AI Must Improve Quality, Not Compromise It
As teams adopt AI for writing, coding, support, research, and analysis, leaders need confidence that outputs meet the same standards as human work. Without visibility into quality, AI can introduce errors, inconsistencies, and downstream risk, especially in customer-facing and production workflows.Reliable quality measurement is essential to maintain trust and scale AI responsibly.
AI Quality Signals Are Difficult to Measure Consistently
AI tools generate outputs quickly, but quality is rarely measured in a consistent way. Reviews happen informally, standards vary by team, and errors often surface only after delivery. As a result, leaders lack a shared view of how reliable AI-assisted work actually is across the organization.This makes it difficult to compare performance, enforce standards, or identify emerging quality risks.
Unmeasured Quality Creates Risk and Rework
When AI output quality is not tracked, issues propagate through workflows and increase manual rework. Teams lose confidence in AI-assisted outputs, and adoption slows. Over time, inconsistent quality undermines trust and limits the ability to expand AI use in a controlled and predictable way.Without dependable quality KPIs, organizations react to problems instead of preventing them.
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What You’ll Gain With TargetBoard
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Trusted Quality Visibility
See how AI affects accuracy and consistency across teams using clear, reliable quality metrics.
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Reduced Rework and Risk
Identify quality issues early to prevent downstream corrections, delays, and execution risk.
Confident AI Scaling
Apply consistent quality standards so AI can be expanded without compromising trust or outcomes.
How TargetBoard Brings Clarity to AI Productivity Measurement
TargetBoard connects AI usage data with workflow and execution metrics to give leaders a reliable view of how AI affects quality, consistency, and reliability across teams.
Connects Your Systems
Connects AI tools, workflows, and operational systems to capture quality signals.
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Tracks Quality, Rework Trends
Measures accuracy, consistency, review cycles, and rework across AI-assisted workflows.
Highlights Reliability Risk
Surfaces where AI-assisted output quality varies, degrades, or introduces execution risk.
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Explore AI Adoption KPIs
Discover how TargetBoard helps leaders connect AI usage, productivity, quality, and cost metrics into one reliable view, improving visibility, decision-making, 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 Output Quality and Reliability
Accuracy of validated AI outputs increased from 73% to 81%, a 11% improvement.
Oct 11 - Nov 9, 2025 vs.
Sep 11 - Oct 10, 2025
This improvement indicates higher reliability of generated insights and stronger alignment between AI suggestions and human review standards.
Optimize AI Investment Impact
TargetBoard helps organizations measure the quality and reliability of AI-assisted work by connecting usage, workflow activity, and execution data into one consistent framework. It provides clear visibility into accuracy, consistency, rework, and reliability trends, enabling teams to reduce risk, maintain standards, and scale AI adoption with confidence.
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From messy data to clear KPIs, TargetBoard delivers insights and turns them into actions.
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