Performance measurement has been critical to businesses since the pioneering work of Frederick Winslow Taylor over a century ago. But now, thanks to the remarkable advancements in artificial intelligence (AI), performance measurement is taking on a whole new dimension. Executives are embracing AI to gain fresh perspectives on performance and redefine how it is measured. Just as the James Webb Space Telescope revolutionized our understanding of the universe, AI is reshaping organizations’ understanding of performance.
Combining AI and Performance Data
By combining AI with performance data, organizations are generating and refining key performance indicators (KPIs) in ways never before possible. AI is being used to identify new high-performance parameters and uncover correlations that were previously invisible to traditional analytics. It’s a game-changer that allows companies to redefine and drive their own success.
Take Google, for example. Avinash Kaushik, former senior director of global strategic analytics at Google, recounts how AI helped his team uncover the missing pieces of their underperforming marketing efforts. Using supervised machine learning techniques, they let the algorithm find the patterns they had been unable to identify. What they discovered defied their expectations, highlighting new metrics that greatly influenced their marketing success.
Smart KPIs that Learn
AI-powered KPIs are not just about tracking progress; they are about driving action. Instead of simply monitoring churn as a lagging indicator of customer satisfaction, companies are now using AI to develop innovative strategies to prevent churn. These algorithms can precisely determine the effort needed to retain at-risk customers and offer tailored retention incentives. The focus shifts from predicting churn to predicting the most effective tactics to retain customers.
Additionally, AI-driven KPIs enable companies to learn from the data and continuously refine the metrics they use. Legacy KPIs are retrospective, while smart KPIs are forward-looking and adaptive. Companies can now identify the most valuable metrics and adapt their strategies accordingly.
Governing Measurement: KPIs for KPIs
To ensure continuous improvement, organizations must evaluate and optimize their KPIs. With AI, the evaluation and governance of KPIs become increasingly managed by machines and humans working together. This ongoing assessment allows companies to refine their KPI portfolios, ensuring strategic priorities and driving better performance.
Companies like Schneider Electric are investing in improving their KPIs and actively seeking ways to measure their impact on organizational outcomes. By creating measurement systems specifically targeted at enhancing KPIs, they gain a deeper understanding of cross-functional value creation. AI algorithms analyze the relationships among multiple KPIs to balance interdependencies and identify areas for optimization.
Improved Alignment via Shared Smart KPIs
No KPI exists in isolation, as organizational units, processes, and functions often overlap. AI not only uncovers these overlaps but also helps resolve conflicts. By sharing AI-generated KPIs, companies can achieve improved organizational alignment and collaboration. CFOs and CMOs, for instance, can collaborate on a shared “patient readmission rate” KPI, aligning their efforts to both improve patient outcomes and reduce costs.
With shared KPIs, organizations gain a holistic view of performance and foster cross-functional collaboration. AI facilitates data sharing and visibility, empowering leaders to optimize performance across business-critical metrics.
AI is ushering in a new era of performance measurement and optimization. It enables organizations to redefine success by generating and refining KPIs in ways never before possible. With AI-powered smart KPIs, companies can drive action, govern measurement effectively, and achieve improved alignment across their organizations. The future of performance lies in embracing AI and continuously evolving our understanding of success.
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