The Operational Excellence Tools Series | #60: From "Do You Use AI?" to "How Well?": Operational Excellence in 2026.

Welcome to the unique weekend article for the Loyal Fan subscribers-only edition.

This is the #60 article of The Operational Excellence Tools Series.

Outlines and Key Takeaways

Part 1 – Official Announcement

Part 2 – Background and Meaning

Part 3 – Analysis Through the Lens of Operational Excellence

Part 4 – Lessons for Businesses

Part 5 – Conclusion

PART 1: OFFICIAL INFORMATION

On July 2, 2026, MIT Technology Review published a piece with a blunt title: “Achieving operational excellence with AI.” The article does not preach that AI will change the world, something almost everyone has heard to the point of nausea. It poses a sharper question that few dare to face head-on: when every business already has AI in hand, what actually creates the difference. The answer that a whole series of operations analyses in 2026 converge upon is this: it is not whether you use AI, but how effectively you embed it into operational decisions, under real-time pressure.

This is an important shift in focus. A few years ago, the leader’s question was still “have we adopted AI,” and simply answering “yes” was enough to score points. By 2026, that question is obsolete, because nearly every organization has some form of AI: a chatbot, a forecasting model, an analytics tool. When AI becomes a commodity, owning it is no longer an advantage. The advantage shifts to something far harder: embedding AI at the right decision points of the machine, where every minute of delay or every wrong judgment converts into money, into waste, into a customer walking away.

The body of data around this theme is rich and worth pondering. A 2026 operations survey shows that operations leaders rank efficiency as their top priority, yet at the same time list it as their second biggest challenge. Meaning everyone knows efficiency is needed, but few find it easy to achieve. More striking still, nearly all of them, about 99% of respondents, admit that each week they still spend time on repetitive, low-value tasks. This is precisely the ground AI is expected to till: freeing people from the trivial so they can concentrate on judgment and decisions.

Where AI is embedded in the right place, the results are anything but abstract. In the restaurant and hospitality industry, units that deployed AI labor scheduling and demand forecasting report concrete figures: productivity up more than 10%, planning time cut by up to 80%, forecast accuracy approaching 90% under optimal conditions. What the successful cases share is that AI is not bolted on as a peripheral add-on, but brought into the operational core, unifying demand signals, staffing plans and execution into a single system. In aviation, the 2026 trend is to use large language models and AI agents as integrators of decision-making amid a highly volatile operational environment, where the differentiator is not whether AI is used, but how skillfully it is embedded into decisions under pressure.

But at the same time, the gap between the promise and reality remains vast, and this is what makes the story worth dissecting. Even within manufacturing, there is a startling figure: about 80% of factories in the U.S. still run with virtually no automation at all. If even basic automation has not reached four-fifths of factories, then the ambition of embedding AI into decision-making is clearly a long road ahead. Business leaders are pulled between two forces: on one side, demographic pressure, as the workforce in many economies is shrinking rather than growing, pushing them toward automation and AI as a way out; on the other, the reality that most organizations lack the data foundation, processes and culture for AI to truly deliver.

What lifts this news beyond a mere technology-trend piece is that it touches the essence of operational excellence. Operations, in the end, is an endless chain of repeated decisions: how much to produce, who goes on which shift, what inventory level to hold, how to handle an abnormal order, how to react when a machine fails or a shipment is late. The quality of the whole machine is precisely the sum of the quality of millions of those small decisions, and the speed of the machine is the speed at which it makes and corrects them. If AI can make each decision both faster and more correct, it touches the very heart of operations. If not, it is merely an expensive coat of paint on a machine still running as before.

And that is why the real question is not “which AI should we buy,” but: where to embed it in the flow of decisions, how to make it fast without being reckless, and how to measure that it truly improves outcomes. To answer with discipline, rather than chasing advertising, we need to view the story through operational tools tested by fire over decades, frameworks born to discuss exactly one thing: how people make decisions and improve within a system.