Your AI Is Ready; Your Operating Model Isn't

KPMG Partner | Global Data & AI Leader | Enterprise AI, indirect tax compliance, and finance transformation across global organizations.

Over the past two years, I have sat in enough boardrooms across enough industries to notice a pattern. Executives ask me some version of the same question: How much should we trust AI? The framing changes—regulatory risk, operational control, what happens when the model is wrong—but the anxiety is the same.

That question, while understandable, is the wrong one. The more consequential question is this: Are we designing our AI programs to multiply human judgment, or are we just building faster queues?

That is the difference between “human in the loop” and “leverage by design.” Getting it wrong is costing organizations real money, real time and real competitive ground.

The Problem Nobody Talks About

Human in the loop started as a governance idea. Let AI do the heavy lifting, but keep a human in the chain before any final decision is made. Responsible in principle. Sensible on a whiteboard.

In practice, I have watched it become a bottleneck factory.

I have seen organizations deploy machine learning models that classify invoices with 98% accuracy—and then route every single invoice through a human approval queue anyway. The technology is faster. The process is not. The organization automated the creation of work without automating its completion.

This is digital congestion. You have moved the problem, not solved it.

The same pattern shows up in customer service, tax compliance, contract review and supply chain operations. The AI works, but the operating model does not. The people caught in the middle, those reviewing thousands of outputs that were almost certainly correct, are not doing their best work. They are doing the work the machine already did, one more time, more slowly.

That is not a technology failure. It is a design failure.