Speaker 1: 0:00

Welcome back to Inspire AI, the podcast where we explore how artificial intelligence is reshaping work, creativity and society. I'm your host, jason McEntee. Let me start with this. Every professional I know is learning prompt engineering. They're taking courses on ChatGPT, udemy, swapping prompt templates, maybe even studying machine learning basics. Linkedin is flooded with posts about how to master prompts or become AI literate, and sure it's exciting. But here's the uncomfortable truth Execution has never been the bottleneck. The real bottleneck in every era of innovation is clarity. In this episode, we're going to dig into two skills that matter far more than prompt tricks Problem finding, which is the ability to identify what's actually worth solving. Communication, the ability to translate AI outputs into human understanding and action. Master these skills and you won't just keep up with AI, You'll stand out.

Speaker 1: 1:14

When GPT and other large language models hit the mainstream, we all became prompt tinkerers. Suddenly, people were experimenting. Explain this like I'm five, or act as a world-class lawyer, or even walk me step by step. We call these zero-shot, few-shot, chain-of-thought prompting, and it felt like magic. Let's give prompt engineering its credit right. It's lowered the barrier to entry. It gave people a way to harness complex models without needing to code. But there's a catch Prompt engineering is brittle. The right wording today may fail tomorrow. The perfect system prompt can crumble with a model update and, let's be honest as these systems evolve, they're getting better at understanding what we mean, not just what we say, which means this Prompt engineering may help you get short-term wins, but it's not the timeless skill we all think it is.

Speaker 1: 2:21

Here's where AI creates a real danger. Let's call it execution obsession, because when it's easier to generate, create and build, what do most teams do? They crank out more and more More reports, more campaigns, more features, and they feel productive because the wheels are spinning faster. But speed doesn't equal progress, you see. In fact, it can backfire in various ways. Let's think about how AI democratizes solution building.

Speaker 1: 2:57

But if you're not good at identifying real problems, you'll end up building elaborate answers to issues that never mattered. This is called solving fake problems. Think about the endless apps nobody needed or chatbots solving problems nobody had. Next, ai doesn't ask is this the right problem? It just helps you move faster with the assumptions you already hold. If your team believes low engagement means produce more content, ai will happily flood you with posts, but if the real problem is poor product fit, you just wasted everyone's time, including your customers. We call that amplifying biases. No-transcript. I've seen teams celebrate AI productivity gains without asking did any of this actually move the needle? This is what we call confusing motion with progress, and the riskiest teams today aren't the ones dragging their feet on AI adoption. They're the ones that have become incredibly efficient at solving the wrong problems.

Speaker 1: 4:18

There's an overlooked truth here Execution has never been the limiting factor In tech, specifically in data science and in product design. Projects rarely fail because teams couldn't build the solution they imagined. They fail because they built something nobody needed. Think about Instagram. They weren't the only photo app. They succeeded because they reframed the problem. People didn't want to share photos, they wanted to share moments. Or how about Tesla? Love it or hate it, they didn't dominate because they built a slightly better car or faster car. They redefined what problem cars should solve not just the transportation, but sustainability and energy. The companies that win aren't the fastest builders. They're the sharpest problem finders. Win aren't the fastest builders. They're the sharpest problem finders. Now that AI levels the playing field on execution speed, the only durable edge left is knowing what's worth executing.

Speaker 1: 5:27

Let me pause here and ask you in your own work, are you chasing execution or investing in clarity about the problem. So I offer you this To avoid execution obsession and to get better at problem solving. There's a proven framework out there. They call it design thinking. It's a structured approach to understanding human needs, defining problems, ideating solutions and testing assumptions. And when paired with AI, it's super powerful. And here's how they complement each other.

Speaker 1: 6:05

Ai can generate thousands of ideas. Design thinking helps you pick the one that matters. Ai can optimize for any metric. Design thinking ensures you choose the metric that actually counts. Ai accelerates action. Design thinking ensures direction.

Speaker 1: 6:26

So here are three habits that make this real. First, ask better questions, don't ask. How do we increase engagement? Two, observe behavior, not just words. Users may say they want more features, but if they're abandoning your app because of complexity, fewer steps might be the real solution. And then, finally, prototype your thinking. You should test assumptions before building A paper sketch. A quick survey or a landing page experiment can save months of wasted execution. This is how you turn AI from a productivity gimmick into a true innovation partner.

Speaker 1: 7:17

But even problem solving isn't enough. Like I said, there's another bottleneck communication. Here's the dirty secret of today's AI boom. Companies are drowning in AI outputs that sit unused Dashboards, reports, risk analyses the petabytes are piling up. They aren't being ignored because they're wrong, but because nobody translates them into human understanding. That's where communication comes in. Let's call it the translation layer for the AI tech geeks out there.

Speaker 1: 7:55

Your job isn't just to operate AI. It's to make it useful to humans by simplifying complex insights into plain language and, of course, contextualizing outputs so they fit your culture, your priorities and your constraints. You should be thinking about how to turn AI's what into a human. So what? And now what? And let's not forget about translating vague business questions into inputs AI can actually handle. Think about it when everyone has access to the same AI insights, the competitive edge doesn't go to the best operator. It goes to the best translator, the person who can explain what those insights mean and what to do about them. So how do you build this competitive communication advantage? You simplify.

Speaker 1: 8:52

Practice explaining AI recommendations in one sentence. If you can't do that, you probably don't understand them well enough. Yet you also want to be able to contextualize. Ai doesn't know your company's politics, budget or timing. You do so. Always frame outputs in the context of what actually matters. Focus on implications. Every AI insight should answer so what and what's next?

Speaker 1: 9:20

Let's not forget about developing the right frameworks. What's next? Let's not forget about developing the right frameworks. Think about having repeatable playbooks for how your team responds to AI insights, for example, if AI flags a customer risk. Here's the exact three-step process you should follow. Finally, ask better questions. Translate fuzzy human requests into clear, structured AI prompts. What used to be help us understand customers better becomes. Summarize the top three frustrations our customers mention in support tickets.

Speaker 1: 10:02

Communication isn't fluff, it's leverage. And the irony is, the more advanced AI becomes at generating outputs, the more valuable clear human communication becomes. And if we quit practicing those skills, we're going to give up more and more of our autonomy to the AI outputs and forget the origins and art of amazing communication that brings people and ideas together. So let's bring it all home. Prompt engineering while it's useful, now it's fading.

Speaker 1: 10:37

Execution obsession is a trap that makes you efficient at solving the wrong problems. Problem finding is timeless. It's the wrong problems. Problem finding is timeless it's the real edge. Design thinking is the discipline that keeps AI pointed in the right direction, and communication is the missing link that makes AI outputs actionable. In the end, the future doesn't belong to the fastest builders and the future doesn't belong to the fastest builders. I'll say it again the future doesn't belong to the fastest builders. It belongs to those who know what's worth building and who can explain why it matters. That's it for today's episode of Inspire AI. Thank you for listening. Don't forget to like share our content. Thank you for listening. Don't forget to like, share our content. Remember clarity beats speed and communication turns insight into impact.