How do you tell if AI tools are genuinely helping your engineers, instead of just adding extra steps or confusion?

We believe in the mantra: “If you can’t measure it, you can’t improve it.” That’s why we track the usage of GitHub Copilot across our development teams  not to micromanage, but to understand impact.

In our Java teams, we’ve observed that roughly one-third of the code is now written with the help of Copilot. That’s not just a statistic, it’s a signal. A signal that AI is becoming a true co-pilot, not a backseat driver.
Of course, productivity is only one side of the coin. We also monitor the use of other AI services across the organization, especially from a security and compliance perspective. Shadow AI is real, and we treat it with the same seriousness as shadow IT.

Ultimately, the goal is simple: AI should reduce friction, not add it. If it’s not making life easier for our engineers, it’s not doing its job.

In our case, AI tools are being integrated into workflows with structured governance and training to support cultural change. Evaluating AI's effectiveness for engineers requires looking beyond surface metrics like code acceptance rates or the volume of AI-generated code; experts suggest focusing on Time to First Commit, Code Review Efficiency, Defect Density, Lead Time for Changes, and Cost/Revenue per Developer. Although AI can speed up development, it may also bring risks such as technical debt, security issues, and complex debugging. Effective teams, leverage AI to support, not replace, human expertise.

We assess AI ( any) tool effectiveness by measuring both outcomes and developer sentiment. If the tool genuinely adds value, we see reduced time-to-deploy, improved code quality, and fewer bugs or rework cycles. We also track adoption metrics. If engineers voluntarily use the tool and integrate it into their daily workflows, that’s a strong signal. Regular feedback loops, retrospectives, and anonymous surveys help surface whether the AI is enabling or obstructing. Tools that add steps without improving accuracy or speed are quickly flagged. The key is aligning AI with engineering goals—automation should simplify, not complicate. Piloting with small teams before wide rollout also helps prevent unnecessary complexity

If you don't already have outcome based metrics, then your best bet is just asking your engineering team.  No engineer wants to use a system that adds extra steps or confusion.

If you are using agile, then look and see if your sprint velocity has increased.  An important aspect of this is that effort estimates don't start taking AI into account.  A task that would have been a 5 prior to using AI tooling should remain a 5 after.  The tasks aren't getting less complex - the velocity of the team has increased.

Based on my experiece, a good way to assess whether AI tools are truly helping your engineers—or just creating friction—is to look at three core areas:

1. Workflow Efficiency:
Are tasks being completed faster and with fewer errors? If AI tools reduce repetitive manual work (e.g., code documentation, test case generation, bug detection), that’s a strong sign of real value. But if they require constant context-switching or create duplicative steps, they may be adding noise.

2. Developer Sentiment:
Gather direct feedback from your engineers. Are they voluntarily using the tools? Do they feel they’re getting actual support—or do they see the AI as a top-down mandate? Engagement is a key indicator of effectiveness.

3. Measurable Outcomes:
Look at concrete KPIs: commit-to-deploy time, number of pull requests merged, test coverage, bug resolution time, and even onboarding speed for junior developers. If these metrics improve post-AI adoption, you’re likely on the right track.

Ultimately, the goal is to augment human capability, not replace or burden it. The best tools are the ones engineers adopt organically because they help them do their job better—not just because leadership says so.