What Makes a Company Successful at Using AI?

Companies in a wide range of industries are trying to integrate analytics and data to improve their operations, with decidedly mixed results. What are top performers doing differently — and better — than others? In general, leading companies did an honest assessment of where they were, formed a vision of where they wanted to be in three or four years, and looked for ways to rack up quick wins. More specifically, they outperformed in five areas: governance, deployment, partnerships, people, and data availability.

Vistra, a major U.S. power producer, had a problem. For its plants to operate efficiently, workers had to continuously monitor hundreds of different indicators, tracking temperatures, pressures, oxygen levels, and pump and fan speeds — and they had to make adjustments in real time. The process involved a huge amount of complexity, and it was too much for even the most skilled operator to get right all the time. To address this challenge, the plant installed an AI-powered tool — a heat-rate optimizer — that analyzed hundreds of inputs and generated recommendations every 30 minutes. Result: a 1% increase in efficiency. That may not sound like much, but it translates into millions in savings as well as lower greenhouse gas emissions.

Companies in a wide range of industries are trying to integrate analytics and data to improve their operations. Wayfair, the e-commerce company, was an early mover in shifting its data to the cloud and investing in machine learning. When Covid-19 hit, and rapid changes to consumer demand followed, it was able to optimize container ship logistics, continually adjusting what goods were sent to which ports. Result: an astonishing 7.5% reduction in inbound logistics costs.

Not all companies have been as successful as Wayfair, however. In fact, top performers can have more than twice the impact in half the time compared to the average company implementing machine intelligence. Why do some companies do so much better than others?

To answer that question, McKinsey and MIT’s Machine Intelligence for Manufacturing and Operations (MIMO) studied 100 businesses in sectors from automotive to mining. Through interviews, research, and a survey, we sought to get a sense of how they used digital, data analytics, and machine intelligence (MI) technologies; what they wanted to achieve; and how they kept track of their progress. By looking at 21 performance indicators across nine categories — strategy, opportunity focus, governance, deployment, partnerships, people, data execution, budget, and results — we were able to divide the 100 companies into four categories: leaders, planners, executors, and emerging organizations to identify the relationships between actions taken and investments made, and tangible and sustainable outcomes.

Any company with ambitions to gain from advanced digital technologies has the opportunity learn from best practice approaches, whether it is a planner, an executor, or an emerging company today. We take a look beyond the top-level numbers to explore the underlying drivers of success.

The race to leverage data and analytics could be won with multiple coordinated actions rather than any single bold move. All four segments — leaders, planners, executers, and emerging companies — are operating in a dynamic space where the bar is rising and the number of machine learning use cases will continue to increase and embed themselves into business-as-usual.

Not everyone should strive to be a leader immediately; they should instead strive to move to the next better state.

Leadersare the highest performers and comprise about 15% of the sample. By investing in the right places, they have captured the largest gains from advanced digital technologies. Leaders are much more likely to have a defined process for the assessment and implementation of digital innovation. They are also more likely to follow that process regularly and to update it continually.