Running data-driven evaluations of AI engineering tools

Engineering Enablement Podcast

0:00

-37:34

Running data-driven evaluations of AI engineering tools

A concise, data-driven framework for testing and adopting AI engineering tools.

Abi Noda

Dec 12, 2025

Listen and watch now on YouTube, Apple, and Spotify.

AI engineering tools are evolving fast. Every month brings new coding assistants, debugging agents, and automation capabilities. I want to help engineering leaders take advantage of that innovation while avoiding costly experiments that distract from real product work.

In this episode, Abi Noda and I share a practical, data-driven approach to evaluating AI tools. I walk through how to shortlist tools by use case, design structured trials that reflect real work, select representative participants, and measure impact using baselines and proven frameworks. My goal is to give you a way to test and adopt AI tools with confidence and a clear return on investment.

Some takeaways:

Data-driven evaluations are essential

Structured, measurable trials prevent bias. Without them, decisions are driven by novelty hype or a few loud voices.
Define a clear business outcome first (reduce toil, improve delivery speed, or raise code quality).
Evaluations must inform real decisions, not just check a procurement box.

Choose the right set of tools to evaluate

Group tools by use case and interaction mode (chat, agentic IDEs, code review assistants, etc.) to ensure fair comparisons.
Match shortlist size to org capacity to support multiple cohorts and reliable results.
Multi-vendor strategies reduce lock-in in a rapidly shifting market.

Re-evaluations are essential, not optional

Incumbent tools must be retested as capabilities evolve and new challengers emerge.
Triggers for re-evaluation include major feature launches, organic developer adoption of a new tool, and upcoming renewal cycles.
Every challenger tool evaluation requires a baseline of the incumbent, so you can compare like-for-like.
A cadence of every 8–14 months ensures decisions reflect the current reality, not the past purchase.

Design trials around research questions

Start with a hypothesis. It keeps experiments aligned to actual goals.
Developer sentiment is necessary but insufficient without measurable outcomes.
Success criteria must be defined in advance to avoid subjective decision-making.

Select representative participants

Diverse cohorts reveal real impact across languages, teams, and seniority levels.
Include skeptical and late adopters to uncover onboarding and enablement needs.
Volunteer-only trials distort results and won’t scale to full org rollout.

Run evaluations long enough to capture true behavior

Eight to twelve weeks is the minimum to get past the novelty phase and into sustained usage.
Align evaluation windows to procurement cycles so insights guide buying decisions.
Short trials lead to false signals and either inflate enthusiasm or create false negativity.

Use self-reported time savings carefully

Self-reporting is a strong early indicator of perceived usefulness.
Humans misremember time, often benchmarking against recent AI use.
Treat CSAT and time savings as directional, not the final truth.
Objective metrics validate real ROI, including throughput, quality, and innovation time.

Expect variation rather than a single winner

Different tools shine in different contexts, so multiple standards are often the best path.
Continuous re-evaluation is required as capabilities evolve every quarter.
The right goal isn’t the “best tool”, but the best tool for each problem space.