When the People Who Understand the Problem Can Build the Solution

The biggest change AI has brought to my work is that I can do more with what I know. After more than 20 years in technology sales and leadership, I can take my understanding of a problem further into building its solution. Experience that once informed a recommendation can now help shape a working product.

My career in IT began around the Y2K days, repairing computers for a hospital. I've been an evangelist for technology ever since. Over the past two years, tools including Claude have helped me move from talking about and selling technology to building with it directly. I founded LYFYE, where I use a digital workforce across software, mobile applications, automation, AI agents and internal systems.

That experience keeps bringing me back to one question: What becomes possible when the people who understand a problem can also build its solution?

In many businesses, the people closest to the work can tell you exactly where things get difficult. They know why a process breaks down, which information is always missing, or what a customer has to do to work around a system. That knowledge can come from years of doing the work. It can be difficult to capture in a requirements document because so much of it lives in the details someone recognizes almost without thinking.

For a long time, acting on that knowledge usually meant explaining an idea, finding the right technical help and getting it onto a list of competing priorities. Those steps still have a place. AI is adding another way to begin. Someone with a clear understanding of a problem can start making something concrete, explore an approach and put a working version in front of someone who can help improve it.

An idea becomes easier to question when you can interact with it. You can see where your own thinking was incomplete. Someone else can point to the part that feels awkward or show you an exception you hadn't considered. Building gives you a way to learn about the problem itself, and a shared starting point for deciding what to do next.

Readers, one of the mobile apps I've published through LYFYE, makes that change tangible for me. It combines illustrated stories, narration and multiple languages, and is available on Apple's App Store and Google Play. With AI's help, I've been able to participate directly in bringing those elements together in a product. The decisions inside that work still matter: what belongs in the experience, how the different parts fit together and what needs more attention. Having help with the implementation gives me more ways to explore those decisions.

That's why I think experience deserves more attention in the conversation about AI. Years spent working with customers or understanding an industry give you a sense of what matters and which assumptions deserve a second look. If you've spent a career recognizing problems and helping others work through them, you have knowledge worth bringing into the building process. You also have a starting point for asking better questions of the tools.

My background in sales is relevant here. Selling technology involves understanding what someone is trying to accomplish and whether a proposed solution makes sense for them. Those questions follow you into building. You can produce something that works as designed and still miss what a person actually needs. Being able to build makes it even more important to listen carefully and choose a worthwhile problem.

For business leaders, there is an opportunity to involve more of the people who understand the work in shaping the tools used to do it. Someone who has never considered themselves a software builder may have a useful idea and enough context to explore it. Giving that person room to try, with appropriate support, could bring forward ideas that would otherwise remain informal suggestions or familiar complaints.

Technical specialists have an important role in that process. A person who knows the business can arrive with something specific to discuss. An experienced engineer can challenge the approach, recognize risks and help make decisions that will hold up as the work grows. People who will use the result can test whether it fits their needs. I see a great deal of possibility in getting those perspectives into the same conversation earlier.

The learning involved is substantial. I use AI in almost every aspect of my daily operation, and keeping up with the moving parts can be challenging. The tools change quickly. You have to make time to understand what you're building, test it and decide where additional expertise is needed. Some ideas will need to be reworked. Taking responsibility for those decisions is part of building, however much assistance you have.

I expect some worthwhile ideas to be very specific. A tool that addresses one difficult part of a particular job could mean a great deal to the people doing it. Someone with years in that role may see an opportunity that is easy for others to overlook. Making it possible for more of those people to participate could broaden the range of problems we choose to solve.

That's what makes this period so interesting to me. I've been enthusiastic about technology for a long time, and I'm still learning how much more directly I can participate in creating it. Building with AI has expanded the range of things I can attempt. The work ahead is figuring out which of those possibilities are genuinely worth pursuing.

If you could bring your experience directly into building a solution, what problem would you finally be able to do something about?