AI Is Easy to Adopt, Hard to Measure
Engineering leaders are under pressure to prove the value of AI initiatives. But without clear metrics, it’s impossible to compare teams, track impact, or justify continued investment. Every team defines success differently — which means nobody really knows what’s paying off.
If You Can’t Measure It, You Can’t Scale It
When teams rely on guesses or anecdotes, they either over-invest in hype or under-invest in what’s actually working. The result: wasted time, missed opportunities, and unclear ROI on one of your biggest technology bets.
Unmeasured AI Becomes Technical Debt
When AI impact isn’t tracked, velocity looks good while code quality and reliability quietly erode. Teams can’t prove what’s actually working, and every new model or workflow adds invisible complexity. The result: AI that scales noise, not performance.
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What You’ll Gain With TargetBoard
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Evidence-Based ROI
See how AI affects productivity, cost, and quality — with real data, not anecdotes.
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Investment Confidence
Know which tools and teams deliver measurable value, and which don’t.
Organization-Wide Clarity
Standardize AI metrics so Engineering, Product, and Finance speak the same language.
How TargetBoard Makes AI Performance Measurable
TargetBoard integrates with AI-enabled developer tools such as GitHub Copilot, Claude Code, Amazon Q, Cursor, and others to automatically measure how AI contributes to delivery efficiency and ROI.
Detects AI-Assisted Work
Identifies AI-generated commits, pull requests, and automation patterns.
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Correlates With Outcomes
Compares velocity, quality, and rework trends before and after AI adoption.
Calculates ROI
Links AI usage to cost and delivery metrics for a clear business case.
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Explore Engineering KPIs
Discover how TargetBoard helps engineering leaders connect delivery, quality, and operational metrics into one reliable view — improving predictability, alignment, and execution across teams.
✨ Top New Insights
% Closed Dev-Items (Last 30d)
Team FrEM's % Closed Dev-Items increased from 12% to 6%, a 50% improvement.
Oct 11- Nov 9, 2025 vs.
Oct 11- Nov 9, 2025 vs.
Significant improvement indicates enhanced team efficiency and productivity.
✨ Top New Insights
AI Adoption in Engineering
Your team’s AI utilization rate increased from 16% to 23%, a 44% improvement.
Oct 11- Nov 9, 2025 vs.
Sep 11- Oct 10, 2025
This improvement indicates broader uptake of AI tools across development workflows.
Measure the Impact of AI Adoption
TargetBoard helps engineering leaders measure AI adoption and its impact on productivity, delivery speed, and cost efficiency. By connecting data from Jira, GitHub, CI/CD, and AI-enabled tools, it quantifies AI’s contribution to real performance gains and ROI.
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From messy data to clear KPIs, TargetBoard delivers insights and turns them into actions.
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