AI adoption is widespread, but enterprise-scale deployment remains uncommon. A global survey found most organizations use AI in at least one function, while only about one-third report scaling AI across the enterprise. Proof-of-concepts often reach production, yet comprehensive AI governance is still limited. Scaling starts with business outcomes rather than model development, emphasizing productivity, cost savings, and enabling top-line growth. Many advanced generative AI initiatives meet or exceed ROI expectations when linked to measurable value. Repeatable scaling also requires foundation work in data, governance, and process, moving beyond treating these needs as back-office cleanup.
"McKinsey's 2025 global survey found that 88% of organizations use AI in at least one business function, yet most are still experimenting or piloting, and only about one-third say they have begun scaling AI across the enterprise."
"The first lesson in how to scale AI is simple: start with business outcomes, not the model. In the podcast, Yao argues that many of the best enterprise AI wins are not flashy. They begin with productivity gains, move into cost savings, and then create room for top-line growth."
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