Leaders who embraced AI early are no longer learning the basics. Here’s how to catch up and stay ahead.

I run a healthcare technology company, and I didn’t come to the company with a formal background in AI. What I have is a view formed by building and testing these systems within real businesses, and those views are rapidly evolving.

Some leaders I know are fully committed. They are testing tools, prompting systems, and learning what works and what doesn’t. I am one of them. We also provide ongoing personalized training to all leaders on our team to ensure they are equally supported. We’re not asking people to figure it out on their own. We’re asking them to participate and we’re giving them what they need to succeed.

Others are waiting too. They want technology to feel more sophisticated, more proven, and more secure. That instinct creates a false sense of control. You don’t need a perfect system to get value from AI. Advantages are built by overcoming imperfections. By doing so, you can learn what’s broken and how to improve it.

A gap that you can’t see until it’s too late

There is a real difference between understanding what AI can do in theory and knowing how to apply it within your organization. This difference can’t be made up just by reading about it or watching a demo. It is built through experience. It comes from prompting the system, iterating over the results, course-correcting over time, and refining the inputs.

That last part is more important than most people realize. It doesn’t refine the output. Adjusting prompts and input. The output is what happened. What we’re actually developing is the ability to translate organizational mechanisms into instructions on which systems can operate. That skill doesn’t exist without practice, and it can’t be built from the outside.

While one group is avoiding early mistakes, another is building internal knowledge. And the gap gets even bigger. By the time those who waited felt the technology was stable, the early leaders had no longer learned the basics. They are optimizing, extending, and incorporating these systems into how their organizations actually operate.

What does AI education actually look like?

Most people understand large-scale language models in the abstract. AI goes out into the world, processes vast amounts of information, and produces output. What is less understood is the work of educating that model based on a specific business. That’s where the real impact lies and it takes intentional effort.

In practice, it starts with loading all the information the organization currently knows into the system. This means policies and procedures, legal documents, existing workflows, finances, sales activity data, onboarding processes, and customer history. Next, we’ll layer the user experience. In our business, it looks like this: What do case managers actually do today? What do intake specialists deal with? What does an account resolution specialist do? You’re describing your business as it is, but not as you want it to be.

Once that foundation is in place, you can move towards results. towards the results themselves, rather than how you think those results should be achieved. Show us how your time to raise money has been reduced from 14 days to 2 days. Show your customers what they want and where they are lacking. Compare the activities your sales team is performing to the win rate you actually need. Review materials, identify gaps, and suggest improvements. All with the aim of increasing revenue, increasing profit margins, and increasing customer satisfaction.

This is different from workflow redesign. Workflow changes are a much later output. What we’re doing in the early stages is educating and encouraging iterative change. Think of it as a second set of eyes on your business. You can analyze more variables than an individual and uncover opportunities you might not have identified on your own.

Review what the system recommends before implementing it. Repeat it. I’ll focus my training on that. This process is a way to build trust, and it requires real involvement from leaders.

What you actually need to go from run to prompt

Organizations that make the most of AI do more than just deploy the tools. They are rethinking the way work flows in business. This change is less about technology and more about clarity. If workflows are unclear, ownership is ambiguous, or success isn’t clearly defined, the system won’t uncover those issues. It simply won’t answer well. It’s not a cliché to say that garbage goes in and garbage comes out. This describes exactly what happens when a system is poorly educated.

What was once execution is now direction. What was once programming is now prompting. Leaders who get the most value understand their products, customers, and workflows deep enough to educate their systems about all of it. Then prompt towards the goal on the whiteboard without telling the system how to get there. The discipline of getting out of your own way and letting the system suggest a path is where most leaders first struggle.

This is also why I believe the real competitive advantage here is not technical. I would always prefer to hire strong operators with deep business knowledge rather than technical experts. People who understand what an organization does, how it does it, and what it’s trying to accomplish can educate and encourage these systems more effectively than people who can only mechanically explain how the systems work.

the risk is already there

Concerns about reliability are understandable. In a regulated environment, defective output is not always included. It moves through workflows, influences decision-making, impacts customers and patients, can create legal or compliance exposures, and is difficult to eliminate. These risks are real, which is why early experiments should be conducted in low-risk environments with explicit human review.

However, waiting does not eliminate the risk. It shifts it. Organizations that sit on the sidelines are accumulating another type of exposure. A widening capability gap, an inability to attract talent who expect these tools to be part of how they get their jobs done, and slow response times when the competitive environment demands it.

Within our organization, I speak up about my position. AI will not replace humans. It’s being replaced by humans who don’t want to take advantage of it. It’s not a threat. It’s an explanation of what’s already happening. We provide all leaders with the training, support, and access they need to participate. Those who choose not to do so will eventually face that reality, regardless of where they work.

Successful companies don’t wait for a better version. They are now building their knowledge base, educating their systems on how the business actually works, and pushing them toward outcomes that their competitors have yet to achieve. This head start is waiting for no one.

I run a healthcare technology company, and I didn’t come to the company with a formal background in AI. What I have is a view formed by building and testing these systems within real businesses, and those views are rapidly evolving.

Some leaders I know are fully committed. They are testing tools, prompting systems, and learning what works and what doesn’t. I am one of them. We also provide ongoing personalized training to all leaders on our team to ensure they are equally supported. We’re not asking people to figure it out on their own. We’re asking them to participate and we’re giving them what they need to succeed.

Others are waiting too. They want technology to feel more sophisticated, more proven, and more secure. That instinct creates a false sense of control. You don’t need a perfect system to get value from AI. Advantages are built by overcoming imperfections. By doing so, you can learn what’s broken and how to improve it.