Almost 90% of organisations say they're at least experimenting with AI, but just 7% report scaling it across the enterprise, according to McKinsey research. The finding underscores that AI's impact comes not from experimentation alone, but from integration into core operational processes - and that the operating model around the technology matters as much as the tech itself.
McKinsey's "Putting AI to work" report, which focuses on AI for Operations, surveyed 1,000 senior and midlevel executives across 696 manufacturing and service-sector businesses. Most responses came from large organisations, with only 20% from companies with less than US$1bn in revenue.
The research found a clear performance advantage for companies that move beyond isolated AI use. Those with AI embedded across multiple functions generate nearly double the profit margins of peers using AI in only a few departments. The difference in capital returns was even starker: three-year return on invested capital was more than five times higher for the multi-function group.
Rahul Shahani, a McKinsey Partner and co-author of the report who leads the firm's Manufacturing and Supply Chain Practice in North America, said: "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."
The limits of AI in isolation
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. But even among these groups, the companies with the best results combined technology with strong operational practices.
Leading companies increasingly move beyond experiments to embed AI in core workflows and link use cases directly to operational and financial outcomes. Those that limit AI to a small set of use cases see far more modest results. The report noted that companies with advanced technology built into their operational excellence achieve higher productivity increases than those relying mainly on manual or analogue systems.
Inside Siemens' Nanjing facility
One of the most relevant examples in the report is Siemens' site in Nanjing, China, a World Economic Forum Global Lighthouse Factory. The facility faced constant pressure from high product variability and small batch sizes, which strained throughput and delivery reliability.
Rather than scaling digital twins prematurely, the leadership team first tightened the operating backbone. It integrated a manufacturing operations management system that governed data flows between virtual models and physical assets. Teams then validated simulations through structured routines before implementing changes.
At the Nanjing site, leaders defined clear decision rights for when human confirmation was required. They treated IT/OT integration and data standards as core operational disciplines, not as side projects. This discipline, combined with the digital twin technology, drove the throughput gains.
Shahani said: "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."
Why this matters for operations professionals
For operations leaders, the report's message is direct: AI tools deliver their biggest returns when the surrounding management system is already strong. Deploying AI into a broken process won't fix it; it will only automate the dysfunction.
Shahani's point about sub-optimal operations echoes a wider truth in manufacturing: technology investments without operational discipline rarely pay off. The Siemens example shows that even advanced tools like digital twins need a foundation of clear workflows, data governance and decision rights.
Operations professionals looking to build these capabilities can explore resources such as an AI Learning Path for Operations Managers. The path focuses on integrating AI into operational workflows, strengthening the very management systems the McKinsey report highlights as critical.
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