Companies are making big investments in machine intelligence, including automation and machine learning to boost efficiency. 

For example, during Tyson Foods, Inc.’s Q1 2022 earnings call last month, CEO and President Donnie King doubled down on the company’s plan to spend $1.3 billion over the next three years to increase automation in meat plants.

But implementing machine intelligence successfully can be complex, according to new research. To learn more, I had a conversation with Vijay D’Silva, a senior partner at McKinsey & Company. He is co-author of the firm’s report, in collaboration with the MIT Machine Intelligence for Manufacturing and Operations program, about what it takes to succeed. The findings are based on an in-depth survey of 100 companies in manufacturing and industrial operations. 

Companies defined as “leaders” have captured the largest gains from their machine intelligence efforts. The 12 highest-performing companies in the survey achieved significantly larger improvements than the rest in 20 of the 21 KPIs evaluated, such as efficiency.

An example of a leader? The e-commerce company Wayfair, according to the researchers. The company was early to move its data to the cloud and use machine learning in several functions. During the pandemic, Wayfair has been able to optimize container ship logistics, continually adjusting what goods were sent to which ports. “With shipping surcharges becoming more prevalent, Wayfair saved about 7.5%  in inbound logistics costs as a result,” according to the report.

Leaders have strong data management, governance, partnerships, and deployment as well as employee-training in machine intelligence. Here’s how they did it.

Data: Machine intelligence is fueled by data. About 92% of leaders had a process in place to track incomplete and inaccurate data. “Taking an inventory of which data is really important is core to implementing an efficient process for tracking data,” D’Silva told me. “Otherwise, a data strategy can become overwhelming, if organizations are trying to manage too much, or ‘boiling the data lake.’ Companies should always come back to the key question: what business problems am I trying to solve?” Maintaining data quality on an ongoing basis, and the democratization of data is also important, he notes.

Partnering: More than 80% of leaders have partnerships with start-ups and academic groups, for example. Meanwhile, less than a third of other companies have these partnerships. A chief technology officer at a manufacturing company (not named) told the researchers partnerships help because “we don’t have consistently well-structured data as you might get from standard enterprise-resource-planning systems,” according to the report.

Governance: More than 90% of leaders had a dedicated center of excellence. Leaders were more likely to have a defined process for the assessment and implementation of digital innovation, and update it continually.

Upskilling employees in machine intelligence: Leaders are five times more likely than the rest to train frontline employees on digitization and the Internet of Things (IoT), the researchers found. An example: McDonald’s used machine intelligence to improve tasks including predicting customer response to promotions. Team members in the field learned about the technology through “easy-to-use tools” created by its corporate center of excellence, according to the report.

Deployment: The researchers measured deployment by factors such as expenditure, how long companies have been working with the technologies,  and the maturity level of their analytical methods. Leaders continually invest more in their deployments, “with budgets 30 to 60% larger (as a percent of total) across manufacturing, customer service, product design, and supply chain,” the report stated.

Since many factors determine the success of deployment like the budget size and technology history, I asked D’Silva: Is C-suite collaboration a vital part of the process? 

“Yes, many of the [companies defined as] leaders involved the entire C-suite,” he says. “For example, we interviewed the CTO of one company who said the big business problems machine intelligence could solve for their organization came from the C-suite, which ensured that the investment was focused on a challenge core to the overall business, and not viewed as a side project.”

See you tomorrow.

Sheryl Estrada
sheryl.estrada@fortune.com

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