McKinsey says AI compresses cycles

McKinsey used CES 2026 to argue that AI is changing product development speed as much as product economics. In a client-facing demonstration reported by IBTimes on May 18, the consulting firm said work that once took six to nine months can now be compressed into roughly two weeks when companies combine AI tools, digital testing and simulated customer feedback. That claim fits with McKinsey’s own recent research, which says the value from AI comes less from adding tools to existing work and more from redesigning workflows around them. In an April 29 article, McKinsey wrote that faster experimentation lowers the cost of iteration and can let organizations improve more quickly as they generate more data and feedback. (ibtimes.com) ### Where are the two weeks actually coming from? McKinsey’s description points to compression across several steps rather than a single coding shortcut. The IBTimes report said the firm showed how AI, digital testing and simulated customers can collapse the loop between idea, prototype, feedback and revision. (mckinsey.com) McKinsey has made a similar argument in its product-development research. In a 2024 article on AI-enabled software development, the firm said AI can support the end-to-end product development life cycle, giving product managers and engineers more time for higher-value work and allowing teams to use broader sources of customer and operational data. (ibtimes.com) ### Why are many companies still not ready for that pace? McKinsey’s own survey work says adoption is widening while scaled impact remains uneven. The firm’s 2025 State of AI survey said many organizations are still moving from pilots to broader deployment, and its earlier survey research found workflow redesign had the biggest effect among 25 tested attributes on whether companies saw EBIT impact from generative AI. (mckinsey.com) Harvard Business Review made a similar point in April. HBR reported that AI initiatives often stall because executives and middle managers see different operational realities, creating gaps between ambition at the top and execution inside the business. ### What breaks first when cycle times collapse? Planning cadence is one of the first pressure points. (mckinsey.com) If product teams can test, revise and ship decisions in days instead of quarters, annual planning, monthly steering meetings and slow approval chains become bottlenecks; that is an inference drawn from McKinsey’s research on experimentation speed and workflow redesign. (hbr.org) Governance is another. McKinsey’s survey research said CEO oversight of AI governance — the policies, processes and technology used to develop and deploy AI systems responsibly — is one of the factors most correlated with higher self-reported bottom-line impact. (mckinsey.com) ### Why does that matter for consulting demand? Consulting demand shifts when clients need help converting strategy into operating rhythm. Consultancy.uk argued on May 19 that AI consulting spending is rising but many traditional advisory models are poorly suited to fast-moving AI delivery, pushing firms toward work that operationalizes change rather than just diagnosing it. (mckinsey.com) McKinsey’s own recent writing also supports that framing. Its April 29 article said sustained advantage is more likely to come from reshaping offerings, business models and market structures than from using AI only to accelerate existing work. ### What should readers watch next? (ibtimes.com) McKinsey’s next public signals are likely to come through its Tech & AI insights page and follow-on research tied to workflow redesign, governance and productivity. The firm’s published materials in April and May 2026 have focused on those themes rather than on stand-alone tool adoption. (mckinsey.com 1) (mckinsey.com 2)