Boost ROI On Your
GenAI Investment

ROI on AI tooling is best measured through effectiveness of spend relative to outcomes – not raw token counts. The metrics we see engineering leaders focus on most:

• AI adoption rate – % of PRs with AI-attributed commits, by team, tool, and time period
• Cycle Time delta – are AI-impacted PRs moving faster end-to-end than non-AI work?
• Review Time trend – is reviewer burden increasing as AI adoption grows?
• Post-review change rate – a proxy for code quality; stable or improving = healthy
• AI spend per merged PR – the clearest cost-per-outcome signal
• Model-level ROI – which tools are delivering the best output per dollar?

Most customers have meaningful data within the first week. For a statistically credible ROI story, we recommend a 30-day window – enough to capture trend lines, compare AI vs. non-AI work across a representative sample of PRs, and give leadership something worth presenting. The first 30 days are typically about establishing a baseline. Days 30–90 are where the narrative gets compelling.

Milestone is built to produce executive-ready output. Dashboards are configurable for different audiences – developers see their own activity, team leads see their team, VPs see org-wide trends and spend. The “Vibe Metrics” feature lets you build custom metrics in natural language and combine signals (e.g. “show me AI spend vs. features shipped per team”) without writing code or exporting data. Everything is shareable directly from the UI.

This is the right question, and it’s one most tools can’t answer because they only measure adoption quantity, not quality. Milestone helps you get at this by correlating AI usage with outcomes: a developer with high AI adoption but rising post-review change rates and longer review cycles is a different signal than one with high adoption, shorter cycle times, and stable quality. The goal isn’t to score developers – it’s to identify where AI is being used effectively and replicate those patterns.

No. Milestone does not save customer prompts and does not train on customer data. For customers with strict data residency requirements, the on-prem deployment option keeps all raw data – including Jira fields, PR descriptions, and commit details – entirely within your own infrastructure.

Milestone achieves better-than-90% practical accuracy. Attribution works by correlating Git activity timelines with AI vendor telemetry – Copilot, Cursor, Claude, Bedrock, and others. A PR is marked as AI-impacted when 30% or more of its commits show AI involvement. Customers can audit attribution at the commit level directly in the platform.

Milestone is built for engineering leaders and managers who need a clear, reliable view of delivery across teams. It supports better decisions without disrupting engineers’ day-to-day work.

Milestone connects directly to the tools you already use, including source control and PM systems. Integration is read-only, fast to set up, and requires no workflow changes.

Teams typically see value within weeks. Milestone uses existing data to establish a baseline quickly and surface meaningful insights early.

Milestone shows how workflows, collaboration, and delivery evolve as GenAI tools are adopted. This makes it easy to understand what’s changing and what’s working across teams.

Milestone is designed for enterprise use, with strong privacy, access control, and governance foundations.

Yes. Milestone is built for multi-team, multi-repo environments and scales across complex organizations. It delivers consistent visibility even as systems and teams grow.

The most successful teams pair faster development with better understanding. Milestone provides the insight needed to learn, adapt, and evolve as software development changes.

Milestone is SOC 2 type 2 compliant, with audited controls covering security, availability, confidentiality, and governed software delivery processes. Our SOC 2 report is available under NDA.