KPMG Chief Puts Decision Redesign Before AI Speed
- Fewer than half of chief executives use AI and other technologies to make decisions faster and clearer.
- Walsh argues that boards and executives must redesign decision processes rather than merely add AI tools.
- Honest academic AI use requires declared assistance, inspectable evidence and human responsibility.
- Future evidence must show whether AI improves decision quality while preserving accountability.
Boards and executive teams should redesign how they make decisions with AI, rather than bolt new tools onto established routines. That is the governance change advocated by Timothy Walsh, KPMG’s US chair and chief executive, in Forbes’s account of his case for redesigning AI-era decision-making.
Walsh’s argument is directed at management committees, chief executives and boards. Drawing on KPMG’s 2026 Adaptability Index, he notes that fewer than half of chief executives use AI and other technologies to make decisions faster and clearer. The finding does not establish that AI improves judgement. It shows that many leaders have yet to incorporate technological information into their decision processes at all.
This is not a statutory rule, and the article identifies no regulator, deadline or formal sanction. Its practical force would therefore come through internal governance. Boards would decide when AI-derived material may inform a decision; executives would establish who remains accountable; committees would test whether the resulting judgement is clearer or merely quicker. Buying another tool is relatively straightforward. Redesigning authority is where the paperwork develops teeth.
The distinction matters because speed is an incomplete measure. An AI system can reduce the time needed to summarise evidence while still introducing errors, obscuring uncertainty or making responsibility harder to locate. A redesigned process should keep the human decision-maker, the evidence used and the system’s limits visible. Without that traceability, rapid adoption may create considerable activity without defensible outcomes.
Education and academic work face the same governance problem. An institution can add generative AI to teaching, assessment or administration without deciding what counts as acceptable assistance, which outputs must be checked, or who answers when a result is wrong. Honest, verifiable use requires declared assistance, inspectable evidence and human responsibility for consequential decisions.
The important test is whether a later reviewer can reconstruct how AI influenced a judgement. That applies to marking, admissions, research synthesis and institutional decisions alike. Faster processing becomes useful only once standards of evidence and accountability are settled.
Walsh expects adoption to accelerate as the technology develops and organisations gain experience. That remains an expectation, not a demonstrated outcome. The evidence to watch is whether organisations report measurable improvements in decision quality, define responsibility clearly and preserve records sufficient to challenge AI-influenced judgements. Otherwise, redesign may prove little more than deployment with better stationery.