Performance measurement has been a top management imperative ever since Frederick Winslow Taylor’s seminal work “Principles of Scientific Management” revolutionized business processes more than a century ago. Taylor’s stopwatch, ruthlessly deployed to monitor and maximize worker productivity, became a controversial symbol of performance analytics. More recently, the purpose of measuring performance has expanded well beyond efficiency and now includes the strategic optimization of a range of business functions and outcomes.

Thanks to radical improvements in artificial intelligence, the purpose and practice of measurement are expanding even further. Executives are working with machines to develop new perspectives on what drives performance and how best to measure it. Much as NASA’s James Webb Space Telescope has overturned astronomers’ understanding of the universe by observing it with unrivaled range and power, AI is overturning organizations’ understanding of performance.

Increasingly, organizations combine AI with performance data to generate and refine key performance indicators, both with and without human intervention. Our conversations with leading AI researchers and practitioners strongly suggest that tomorrow’s most effective leadership teams will use KPIs not simply to monitor enterprise success but to redefine and drive it.

Avinash Kaushik, chief strategy officer at digital marketing agency Croud, was formerly the senior director of global strategic analytics at Google, where, in Webb-like fashion, machine learning helped his team reimagine the possibilities of performance measurement. He explains that Google used AI to identify new high-performance parameters that greatly improved the technology giant’s substantial but underperforming marketing investments on one primary digital channel.

The thinking at the time, Kaushik recalls, was that “lots of people get really good results on a primary digital channel, but not us. And we’re spending lots of money. And we have lots of reports and segments and statistics of all kinds. But we have no idea what the hell is wrong with us. We know we’re failing; we just don’t know why, and we’ve exhausted all the questions we can ask.”

Google’s team’s wealth of talent, analytic resources, and data access wasn’t enough to crack the code. “So, after having analysts and statisticians have a whack at it, we decided, ‘You know what? We’re going to collect a very smart algorithm, and we’re going to feed it as much data as we have,’” Kaushik says. “And we’ll just say, ‘Tell us what’s wrong.’”

Kaushik’s team used supervised machine learning techniques — classification trees, specifically — to identify connections and correlations they had missed. “Because we didn’t even know what questions to ask, this kind of unsupervised machine learning algorithm was a really good approach,” he says. “We let the algorithm find the patterns.”

What the algorithm found surprised Kaushik and his team: The KPIs they had thought were most essential to optimize actually weren’t. “Which metrics were most influential, the order of their importance, and in which ranges we need to play for individual metrics was a revelation to us,” he says. Among these surprising metrics was the significance of available headroom for the brand metric, which was not on the team’s consideration list of top influencers.1 A second was the strong impact of audible and visible on complete (AVOC), a measure of the percentage of impressions in which a person viewed and heard a full ad. If the AVOC was below a certain percentage, the marketing campaign was doomed to fail. If the percentage was higher, the campaign had a chance for success.

“Six months after we implemented the algorithm’s recommendations, there was a 30-point improvement in performance. That is an insane performance improvement,” Kaushik says. “It’s because instead of the humans figuring out what questions we should ask of the data, we simply said, ‘Hey, why don’t you figure out what the trouble is?’”

Google’s successful use of AI to rethink performance cannot be explained away as the singular accomplishment of a company with a trillion-dollar market cap and cutting-edge technological capabilities. On the contrary, we’ve seen similar examples across the industry landscape in domains ranging from professional sports to health care to energy. More and more companies are harvesting new riches from pattern recognition and discerning performance drivers that are computationally invisible to legacy tools and analytics. Our interviews with corporate executives make clear that transforming how organizations measure can fundamentally transform what organizations measure. (See “What Is a KPI? Now and Then.”)

Businesses that use AI to generate new metrics or refine existing ones enjoy a range of benefits over those using the technology primarily to improve their performance on legacy metrics. Our research already indicates that companies that derive substantial financial benefits from their AI investments are 10 times more likely to change how they measure success compared with companies that realize smaller returns from their AI investments.2 We see organizations using algorithms to challenge and improve enterprise assumptions about the sources of performance, profitability, and growth. In short, businesses increasingly use AI to redefine, not just augment, performance.

The organizational, operational, and cultural significance of enlisting AI for performance measurement is difficult to overstate. Leaders can now use AI-powered KPIs not only to measure past performance but to serve as organizing principles for aligning the organization toward its strategic goals, improving how the company understands and defines success, and catalyzing growth.

This article identifies three practical and valuable but little-discussed business implications and benefits of using AI both to generate and refine KPIs.