice, nothing naturally forces them to do so. New tools create obvious triggers, but the tools you already use get ignored, even though the AI category moves faster than anything else in engineering.

The incumbent tools need continuous measurement so you understand how they are performing before you compare them to challengers. If you introduce a new tool without re-evaluating the one you already have, you end up testing the challenger without baseline data, from which you can’t draw meaningful conclusions.

How many tools should you evaluate at once, and how should cohorts be designed?

Laura: The largest evaluation I have seen is six tools at the same time, but that was a company with enough developers to run six clean cohorts where the only variables were the tools themselves. Most organizations can realistically compare one or two challengers alongside their incumbent. Org size determines what is possible. If you cannot create cohorts with a shared baseline and enough developers to produce reliable data, your evaluation will not hold up.

Abi: You also have to separate tools by category. IDEs, agentic environments, chat tools, and code review assistants serve different purposes, so each category needs its own isolated experiment. You cannot stack them together.

The subjects you choose matter as much as the tools. Results vary across seniority levels, programming languages, tech stacks, and even project types. Diversity across these dimensions is what makes the evaluation reliable and representative of the entire engineering organization.

Who should participate in the trials, and how do opt-in vs assigned cohorts impact results?

Laura: This decision has a huge impact on the quality of your data. Opt-in cohorts only give you early adopters, which means you miss the signals that matter most at rollout. To understand the real adoption, enablement, and training challenges, you need developers who would not have volunteered on their own. Those are the cases that reveal what is not working, what is hard to onboard, and where the tool will struggle once it reaches the full organization. Assigned cohorts give you that visibility, and that data is often the most valuable part of the entire evaluation.

Abi: Diversity across the cohort is essential. Junior and senior engineers often produce very different results. Different programming languages, tech stacks, and project types all influence how a tool performs. When you bring together a mix of these groups, you get a clearer view of the tools’ strengths and limitations across the whole engineering organization. That range of perspectives is what makes the evaluation reliable.

How does enablement influence the outcomes of an AI tool evaluation?

Laura: Enablement is one of the biggest drivers of evaluation outcomes, and leaders often overlook it. To get decision-grade data, every tool in the evaluation needs the same level of onboarding, documentation, and support. I have seen organizations invest heavily in one tool and barely enable another, and the results become completely distorted. Treat enablement like a controlled variable. Give each cohort the training they need to use the tool in real day-to-day work, then measure performance once habits have formed.

Abi: If one tool gets more training, guidance, or internal champions, it will look better even if it is not the stronger option. Leaders should standardize onboarding, provide simple documentation, and make sure each team knows how to get value from the tool before collecting data. Design enablement to match how the tool would be rolled out in real life. If your long-term plan is team-level adoption, support the evaluation at the team level too. When enablement is consistent, the results reflect real differences between tools rather than differences in how much help users received.

How do you structure a tool trial around data instead of developer opinions?

Laura: Developer satisfaction is absolutely part of a healthy evaluation, but it cannot be the only signal. CSAT should be collected across the full cohort, not just from early adopters. To make the results meaningful, every evaluation needs to start with a clear research question that ties directly to a business outcome. Are you trying to reduce migration toil, increase throughput, create more time for innovation, or speed up delivery? AI only creates value when it is pointed at a specific problem, and that research question guides everything that follows, including cohort design, enablement, and the metrics you collect.

Abi: Developer feedback matters, but it needs to be paired with outcome data. Look at PR throughput, cycle time, change failure rate, and the developer experience signals that consistently correlate with adoption and impact. These metrics show whether the tool is improving flow, removing friction, or helping people ship work faster. When the evaluation starts with a research question, the results become easier to interpret and more reliable because you know exactly what you set out to measure.

How long should an AI tool evaluation run to produce reliable results?

Laura: The evaluation needs enough time for developers to build real habits with the tool. Anything shorter than a full development cycle does not surface how the tool affects teams once it becomes part of their everyday work. Most organizations land in the 6 to 12 week range because it gives multiple sprints for enablement, onboarding, and daily use. Shorter trials only capture first impressions, not actual workflow changes.

Abi: Duration should be long enough to collect baseline data, run the evaluation, and review the results before you reach the point where procurement decisions need to be made. When teams don’t leave enough room for that window, they end up rushing the evaluation or relying on anecdotal feedback instead of real data.

What metrics and data should leaders use to compare AI tools and justify decisions to the business?

Laura: Baselines are essential. If you already measure engineering performance with something like the DX Core 4, you can segment cohorts and see how AI affects throughput, quality, flow, and time for innovation.

If you are new to measuring AI, DX’s AI Measurement Framework gives you a structured way to track utilization, impact, and quality signals. Break results down by attributes like seniority, tenure, and technology stack, because different groups will experience the tools differently. Alongside impact metrics, bring in the same procurement criteria you use for any other tool, including security, pricing, support, and the vendor’s roadmap.

Abi: There is no single metric that tells the whole story. Use a consistent scoring model across all tools so you can compare them in a fair way. And make sure the work you measure reflects real development, not prototype building or short-term experiments. Tools need to be evaluated in the same conditions they will be used day to day, because that is the only way to see true differences in adoption and impact.