AI Creates New Bottlenecks as Progress Outpaces Business Adaptation

According to a McKinsey report, AI capabilities are advancing much faster than the infrastructure and organizational changes needed to deploy them. This gap is creating new bottlenecks for businesses, limiting productivity gains despite high AI adoption.

Artificial intelligence capabilities are advancing faster than the infrastructure and organisational changes needed to deploy them, creating new bottlenecks for businesses, according to a report by the McKinsey Global Institute.

The report, released this month, said the complexity of tasks that AI can reliably perform has been doubling roughly every four months since 2023. However, infrastructure such as data centres, power capacity and chip manufacturing is expanding at a much slower pace.

The Organisational Gap: Adoption vs. Integration

“The tools are here. But the reorganization is not,” McKinsey said, pointing to the gap between the rapid development of AI and the slower changes taking place within businesses.

According to the report, 89 per cent of organisations surveyed in 2026 were using AI in at least one business function. However, only 46 per cent had moved beyond the pilot stage.

The report also found that outside a small group of high-performing companies, only around one-quarter of respondents had redesigned their workflows around AI instead of simply adding AI tools to existing processes.

McKinsey said this gap could determine how quickly AI delivers productivity gains and contributes to wider economic growth. While AI is already helping businesses complete individual tasks faster, broader productivity gains will require companies to redesign workflows and implement these changes across their operations, the report said.

Shifting Constraints and Infrastructure Investment

The report noted that access to advanced chips and computing capacity initially emerged as key constraints on AI expansion. Later, electricity supply and grid connections became major challenges. Going forward, McKinsey expects applications, workforce skills and organisational workflows to become increasingly important constraints on the wider adoption of AI.

At the same time, investment in AI infrastructure is continuing to rise rapidly. McKinsey estimates that global investment in data centres could reach USD 7 trillion cumulatively between 2025 and 2030, with a significant portion driven by AI demand. However, the report cautioned that infrastructure investment could also grow faster than actual demand, creating the risk of excess capacity.

A Call for Business Agility and Adaptation

For businesses, McKinsey said AI capabilities could advance faster than companies can adapt their technology, workflows and organisational structures. It urged business leaders to build the ability to adapt quickly and use AI not only to automate existing tasks but also to develop new products, businesses and sources of growth.

The report concluded that the future impact of AI will depend not only on technological progress but also on how infrastructure, investment, regulation, organisations, markets and workers adapt to rapidly advancing AI capabilities.

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