It is easy to blame “big bad pharma” for everything that is wrong with healthcare. It is worth remembering that prescription drugs represent only one piece of the total healthcare-cost equation.

To many, it is just as easy to blame the doctors, nurses, technicians, administrators, and thousands of other employees working inside large healthcare systems.

Healthcare is expensive. It is complicated. There are egos, inefficiencies, mistakes, long waits, confusing bills, aging facilities, staffing shortages, and dozens of other things that make healthcare providers easy targets for criticism.

Until you need them. Then it becomes a very different story. I recently had the opportunity, although “opportunity” probably isn't the word I would have chosen,  to experience the healthcare system from the other side. And it caused me to rethink something I have been teaching for decades.

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We’ve started noticing something interesting during our business simulation rounds.

Participants are increasingly using AI as they work through complex business decisions. That’s not surprising. AI has quickly become part of how many of us analyze information, explore ideas, and solve problems. What is interesting is how differently people use it.

Some participants take a screenshot of their simulation results, paste it into an AI tool, and ask: “What is happening here? What should I do next?”

Within seconds, AI can read the numbers, identify trends, summarize performance, and suggest a course of action. Useful? Absolutely.

But we’re also seeing participants use AI differently. Instead of asking AI to do the analysis, they develop their own interpretation first and then use AI almost like a business consultant to challenge assumptions, uncover relationships, and test their thinking.

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My head is still spinning from ATD in May. Everywhere you turned, there was another AI
announcement, another product launch, or another promise about how AI was going to transform learning. It was exciting, but also a little overwhelming.

A few months later, the picture is becoming clearer.

Organizations are moving beyond experimenting with AI and beginning to identify where it delivers real value. Rather than asking, “Should we use AI?” the question is becoming, “Where should we use AI first?”

As we head into the second half of 2026, here are five trends I believe will have the biggest impact on Talent Development.

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 Short, AI-powered business simulations that prepare teams for real customer conversations. 

The modern customer has fundamentally changed the way they buy and behave, forever changing the sales process.

The new customer no longer needs sales professionals to explain product specifications, compare features, or provide information that can be found in seconds with an AI assistant. Before the first meeting ever takes place, customers have already researched your company, evaluated your competitors, read analyst reports, explored your competitors' websites, and formed opinions about the solutions available to them.

What they cannot easily find is someone who understands their business.

Someone who appreciates the financial pressures they face, the strategic initiatives consuming their leadership team's attention, the operational challenges limiting growth, and the difficult trade-offs they make every day.

That is why the definition of a great salesperson is changing.

The most successful commercial professionals are no longer simply product experts. They are business advisors. They create value by helping customers solve business problems, not by explaining product features.

The question for commercial leaders is straightforward:

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 Why successful AI adoption is really about balancing competing business priorities.

For the past two years, the conversation around artificial intelligence has focused almost entirely on the technology. Which models are better? Which AI tools should we use? How quickly is AI evolving?

While those are important questions, they aren't the questions most organizations are struggling to answer.

The real challenge isn't AI itself. It's the competing priorities AI introduces across the business. Senior leaders are pushing to move faster and embed AI into products, services, and business processes. Employees are trying to determine which tools they should use. Legal and Security teams are working to protect the organization. Finance is trying to understand rapidly changing costs and whether AI investments are producing measurable business value.

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