McKinsey: The Operations Around AI Matter As Much As Tech

A report from McKinsey shows that almost 90% of organisations say they’re at least experimenting with AI, but just 7% report scaling it across the enterprise.

McKinsey says the pattern suggests that AI’s impact comes not from experimentation alone, but from integration into core operational processes. It highlighted global sites such as a Siemens facility in China, a World Economic Forum Global Lighthouse Factory.

A McKinsey Partner and one of the authors of the report, Rahul Shahani, who leads McKinsey’s Manufacturing and Supply Chain Practice in North America, told Manufacturing Digital: "One of the clearest findings from our research is that manufacturers don't realise the full value of AI simply by deploying the technology into operations that are sub-optimal.”

AI in isolation

McKinsey’s report showed a performance advantage for companies that moved beyond isolated AI use and scaled across enterprise. 

It found that even with widespread disruption, reported by almost all of its respondents, companies with AI embedded across multiple functions generate nearly double the profit margins of peers using AI in only a few departments.

Among the companies it surveyed that embedded AI across multiple functions, the difference was even more pronounced in capital returns, with three-year return on invested capital being more than five times higher.

McKinsey found that parts of advanced manufacturing are more consistent in deploying AI across functions, reflecting years of investment in data, analytics and execution discipline.

For this report, McKinsey surveyed 1,000 senior and midlevel executives across 696 manufacturing and service-sector businesses. Most responses were from large organisations, with only 20% from companies with less than US$1bn in revenue.

Key findings on AI in manufacturing

The report highlighted that companies that embed AI across more functions report steadily higher productivity gains, while those limiting AI to a small set of use cases see far more modest results. 

Leading companies increasingly move beyond experiments to embed AI in core workflows and link use cases directly to operational and financial outcomes.

Companies that are advanced in applying AI see significantly higher productivity and profitability than their peers, but that operational excellence, including robust management and technical systems, a strong corporate purpose and well-defined operating principles and behaviours, mattered too. 

It said companies with the best results were combining the two. Companies that have built advanced technology into their operational excellence achieve higher productivity increases than companies relying mainly on manual or analogue systems.

Lessons from Siemens’ Nanjing Facility

Throughout its report, McKinsey analysed key examples of how manufacturers were embedding AI. One of the most relevant examples to its argument about operational excellence and AI integration was Siemens’ site in Nanjing, China. 

Rahul told Manufacturing Digital: “The biggest gains come when companies pair these tools with strong management systems, clear operating principles and disciplined execution. Siemens' Nanjing facility, part of the World Economic Forum’s Global Lighthouse Network, is a good example.

“By combining digital twin capabilities with broader operational improvements, the company was able to significantly increase throughput. For manufacturers, the lesson is that technology matters, but the operating model around it matters just as much.”

Siemens' AI integration

At Siemens’ Nanjing site, high product variability and small batch sizes were putting constant pressure on throughput and delivery reliability. While the leadership team explored the use of digital twins, it resisted scaling them prematurely, McKinsey's report says. 

The site responded by tightening its operating backbone before expanding the technology. It integrated a manufacturing operations management system that governed data flows between virtual models and physical assets. 

Teams at Siemens’ site then validated simulations through structured routines before implementing changes. Clear decision rights were defined when human confirmation was required and leaders treated IT/OT integration as well as data standards as core operational disciplines.