AI Is Helping Companies Redefine, Not Just Improve, Performance

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.