Is Your AI Idea Any Good? A 5‑question Stress Test From a Serial Founder

In the gold rush of AI startups, how do you separate the valuable ideas from the interesting ones? In her latest piece for Every, Stella Garber offers a five-part framework for evaluating business opportunities before you expend time and capital. Drawing from personal experience across five startups and 32 angel investments, she provides a practical roadmap for founders navigating the AI landscape. I love the mix of strategic wisdom (focus on execution over ideas) and tactical guidance (invite users into a Slack channel to share feedback)—read on to learn more.—Kate Lee

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With startups, it’s almost never about the idea—it’s about the execution.

As a founder, I would know: I’ve had bad ideas that turned out well, and some ideas I thought were great that turned out to be…not so much. Now that it’s easier than ever to make your product idea a reality with AI, how do you tell if your idea is fatally flawed before you waste time, energy, and seed money?

Before my startup Hoop had its current product direction—an AI executive assistant for top busy founders, leaders, and execs—we were looking to solve decision-making for teams. We began making an app where teams could lay out decisions asynchronously and create team-wide decision logs. There weren’t many participants in the market, so we figured it was a big opportunity. This turned out to be a bad omen: Decision-making is convoluted, people don’t want to add another collaboration tool to their workflows, and AI doesn’t solve the complexity of human communication. So we pivoted into an area where my team had a lot of experience: work management—and conversion to paid users went from zero to 15 percent.

Now we’re building an AI-native solution to an age-old problem: How do I make sure I don’t drop the ball on everything I have to do? AI gives us an opportunity to make something that was prohibitively expensive, an executive assistant, more widely available to more people.

With every hype cycle, there are a lot of founders who want to get in on the action. And the temptation to seize on an idea and charge ahead as fast as possible is real. But if you want to build something lasting, you need to make sure you’re not falling into the trap of doing what’s popular versus doing what’s right for you (and being the right founder to solve the problem).

I’m currently on my fifth startup, with a range of outcomes from not-so-great to life-changing success. As an angel investor, I’ve written 32 checks into early-stage companies. Over time, I've developed a set of critical questions that help me distinguish between AI ideas that are merely interesting and those that are genuinely valuable. Here is my five-part framework for thinking through whether there's a market for your startup, and how to think critically before diving into a specific idea.

1. What existing workflow are you fundamentally changing?

AI isn't magic. It's a set of tools that needs to dramatically improve an existing process. The most compelling AI startups don't just add a feature: They reimagine entire workflows, remove the need for them, or figure out ways to automate them.

Innovation makes people’s lives easier by helping them accomplish more in less time, or freeing up their time for creative or leisurely pursuits.

Take task management. Most of my teammates at Hoop worked together previously at Trello, where we worked on the product, design, engineering,and marketing teams. We brought this experience into the next generation of task management by thinking through how AI can help people in ways software previously couldn’t.

The breakthrough wasn't just, “AI can prioritize my existing tasks.” It was, “I don’t need to create tasks or remember to do things because AI should know what I’ve committed to by being omnipresent in my workflow.” We didn’t want to make task management incrementally better. We wanted to eliminate the need to write down and organize tasks entirely.

Startups have an edge on incumbents in AI because many incumbents tack on AI features to existing legacy codebases. The real innovation is thinking through first principles on how to fundamentally solve a problem.

2. Where is the customer's deepest frustration?