How A.I. can transform and improve your company’s performance

How A.I. can transform and improve your company's performance

AI Revolutionizing Performance Measurement: Creating New KPIs, Prioritizing Metrics, and Aligning Objectives

The advent of AI technology has brought about a revolution in the field of performance measurement, challenging traditional assumptions about what drives performance, profitability, and growth. Companies are turning to AI not just to keep track of legacy metrics, but to transform their performance and sustain that transformation. According to the BCG-MIT 2023 Global Executive AI Survey, 70% of respondents agree that better key performance indicators (KPIs) are critical for success.

AI leaders are utilizing data and technology, such as supervised and unsupervised machine learning or deep learning, to measure and manage KPIs in three key ways: creating new KPIs with AI, prioritizing the KPIs that matter using AI, and improving alignment across the organization with AI-designed shared KPIs.

Creating new KPIs with AI

One standout success story comes from Google, a tech giant that struggled to boost the performance of a primary digital channel for years. Despite having a vast amount of data, they were unable to identify the key parameters necessary to improve client campaign performance. Frustrated with the lack of progress, Google turned to AI. By developing an algorithm and leveraging unsupervised machine learning techniques, Google allowed the AI to analyze the data and identify connections, correlations, and causations that had been missed by human engineers.

The AI not only identified new performance indicators but also ranked their importance, revealing that some previously thought crucial metrics were, in fact, unimportant. Google implemented the AI’s recommendations, focusing on previously overlooked metrics, and saw a remarkable 30-point improvement in campaign performance within six months. This ability of AI to identify novel performance indicators is not limited to tech giants; even smaller companies can benefit. For example, a South American retailer used AI to develop new KPIs that optimized logistics, resulting in a 14% decrease in logistics costs within 90 days.

Prioritizing KPIs with AI

DBS Bank, headquartered in Singapore, faced challenges in its early attempts to use AI to track conventional metrics. However, the bank made a breakthrough when it adopted the concept of consumer “journeys” and created an AI-driven control tower to track these journeys. This allowed DBS to identify the factors that were crucial for desired outcomes, such as customer experience, profitability, employee experience, and risk level. By prioritizing these factors and making AI-driven insights visible to cross-functional teams, DBS witnessed a significant increase in profits before tax, from around $5 billion in 2021 to over $6 billion in 2022.

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As industries become more complex and companies grow larger, the sheer number of KPIs can become overwhelming. Using AI to develop a streamlined set of KPIs, tailored to each employee’s performance goals, helps channel efforts towards organizational objectives that truly matter. Schneider Electric, for instance, has created a performance management office to improve its performance and the metrics it uses.

Aligning with shared KPIs

AI is well-suited to uncover overlaps among KPIs and resolve resulting trade-offs and inconsistencies. This has the potential to significantly improve organizational alignment. For example, in the healthcare sector, reducing admissions is crucial for cost reduction and a key outcome indicator. However, CFOs and chief medical officers (CMOs) often have different priorities. AI can now analyze patient data, identify root causes, and suggest targeted interventions, enabling CFOs and CMOs to share a “patient readmission rate” KPI. This shared KPI promotes alignment across the organization by simultaneously improving outcomes and reducing costs.

With companies increasingly operating within business ecosystems, managing a shared set of KPIs becomes essential for alignment. While some companies may prioritize a single North Star KPI, most find it impractical due to conflicting priorities and different data sets. By developing shared KPIs using AI, executives can deepen their understanding of how the organization creates value and rally teams behind common objectives.

Companies that embrace AI for performance management have the opportunity to optimize existing KPIs and design new ones. This balance allows CEOs to shift their focus from managing by looking back to managing by looking ahead. The incorporation of AI into performance measurement opens up new frontiers for business performance, marking a new era defined by transformative and data-driven decision-making.

Read other ANBLE columns by François Candelon

François Candelon is a managing director and senior partner at BCG, and the global director of the BCG Henderson Institute. You may contact him at [email protected]. Shervin Khodabandeh is a managing director and senior partner at BCG. Michael Chu is a partner and associate director, data science, at BCG X. Gaurav Jha is a consultant at BCG and an ambassador at the BCG Henderson Institute. Some of the companies featured in this column are past or current clients of BCG.