Using AI is Easier Than You Think

In my last column, I wrote about treating Artificial Intelligence more like a junior protege and less like a search engine to get it to perform meaningful tasks that you can use in your daily life (“Learning to Talk to AI,” November 2025). Now, let’s talk about what AI is actually being built for, and it’s probably not what you think.

It’s meant to turn you into a programmer. And that’s easier than it sounds.

Many of us look at AI as the ChatGPT widget on our phone that drinks a lot of water and gives half-baked answers, and we’ve talked about how to at least get better responses per gallon, as it were, with smarter prompting, collaborative approaches, and intentionality. But the real forces driving investment in AI are in the software development marketplace, not the consumer chatbot interface.

In 2025, not only did over half of all venture capital flow into AI, but the majority of it went into software development tools. Consumer-facing chatbots and the like don’t even make the cut as an investment category because, in reality, they are merely byproducts of the real revolution happening in AI.

The primary drivers of AI investment are technical tools designed to address business challenges. But here’s the thing: those same tools can help you solve technical problems, including some you may not have thought to throw at AI.

For example, let’s say you want to visualize your monthly spending.

You’ve got a spreadsheet with transactions, and you want a clean chart showing where your money goes. You could wrestle with Excel’s chart wizard and end up with something that looks like it was designed in 1997 — a 3D pie chart with shadows, cluttered labels, and colors that clash. Or you could describe what you want to AI: “Here’s my spending data. Create a clean, modern chart showing my expenses by category.” What you get back is something you actually want to look at — clear, readable, professional.

But first, you need to go into ChatGPT and turn off the toggle that lets you opt out of it using your data for training, which is on by default. Second, clean your data to remove any identifiers AI doesn’t need for what you want it to do.

That’s a small example, but it illustrates the point. If you’ve ever built a complex spreadsheet, you’re already thinking like a programmer. You just don’t have the job title. The skill that matters isn’t knowing how to write code. It’s knowing how to break a problem down into logical steps to find a solution. That’s what you’ve been doing all along.

Here’s another example. In early January, Anthropic released an AI tool called Claude for Healthcare. It’s a tool for you to connect your health records through services like HealthEx, Apple Health, and Android Health Connect. Then you can ask Claude to summarize your medical history, explain test results in plain language, spot patterns across your health data, or help you prep questions for your next doctor’s appointment.

I know what you’re thinking: Is this safe? It’s a fair question, and one I’ve been asked repeatedly. Here’s what matters from a privacy standpoint. First, it’s opt-in: You choose exactly what information to share, and you can disconnect or change permissions at any time. Second, your health data is not used to train their models. Third (and this is the part that impressed me as someone who works in healthcare data security), Claude doesn’t download your entire medical file. It uses something called the Model Context Protocol to retrieve only the specific pieces of your record needed to answer your question. That’s a meaningful privacy protection. It’s the “minimum necessary” principle built into the architecture.

None of this replaces your doctor. Anthropic is clear about that, and so am I. But think about what this actually solves. You’ve got test results from three different labs, medication lists from two pharmacies, notes from specialists who don’t talk to each other, and an insurance company that speaks a language nobody understands. You’re the one trying to tie it all together, usually on the phone, usually frustrated. Now you have a tool that can help you make sense of it — one that’s grounded in your actual health history, not generic advice from the internet.

This is the kind of problem AI was built to solve. Not chatbot small talk. Real, complicated, personal problems where you need help organizing information and asking better questions.

You don’t need to start with your medical records. Start with that messy spreadsheet you’ve been meaning to fix, or that report you build by hand every month, or that question about your finances you’ve never been able to answer. Describe what you want. See what happens.

The worst outcome is that it doesn’t work, and you learned something. The best outcome is that you solve a problem that’s been nagging you for years. Either way, you’re closer to being a programmer than you think.

Thad Dickson is CEO of Xpio Health, a Gig Harbor company focused on security and compliance for healthcare organizations. He lives in Lakebay.