Brian Wisniach
Content Marketing
TL;DR
AI shopping assistants answer the questions that stall a purchase (fit, specs, compatibility, “will this work for me”) so shoppers can decide with confidence instead of guessing.
It’s for e-commerce merchants weighing whether to add it to a site that may already have a support chatbot in place.
AI shopping assistance works when it does the research legwork, draws on accurate live product data, and still leaves the actual choice to the shopper.
What questions stop your shoppers from buying?
Half of shoppers have walked away from a purchase in the last six months because they couldn’t find enough product information to feel sure about it, and 35% went on to return something anyway because what they got didn’t match what the page led them to expect. Take another look at that second number. Yes, a shopper who walks away costs you a sale you never had. But a shopper who buys, then returns, costs you that sale, the shipping both directions, and a shopper who now associates your brand with getting it wrong. That’s a bell you cannot un-ring.
The questions that lead there usually don't vary much:
Will this fit?
Will it work with what I already own?
Will it hold up doing the specific thing I bought it for?
What's actually different between this one and the nearly identical one two tabs over?
What happens if I'm wrong?
None of those questions are unreasonable to ask. They represent the plainest form of due diligence a shopper can do before handing over their card.
The trouble is there's rarely a person standing in front of them anymore, just a product page that can’t answer back. A seller on eBay captured how blunt that gap gets in practice, describing buyers who ask 'will it fit me' and stop there. No measurement and no further context. It almost reads like a lazy question until you realize this is simply someone who's used to asking a person and has nowhere else to put the question.
So, before you build anything, go ahead and pull 90 days of pre-purchase chats and emails, tag each by question type, and check which types cluster around your highest-return SKUs.
Leaving those questions unanswered costs you three times
Most brands track these as three unrelated problems, but in reality, they are the same problem counted three different ways.
The shopper who leaves rather than guess shows up as abandonment, a marketing number.
The shopper who guessed wrong shows up weeks later as a return, an ops number.
And the shopper who asked instead of guessing shows up as a support ticket, usually answered well after they've already closed the tab and bought somewhere else.
Migraines come in threes. Three teams, three dashboards, three KPIs nobody's comparing against each other. That is why the size of this entire discussion can be so easy to underestimate.
If you’re able to answer the questions we discussed earlier, before the shopper has to guess, all three numbers move together. Leave it for them to figure out on their own, and three teams keep fighting a fire lit by the same match
How can ecommerce brands use AI to answer detailed product questions before purchase?
Think of it as the research a genuinely good in-store associate would do, compressed into the few minutes a shopper spends on the page.
Shopper asks | What a good assistant does |
|---|---|
“Will this fit me if I’m usually a medium in [brand]?” | Cross-references your size chart against known fit patterns for that brand, flags the discrepancy |
“Does this work with my existing setup?” | Checks compatibility against the shopper’s stated equipment, not a generic spec sheet |
“Will this hold up on a cold-weather trip?” | Pulls the actual durability specs relevant to that use case, not the full spec dump |
“What’s the difference between these two?” | Compares the two SKUs directly, on the dimensions the shopper cares about |
Where should AI stop and let the shopper decide?
There's a version of this that goes too far, one where the assistant doesn't just answer the question but picks the product and adds it to a cart before your shopper's sure. Which touches on a fascinating philosophy when it comes to the shopping experience as a whole.
Seel CEO Zack Peng puts it this way:
"Humans are visual animals... I think shopping is like gaming in a way. You don't want to automate away the fun, but you should automate away the legwork."
The joys of shopping (discovery, browsing, the moment something resonates with your specific taste) must feel like an owned experience to your shoppers. Nobody is going to miss comparing prices, tracking a return, and chasing a refund. Agentic commerce should take these things off their hands entirely and ensure the shopping experience remains something they'd brag about finding themselves.
In practice, that means an AI shopping assistant that asks clarifying questions instead of guessing, offers a short list instead of a single verdict, explains the trade-off between options, and never adds anything to cart without being asked to.
Gartner’s own research backs this up with numbers. In a pair of 2025-2026 surveys, only 11% of consumers were comfortable letting AI make the actual purchase decision for them in lower-stakes categories, while roughly 28 to 31% were happy to have AI narrow down the choices (Gartner).
Shoppers already know exactly where they want the help to stop, and the brands still treating that as an open question are the ones most likely to build an assistant nobody actually wants to use.
Why does a shopping assistant bolted onto a support chatbot often fall short?
A support bot’s primary job is to deflect and resolve. Selling is a different job description, and when the two run as separate systems, neither one is actually tracking the same conversation, so the shopper is the only party in the exchange who remembers what's already been said. That’s a recipe for repetition, and when it comes to customer service, excessive repetition is the fastest way to turn a shopper into an ex-shopper.
Not to mention, if you add a separate search tool and a separate recommendation widget on top, now you’re wrangling three systems syncing against the same catalog, each with its own dashboard. It all becomes a rather expensive game of telephone.
A great in-store associate carries a kind of working knowledge that never makes it onto a product page, the fit quirks, the return patterns, the questions customers actually ask once they're holding the thing in their hands. A bot stitched onto an existing chat tool never picks any of that up either, because it was never built to.
Seel calls this the unstructured-context problem, and no model solves it on its own. It only gets solved by a system built to actually go find that knowledge and keep finding it as it changes, which is the real test of whether an assistant is worth trusting with the conversation at all.
For instance, Seel’s Support Agency creates a daily feedback loop for teams working with AI support agents through the AI Manager feature. The system doesn’t wait for someone to notice a problem and rewrite a prompt. Instead, it reviews its own conversations every day, surfaces the cases it got wrong, and proposes rule updates to fix them.
The merchant’s team can teach it the way they’d coach a new hire, through chat, voice, or working a ticket alongside it. A CX lead approves every change before it goes live, so the agent keeps learning without the brand losing control of what it says.
Before adding an AI shopping assistant, it’s worth asking a provider four blunt questions:
Does it work off live catalog data or a static export?
Can a shopper actually complete a purchase without leaving the chat?
Does it know when to hand off to a person instead of guessing?
Who’s responsible when it gets an answer wrong?
How do you keep AI product answers accurate?
An assistant can only be as accurate as what's feeding it, and these three habits are where that accuracy is protected:
Live catalog data, not a static export that goes stale the day inventory shifts.
A closed loop. Every question the assistant couldn’t answer becomes a content backlog item, not a dead end, which means you need a tool that gives you a real audit trail of what shoppers actually asked.
A guardrail that lets the assistant say “I’m not sure” and hand off to a person, rather than guess confidently and be wrong.
Of course, the burning question here is who keeps this all current? Decide up front whether that’s an internal owner on your team or the vendor. A provider that builds to your specific catalog and team structure saves you ongoing hours spent prompting and re-tuning a model that only you understand.
A tool hands you a dashboard and a prompt window. But an AI agency hands you an answer that's still right next quarter.
One AI shopping assistant, built all the way through
Seel didn't arrive at any of this from a clinical product roadmap, but rather from years of being the one on the hook when a shopper's question went unanswered. This tends to sharpen your opinion about what “helpful” actually means.
Seel’s Sales Agency answers product questions and recommends from live catalog data across both pre- and post-purchase touchpoints, and a shopper can complete the purchase directly inside the chat.
When GravaStar, the sci-fi-inspired gaming-gear brand, put Seel's Support Agent to work across live chat and email, the goal was for it to independently resolve 70% of conversations. It came in at 79%, and that’s without intervention from the support team. Those same conversations also did real selling along the way: in-chat product recommendations converted at a 20 percent rate, with an average order value near $200. Shoppers with buying questions don't wait around, which is just as true of a support conversation as it is of a sales one.
If you’re weighing whether this belongs on your own site, talk to sales and we’ll walk through where the unanswered questions are actually costing you.
Frequently asked questions about AI shopping assistants
Does an AI shopping assistant actually increase conversion rate? When it’s grounded in accurate, live catalog data and focused on answering real pre-purchase questions, yes, the mechanism is straightforward: it resolves the uncertainty that would have otherwise ended in abandonment or a return.
How can AI chat increase add-to-cart rate? By closing the gap between a shopper’s specific question and a specific answer in the moment they’re deciding, rather than losing them to a support queue or a generic spec sheet.
How do I reduce vendor sprawl across search, chat, and recommendation apps? Look for one system that handles discovery, comparison, and post-purchase questions together against the same live catalog, rather than three separate tools each syncing their own copy of it.
How can AI shopping assistants help shoppers find the right size online?
By cross-referencing what a shopper says about their own fit (their usual size in another brand, their measurements) against that specific brand's known fit patterns, rather than relying on one generic size chart, and flagging the gap directly when the two don't line up.
