<ul><li>AI delivers value when its impact is measured effectively, not just because it works.</li><li>Clear definition of success is crucial in AI projects to recognize and achieve it.</li><li>Up to 70% of AI initiatives underperform due to poor measurement practices, not faulty models.</li><li>Four key areas where measurement matters in AI implementation are highlighted.</li><li>Measurable success includes metrics like cost savings, efficiency gains, faster response times, and innovation speed.</li><li>Real examples show how AI systems can lead to tangible benefits like reduced downtime, cost savings, and improved client satisfaction.</li><li>Key metrics to track include efficiency gains, revenue growth, speed of innovation, and decision quality.</li><li>Consistent and relevant measurement is essential in proving the value of AI for business growth.</li><li>The importance of aligning AI with strategic business objectives and continuously tracking relevant metrics is emphasized.</li><li>Presentation of AI results should focus on key metrics that showcase value to senior leadership.</li></ul>

Measuring AI Success: The Metrics That Actually Matter

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