PR volume and deployment frequency are up, but the roadmap is still slipping. The numbers may be accurate and still fail to answer the question leadership cares about: Is AI improving delivery, quality, and business value at a defensible cost?
Most engineering organizations already have plenty of data. GitHub Copilot, Cursor, Claude Code, and other tools report usage within their own products. CI/CD systems show delivery performance. Finance sees invoices, while engineering leaders see pull requests, deployments, and incidents.
The problem is fragmentation. Tool dashboards rarely show whether usage changed delivery outcomes, whether code quality held up, or whether the organization generated enough value to justify the full cost.
Each measurement framework also serves a different purpose. DORA’s current model uses five software delivery metrics spanning throughput and instability. The SPACE framework broadens the view across satisfaction, performance, activity, communication, and efficiency. Both reinforce the same principle: developer productivity can’t be reduced to one activity count.
AI adds another attribution problem. A deployment may include human-written code, inline AI assistance, and autonomous agent output. Without separating those inputs, executives can see that delivery changed but not whether AI caused the change, whether the result was durable, or what it cost.
Start with delivery performance, not code-generation activity. Deployment frequency and change lead time need to sit beside change fail rate, deployment rework rate, and recovery time.
For AI-assisted teams, add:
Larridin’s Developer AI Impact Framework connects adoption, code share, throughput, quality, and cost so leaders can interpret delivery changes in context.
Seat counts show access, and weekly active users show basic adoption. But neither tells leadership whether engineers have integrated AI into useful workflows or developed the proficiency to generate reliable outcomes.
Segment adoption by team, role, tool, and use case. Distinguish occasional users from engineers who use AI consistently for work that affects delivery. Show proficiency beside adoption so leaders can see where enablement, workflow redesign, or stronger guardrails may create more value than additional licenses.
Larridin’s AI Adoption dashboard provides team-level adoption, usage, cost, and governance visibility, while AI proficiency adds the capability lens.
Leaders need total AI coding cost by tool, team, and use case, including seat licenses, usage-based charges, and agent activity.
Pair cost with durable output, delivery improvement, and quality. Also surface idle licenses, sudden usage spikes, and teams whose costs are rising without a corresponding improvement in outcomes.
Token Spend & Insights consolidates AI spend across tools and agents, giving finance and engineering a shared cost view.
The final layer connects engineering signals to the outcomes executives fund: roadmap progress, product delivery, customer impact, risk reduction, and financial return.
It should show whether AI-assisted work helped teams deliver important initiatives faster while maintaining or improving quality. It should also expose tradeoffs, such as higher deployment frequency paired with missed roadmap commitments or shorter lead time paired with more rework.
Larridin’s AI Impact platform connects AI activity to engineering productivity and business outcomes, turning adoption data into evidence leadership can use for investment decisions.
A board-level dashboard should emphasize decisions, not exhaustiveness.
The dashboard should help an executive answer four questions quickly:
DORA measures software delivery performance. It doesn’t identify which work was AI-assisted, how deeply teams use AI, what the tools cost, or whether AI-generated code is durable. DORA is still valuable, but AI attribution, quality, adoption, proficiency, and cost provide the context executives need.
AI code share is the portion of committed work associated with AI-assisted development. It’s a context metric, not a performance score. Paired with delivery, quality, and cost signals, it helps leaders see whether rising AI involvement corresponds with better outcomes.
Operational data should refresh often enough for engineering and finance leaders to catch cost spikes, quality regressions, and adoption problems before they compound. A summarized executive view can be reviewed monthly, with a more strategic version prepared for quarterly or board reporting.
Use DORA for software delivery performance and SPACE for the broader, multidimensional productivity perspective. Then add AI-specific measures for attribution, adoption, proficiency, code durability, complexity-adjusted throughput, spend, and business value. The goal is a connected measurement system, not a stack of unrelated framework dashboards.
Larridin connects AI adoption, proficiency, spend, engineering outcomes, and business impact in one executive-ready measurement system. Leaders can see where AI is creating value, where risk or cost is building, and what to do next.
Book a discovery call to see the executive dashboard in action.