How to introduce AI impact metrics into an engineering organization
As AI coding assistants and agents become more integrated into engineering workflows, tech leaders are increasingly under pressure to understand their true impact. This is a complex problem for many companies. Not only do you need to have confidence in what to measure and how to collect the data, you also need to roll out metrics in a way that reinforces the role of AI as a tool to support developers. Metrics are in place to improve the system, not to scrutinize developers’ daily work habits.
If you’re beginning to roll out AI tooling and are wondering how to measure its impact thoughtfully, here are four best practices to anchor your approach.
Focus on team-level aggregation
When tracking metrics like tool adoption or code suggestions accepted, always aggregate at the team level. Individual-level metrics, especially when tied to performance evaluations, undermine trust and create perverse incentives. Developers may feel surveilled, discouraged from using AI tools, or unfairly judged for how often they rely on them.
Remember: these tools are meant to support teams, not replace individuals. Aggregating at the team level protects psychological safety and helps ensure you’re measuring patterns that reflect meaningful workflows, not just individual quirks.
Communicate clearly and often
Many developers are still figuring out how (or whether) to use AI tools in their day-to-day work. If you’re collecting data on usage or outcomes, it’s critical to be transparent about why you’re doing it, what will (and won’t) be done with that information, and how it supports a broader organizational goal.
Too often, teams hesitate to engage with AI tools because they’re unclear on the risks: Will this count against me? Does this violate copyright or licensing? Clear internal communication can remove this ambiguity. Position your measurement efforts as part of a shared, exploratory journey.
Treat it as an experiment
Rolling out AI tooling is not an on or off switch. Think of it instead as a series of experiments. Define a baseline for your core engineering metrics, like those in the DX Core 4, then track how AI tooling influences those metrics over time.
Supplement quantitative indicators with qualitative feedback. Are developers using the tools because they’re genuinely helpful, or because they feel pressured to? Are the tools aiding focus and flow, or creating new friction? Combining utilization, impact, and cost data will give you a fuller picture of what’s working and where to iterate.
Remember the bigger picture
AI is not a silver bullet. While it can unlock impressive short-term productivity gains, it won’t automatically address deeper bottlenecks in systems, tooling, or team dynamics. The real value of AI tools lies in how they improve developer experience, and developer experience is what ultimately drives better organizational outcomes. Don’t let the excitement of AI distract from long-term investments in code quality, infrastructure, onboarding, and feedback loops. The most successful teams treat AI as one part of a larger strategy to build high-performing, sustainable engineering cultures.
AI coding tools hold real potential to reshape how teams build software, but realizing that potential requires thoughtful stewardship. These measurements help you make better decisions about your AI strategy and help guide sustainable change. By focusing on team-level trends, communicating openly, and treating AI adoption as an iterative process, engineering leaders can foster both trust and transformation.