Wasted energy, resources, time, or cash will eventually destroy even the best business strategy.

Every dollar spent on unnecessary work, every hour lost to poor coordination, and every decision delayed by incomplete information creates friction inside the organization. Over time, that friction compounds, reducing profitability, slowing innovation, frustrating customers, and ultimately diminishing shareholder value.

The antidote is operational efficiency.

When most leaders hear the term "operational efficiency," they immediately think about cost reduction. They picture lean operations, lower headcount, tighter budgets, and squeezing expenses wherever possible.

But the most successful organizations understand something different.

Operational efficiency is not simply about lowering costs. It is about creating a system where people, processes, technology, and resources work together to deliver maximum value to customers. When done well, operational efficiency improves quality, accelerates innovation, strengthens customer relationships, increases profitability, improves cash flow, and creates sustainable competitive advantage.

In other words, operational efficiency is one of the most important enablers of business strategy.

And today, artificial intelligence is rapidly becoming one of the most powerful tools available to improve operational efficiency across the enterprise.

Based on our recent work helping organizations build business acumen and leadership skills in the age of AI, here are five practical ways AI can improve operational efficiency while supporting the execution of business strategy.

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This is now the first question asked at nearly every business acumen simulation workshop we deliver for our global clients, regardless of industry, geography, or leadership level.

"Are we allowed to use AI?"

The answer surprises people.

Yes.

And no.

For context, our business simulations are not connected to AI. Participants never have access to the underlying simulation engine, the algorithms, or the calculations that determine the business results. They cannot ask AI for the "right answer" because the AI has no access to the actual simulation model.

Participants can copy and paste information from reports, dashboards, financial statements, market research, and operational data into an AI assistant to request guidance, coaching, recommendations, or explanations.

In other words, AI can help them think. But AI cannot do the work for them.

At least not yet. What has fascinated me over the past six months is watching how different teams use AI and how dramatically the outcomes vary. Some teams become smarter, faster, and more strategic. Others become dependent, disengaged, and surprisingly ineffective.

The experience has convinced us that the real question is not whether AI should be allowed in learning environments.

The real question is whether people know how to use AI without surrendering their own judgment.

Based on what we have observed and measured across hundreds of participants, here are five reasons to allow AI into business simulations…and five reasons to be cautious.

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I hate to be the one to break it to you, but your Q2 results are already screwed.

Most of the reasons why are probably out of your control. Your cost of goods sold has increased due to geopolitical issues affecting supply chains. You’ve “taken price” as much as you can without losing customers. You’ve already cut costs to the point that you are starting to alienate both your customers and your employees.

It’s not a pretty picture.

And that’s the dangerous moment. Because when leaders feel margin pressure, the instinct is to squeeze harder. Cut another role. Delay another investment. Reduce another service level. Push suppliers one more time. Stretch employees just a little further.

At some point, you have to ask, “Is this worth it?”
Or more importantly, “Am I cutting into the bone past the point of no return?”

Obviously, there is not much you can do about past margins. But there are absolutely things you can start doing today to build future margins while protecting your key customer relationships and employee morale.

Here are five things leaders should be doing today to improve the margins of tomorrow.

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Everyone in the business acumen simulation workshop I conducted last week was using AI assistants to research data, understand reports, and make decisions.

Some teams performed better than others, and a couple were absolutely extraordinary.  They set all-time simulation records for stock price, revenue growth, profitability, and shareholder value.

Naturally, during the final debrief, I asked the top-performing team what they had done differently. I expected to hear something about strategy, financial analysis, or decision-making discipline.

Instead, one participant said something fascinating:

"We coached our AI agent much differently than we coached each other. The feedback and the way we communicated with the AI were much clearer and more clarifying, and it made a huge difference."

The room got quiet.

As we unpacked that statement, it became obvious that they had stumbled onto something profound. The skills required to coach AI effectively are surprisingly different from the skills required to coach people effectively.

In fact, in some ways, AI is exposing weaknesses in how we communicate with each other.

As we continued discussing it, something started to bother me.

For years, leaders have been taught that coaching is fundamentally a human skill. It requires empathy, trust, emotional intelligence, and the ability to adapt your communication style to different personalities.

Yet here was a team telling me that one of the reasons they were successful was that they coached an artificial intelligence tool more effectively than they coached each other.

The more we talked, the more I realized this wasn't really a conversation about AI at all. It was a conversation about communication. AI was simply exposing habits that have quietly crept into our workplaces for years.

We have become comfortable being vague. We assume people know what we mean. We avoid difficult feedback. We rely on titles and authority to drive action.

And because humans are generally pretty good at compensating for our communication flaws, we rarely notice the problem.

AI doesn't compensate. It simply gives us back exactly what we asked for. That realization led to five observations that I haven't been able to stop thinking about.

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What “Owning” the Work Really Means in the Age of AI-Enabled Leadership

One of the most interesting parts of my job is interviewing senior leaders from around the world and across industries to gather their insights so we can build realistic business leadership simulations that help organizations learn by doing.

Recently, as part of a new leadership simulation for a global professional services firm specializing in construction and project management, I had a fascinating conversation with one of their senior leaders.

Like many organizations, they are trying to develop stronger early-career and mid-level leaders. Their business operates in two worlds simultaneously. They must execute today's projects flawlessly while also building tomorrow's business through a pipeline of opportunities, customer relationships, and future commitments.

During our discussion, we began talking about accountability and ownership. The leader made a comment that really got me thinking about what he said and then about what I am seeing in the business world these days.

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