INDUSTRY NEWS
BUILDING BETTER COMPANIES
Adapting Before You Have To What McKinsey’s cuts should tell every commercial construction leader
By Jane Gentry
T
he news came out quietly, the way these things usually do. McKinsey is cutting 3,000 to 4,000 positions in 2026—roughly 10% of its global workforce, the largest reduction since 2008. Bain, BCG and Deloitte are doing variations of the same thing. Slower hiring. Headcount reductions. A pulling back that the firms themselves describe, carefully, as a response to AI productivity gains. Read that again. The world’s most prestigious advisory firms are cutting people because AI is doing the work those people used to do. That is not a technology story. That is an operating model story. And if you lead a commercial construction firm, it is your story too — whether you see it yet or not.
The Work That Was Cut
McKinsey didn’t eliminate partners. They eliminated the analytical layer underneath them—the associates and analysts who spent weeks synthesizing research, building models, producing deliverables that clients paid for at premium rates. That work turned out to be compressible. Not because the people weren’t capable. Because the work itself—pattern recognition, document synthesis, comparative analysis, structured reporting—is exactly what artificial intelligence (AI) does well and fast. Now look at your own organization. Who tracks RFIs and summarizes status for the project executive? Who pulls together the weekly schedule report? Who runs the first pass on subcontractor bids? Who writes the owner update? Who coordinates the submittal log across three trades? That work is not the same as McKinsey analyst work. But the underlying structure is identical. Information processing. Pattern recognition. Synthesis and reporting. Work that requires training and intelligence— and that AI is now compressing on a timeline most construction leaders haven’t fully absorbed. This is not a prediction. It is already happening. Firms using AI-assisted estimating
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are running comparables in hours that used to take days. Document review that required a coordinator’s full afternoon now takes minutes. RFI response drafts that sat in someone’s queue are being generated in real time. The compression is real. The question is whether your operating model is designed for what comes after—or still built for what worked before.
What the Compression Exposes
Here is where most firms get stuck. They see the compression coming, buy a platform, run a pilot, and call it an AI strategy. The tools do what they were built to do. And then not much changes—because the tools were layered on top of an operating model that was never redesigned to use them. The technology is not the problem. The sequence is. An AI strategy matters. You need one. But an AI strategy built on top of a fragmented operating model does not create leverage. It accelerates the existing dysfunction. Faster reporting on a project
COMMERCIAL CONSTRUCTION & RENOVATION — ISSUE 7, 2026
with unclear decision rights is still a project with unclear decision rights. Better data flowing to people without authority to act on it is still a bottleneck—just a better-informed one. McKinsey learned this at scale. They built a business model on billable analytical hours, and then the thing that made those hours billable got automated. What survived was senior judgment—the partners who could walk into a boardroom and tell a CEO something they didn’t already know. The analytical scaffolding underneath that judgment turned out to be the exposure, not the asset. Your firm has the same anatomy. The question is whether you know where your exposure sits.
AI Needs People. Specifically, It Needs Judgment.
There is a version of this conversation that treats AI as a headcount reduction strategy. That framing will cost you. AI does not manage owner relationships. It does not make the call on a subcontractor