June 16, 2026
Every AI shopping agent will eventually have to answer a deceptively human question:
“Will this actually fit me?”
In a new episode of Street Talk with Arthur Zaczkiewicz, True Fit co-founder and CEO Jessica Murphy talks about what nearly two decades of fit intelligence infrastructure means now that agentic commerce is moving mainstream.
The conversation covers why fit has become one of retail’s most important AI use cases, how True Fit is connecting fit intelligence into AI shopping agents through MCP, and why retailers need to ask harder questions about the data behind the AI tools they are deploying.
Because the front-end experience can only go so far. Once the product is in the shopper’s hands, the truth shows up: did it fit, did the shopper keep it, or did it become another avoidable return?
For retailers, that is where AI needs more than a clever interface. It needs reliable ground truth data that can help shoppers buy with more confidence while protecting conversion, margins, and trust.
Fit has never been just about choosing small, medium, or large.
Shoppers want to know how the waist will hit, whether the inseam is right, whether the fabric has stretch or structure, where the hem will fall, and whether the item will feel as good in real life as it looked online.
Those questions become even more personal as shoppers’ bodies and preferences change. In the episode, Jessica and Arthur discuss how factors like GLP-1-driven body change are adding new pressure to sizing and fit, especially in categories where shoppers are already underserved or unsure what will work for them.
That is why fit guidance has to move beyond “What size should I buy?” and into the real questions shoppers ask before they commit.
One of the clearest points from the episode is that the shopper journey has changed.
For many shoppers, the product detail page is now the real front door to the brand. It is where discovery, evaluation, comparison, and decision-making increasingly happen.
That matters for AI investment because the PDP is also where uncertainty becomes expensive. If a shopper cannot understand whether an item will work for their body, their preferences, and their expectations, the retailer risks hesitation, size sampling, returns, or lost trust.
As AI shopping agents become part of that journey, the quality of the answer matters even more.
A generic AI agent can describe a product, summarize reviews, or repeat information from a page.
But fit requires more than language.
It requires intelligence grounded in real shopper behavior, product attributes, size and fit patterns, and outcome data. True Fit’s network spans 91,000 brands and hundreds of millions of shopper profiles, giving retailers access to fit intelligence that is difficult to recreate with generic AI alone.
That is the ground truth question Jessica raises in the episode: before deploying an AI agent, retailers should ask what data the system is actually using to make its recommendations.
If the answer is only product copy, scraped content, or generic model knowledge, that may not be enough for one of the most personal questions in commerce.
Jessica also discusses how True Fit is connecting its fit intelligence directly into AI shopping agents through Model Context Protocol, or MCP.
That matters because retailers are not all building the same AI experience. Some will build their own agents. Some will use broader platforms. Some will orchestrate multiple specialized tools.
MCP makes it possible for fit intelligence to show up inside those agentic flows, wherever purchase decisions are increasingly being made.
The goal is not to force every retailer into one interface. It is to make trusted fit intelligence available across the AI-powered experiences shoppers are beginning to use.
The episode also connects fit intelligence to one of retail’s biggest financial challenges: the $850 billion returns problem.
For shoppers, poor fit creates frustration and regret. For retailers, it creates margin pressure, operational cost, inventory complexity, and lost confidence.
That is why fit intelligence matters to both the shopper and the CFO.
A better fit experience can help shoppers make decisions with less uncertainty, while helping retailers reduce avoidable returns at the source.
Jessica frames True Fit’s value proposition clearly: less regret for shoppers and fewer returns for retailers.
That is the real promise of AI in fit. Not just a more impressive interface, but a better decision.
As commerce becomes more conversational, the winners will not simply be the retailers with the most AI features. They will be the ones that can give shoppers answers they can trust when it matters most.
Watch the full episode on YouTube:
https://www.youtube.com/watch?v=RPjL-JmIFmU
Watch the YouTube Short:
https://www.youtube.com/shorts/z1m5lMGn5r8
Listen on Spotify:
https://open.spotify.com/show/0NjzltICG0asaQe6lhoGQa
Listen on iHeartRadio:
https://www.iheart.com/podcast/269-street-talk-with-arthur-z-291218379/
Request a demo to learn how True Fit helps retailers improve shopper confidence, reduce fit-driven returns, and bring trusted fit intelligence into the next generation of AI commerce.
