The gap between "I wish this tool existed" and "I just built it myself" has officially disappeared, and most business leaders haven't caught up yet.
That's the takeaway from my recent conversation on Episode 191 of The Artificial Intelligence Show with SmarterX and Marketing AI Institute founder and CEO Paul Roetzer. He shared how he's been using AI to solve everyday problems, including building custom software applications with zero coding experience.
One of the most immediate use cases Roetzer shared involves financial reporting. When his COO shared a screenshot of the company's recent Profit and Loss report, Roetzer decided to run an experiment.
He dropped the screenshot into both Google Gemini and his ChatGPT "Co-CEO" custom GPT with a simple prompt: "Analyze this and write a summary I can share with the team. Focus on insights and opportunities."
Despite providing minimal context, both AI tools produced financial summaries that demonstrated what Roetzer calls "tremendous potential" for automated reporting. The exercise reinforced a broader initiative underway at SmarterX: tighter integration of AI into all company reporting so teams can more quickly turn data into intelligence, intelligence into actions, and actions into outcomes.
Sometimes the most powerful AI use cases are the simplest ones. Roetzer was planning a six-hour drive from Cleveland to Chicago for a speaking engagement with Good Karma Brands when winter weather took a turn. Rather than bouncing between weather apps, flight booking sites, and traffic reports, he turned to Gemini.
He explained his plan to drive from Cleveland to Chicago the next day but now the weather and roads didn't look great. He asked the AI to review the forecast and consider whether he should drive or flying.
Gemini's response? Don't drive.
Then it searched flight options and recommended ones for Roetzer to book.
What would have taken 20-25 minutes of tab-switching and manual research was handled in a single conversation. The AI synthesized weather forecasts, traffic conditions, and flight availability into a clear recommendation. This kind of decision-making used to require either significant research time or a human assistant.
“It was awesome,” Roetzer says.
But the travel planning story was just the warm-up. The real breakthrough came when Roetzer decided to tackle a problem that had frustrated him for years: finding a decent org chart builder.
"For years, I've been trying to find an org chart builder that could help me visualize different structures for the organizational design of the company, and I’ve always been disappointed," he says.
Instead, he decided to build his own using Lovable, a no-code AI app builder. He started with a straightforward prompt: "I need to build this interactive org chart and here’s what I want it to do."
Lovable took him through a series of questions about features and functionality, using its reasoning capabilities to clarify requirements. Ten minutes later, he had an org chart that he continued to chat with Lovable about to make some tweaks.
"Things that I used to have to email a developer, say, 'Hey, here's five changes I'd like to make.' And then 24 or 48 hours later, I'd get the comp sets back from the developer. These are not what I wanted. It’s this super iterative, very long drawn out process," says Roetzer.
"Now it's like, well, what about this? Let's take a look at this. Okay, great. Ten seconds later, boom. Here you go. In production. It was amazing!"
As someone with zero coding ability, he was able to build and refine a working app better than anything he'd ever tested from commercial software vendors.
"Let that sink in," Roetzer wrote in his weekly newsletter. "With AI, you can just build things."
Here's where things get interesting from a competitive standpoint. Roetzer points out that most organizations are still struggling with basic generative AI adoption, such as figuring out how to use ChatGPT or Gemini for everyday tasks. Meanwhile, a small cohort of AI-forward professionals has already moved to the next level: building custom tools on demand.
"We're three years into gen AI and there are still a ton of companies that haven't figured out how to use ChatGPT or Google Gemini or whatever," he says. "I feel like we're gonna have the same adoption curve to where you can just build things.”
For internal tools and workflow automation, the barrier to entry has effectively vanished. And those who recognize this now have a significant runway advantage.
"That knowledge, that ability to run a company that way or a team or campaigns, where you can just build tools, that is going to take years for the rest of the business world to adapt to," Roetzer explains.
"But for those of us who know how to do it right now, you have this massive head start to go do these things."
There's a catch, though. With so many AI tools and capabilities available, keeping track of what you've already built, or even what conversations you've already had, is becoming its own challenge.
Roetzer describes a phenomenon he calls "AI overload": starting a new project in one AI tool, only to realize partway through that he'd already explored the same idea weeks ago in a different platform.
"I'll be like, ‘Oh, I should talk to ChatGPT about this thing, or I should go build this app. Then I'll get in there and start the thread and, ‘Wait a second. I think I've had this conversation already somewhere,’" he says. "And then you're going into Gemini and ChatGPT and you're thinking, ‘Where is that?’"
The solution, he suggests, could come from more deeply integrated AI experiences, or tools that live inside the applications where work actually happens, rather than requiring users to switch to separate AI interfaces.
"I said in the early days of all this, I just want to go into Google Docs and have Gemini in there. I don't want to have Gemini out here where I'm having these separate conversations," Roetzer says.
"I'm overloaded by what's possible and it makes it really hard to keep track of all the stuff that's going on."
The ability to create custom software in minutes is a fundamental shift in how organizations can operate. Need a specialized tool for your team? Build it. Want to automate a repetitive workflow? Describe it to an AI and deploy it the same afternoon. Need to turn raw financial data into executive-ready insights? Drop a screenshot into an AI and ask for analysis.
This capability will eventually become commonplace. But right now, it's still a differentiator.
It's about reimagining what's possible when you can build exactly the tools you need, without waiting for IT, without hiring developers, and without settling for mediocre products that don’t get you to a solution.
As Roetzer put it in his recent ExecAI Insider newsletter:
"The future of work has arrived. It's just not evenly distributed."