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.
If You Can’t Measure It, AI Becomes Overhead
Without data, it's impossible to prove the impact of AI adoption, making it hard to justify continued investment. Teams drift into inconsistent adoption patterns, tool costs grow faster than results, and what started as efficiency becomes technical noise.
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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, credible business case.
Set up in days — not months — with out-of-the-box AI performance KPIs ready to review.
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See AI’s Impact in Action
Discover how TargetBoard helps engineering leaders measure, optimize, and communicate the real business value of AI — all from one platform.
✨ 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
% Closed Dev-Items (Last 30d)
Team FrEM's % Closed Dev-Items increased from 8% to 26%, a 225% improvement.
Oct 11- Nov 9, 2025 vs.
Oct 11- Nov 9, 2025 vs.
Significant improvement indicates enhanced team efficiency and productivity.
The Impact of AI
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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Related Use-cases
Developer Productivity Insights
Project Delivery Forecasting
Resource Allocation & Investment
R&D Cost Capitalization
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