If you’ve been anywhere near earth this year, you’d think AI personally invented e-commerce. Every talk, every LinkedIn post, every “next big tool” pitch is about it. Liek who didn't hear that ChatGPT now has checkout abilities this week?

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Where AI actually matters is product development. It’s now a HUGE part of the business where data and instinct have to work together to win.

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That’s where my attention has been this year — and it led me straight into one of the most effective systems for product research and launch I’ve seen yet.

AI, Product Research, and the Real Reason Jungle Scout Isn’t Enough Anymore

While I haven’t had Ritz crackers in years, I still remember what made them good — simple ingredients, perfectly balanced, hard to replicate. (Chat gave me this line, and I just went with it)
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That’s how I’d describe Izabella Ritz’s product research framework. It looks simple on the surface, but underneath it’s a balanced system that’s nearly impossible to duplicate.

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From May to July: Learning the Ritz Momentum System

I first met Izabella Ritz a few years ago at ASGTG, but we really connected at my favorite conference in the space, Sellers Summit hosted by my friends Steve Chou and Toni Herrbach. A few weeks later, I joined her 60-Day Accelerator through Ritz Momentum, which ran from May to mid-July.

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The program focused on one thing: de-risking Amazon-first product launches through a blend of AI-driven insights and human judgment. The whole idea is data is backing your product development process, not subjective human emotions.

Izabella’s methodology is AI-centered but not AI-dependent.​
It’s not about pulling data from SmartScout, DataDive, or Helium 10 and asking ChatGPT, “Should I sell this?”
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It’s about learning how to interpret that data — what it means, what to ignore, and what questions still need a human to answer.

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Over the two months, we met twice a week — one main Zoom call and one office-hours session. Her agency even picked a product for me as part of a white-glove service while training me on their internal SOPs.

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It was impressive. I’ve since shared parts of her framework in several masterminds, and even veteran sellers running seven- and eight-figure operations stopped to take notes (and yes, I gave full credit).

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By the way she does this newsletter thing too. Check it out.​

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The Old Playbook Doesn’t Work Anymore

Gone are the days when you could open Jungle Scout, sort by search volume, and call it product research.
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That playbook died around the same time “Amazon Basics” started eating entire categories alive.

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If you’re still using that approach, you’re not doing research — you’re gambling.

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The Ritz Momentum process is how serious operators now adapt: AI finds the patterns; humans decide what’s actually possible.

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October: When the Software Claimed to Replace the System

Fast-forward to early October.
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Just last week, I attended a webinar by Shawn Hart and Seth Stevens, who unveiled a software that supposedly automates the entire research and validation process — everything from niche analysis to launch confidence scoring.

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The tool, called Scale Pilot, is built around four success factors:
​Nicheability. Growing Demand. Profitability. Launch Confidence.

Sound familiar?

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It’s essentially the same framework Izabella trains her accelerator students to execute manually — now wrapped in an “AI does it for you” interface.

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My Take

The demo was impressive. But let’s be honest: automation doesn’t replace discernment.

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AI can crunch data, but it can’t feel risk.
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It doesn’t understand supplier reliability, category nuance, or when a product’s “profit potential” evaporates under a 15% ad tax.

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Software can narrow the haystack, but it still takes a human to pick the needle.

So from May to October, I’ve seen both ends of this evolution:

• AI-assisted human strategy through Izabella Ritz.
• Fully automated prediction through Shawn Hart and Seth Stevens.

And the truth sits right between them.

AI can accelerate discovery — but it can’t replace reasoning.

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What’s Next

I’m currently applying the Ritz Momentum process to validate and launch new products, layering in what I’ve learned from both approaches.
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In the next issue, I’ll share how the data looks once human strategy meets AI prediction — and whether either side truly wins.

Until then, if a tool promises to “do all the work for you,” remember:
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That’s what people used to say about Jungle Scout.