How Merchandisers Experience UX Differently When AI Is in the Loop
Most conversations about ecommerce UX focus on the shopper. Makes sense - they're the ones buying. But there's another user navigating these systems every day, and their experience gets almost no attention: the merchandiser.
Merchandisers are the operators. They're the ones deciding which products show up where, which promotions get priority, which rules apply to which categories. And for years, they've been stuck with tools that feel like they were designed by engineers who never had to use them.
That's changing. AI is reshaping ecommerce UX design not just for shoppers, but for the people running the show behind the scenes. The question isn't whether AI will change how merchandisers work. It's whether the UX will keep up. Let's call it ecommerce merchandising UX. But we're open to suggestions.
In This Article
Traditional Merchandising Tool UX
Here's what a typical merchandising workflow looks like without AI: spreadsheets, admin panels with dozens of tabs, and manual rule-building that requires a PhD in Boolean logic. You want to boost a product for a seasonal campaign? That's a 20+ minute task spread across 18 different screens. You want to see how your changes affect the sort order? Good luck - you'll need to publish, wait, then check the frontend manually.
This isn't an exaggeration. Talk to any merchandiser who's worked with legacy systems and they'll tell you the same thing. The tools work, technically. But they fight you every step of the way.
E-commerce UX best practices have historically focused on the customer-facing side: faster load times, cleaner PDPs, better filtering. Meanwhile, the people actually managing the catalog are stuck with merchandising workflows that look like they haven't been optimized since 2010.
The result is merchandisers spend more time wrestling with tools than actually merchandising. They become data-entry clerks instead of decision-makers. And when AI gets layered on top of these clunky systems, it just adds another layer of confusion.
Traditional vs. AI-Powered Merchandising UX
The difference between old-school merchandising software and AI-powered tools isn't just about automation. It's about who's doing the work. Human-in-the-loop vs. AI-in-the-loop.
In traditional systems, the merchandiser does everything. They set the rules. They monitor performance. They adjust the sort logic. They check the results. Every decision requires manual input, and the system just executes whatever it's told - nothing more.
"In traditional systems, the merchandiser does 100% of the thinking and 100% of the execution,” says Jeff Dearing, Head of User Experience at Syntheum. They decide what to update, how, where to apply rules, and then manually configure everything. The software just executes whatever it's told.”
AI-powered merchandising flips that relationship. Instead of waiting for instructions, the system surfaces recommendations. It simulates outcomes before you commit. It flags anomalies you might have missed. The merchandiser shifts from operator to editor - reviewing, refining, and overriding when needed, but not starting from scratch every time.
“Now the system does the initial analysis, surfaces recommendations, and simulates outcomes,” Dearing continued. "Instead of building every rule from the ground up, merchandisers react to what the AI proposes - accepting strong suggestions, tweaking marginal ones, and rejecting bad calls.". The work doesn't disappear. It just gets redistributed to whoever can do it better."
Here's a example. Say you're running a category page for winter jackets. In a traditional system, you'd manually set a boost rule for high-margin items, maybe suppress out-of-stock SKUs, and hope the sort looks right. In an AI-powered system, the tool might say: "Based on click-through and conversion data, these five jackets should rank higher. Here's a preview. Want to apply it?"
That's not removing control. That's giving the merchandiser a head start.
The UX implications are real. Traditional tools require you to know exactly what you want before you start. AI tools let you see options first, then decide. For merchandisers juggling dozens of categories and thousands of SKUs, that difference in workflow compounds fast.
How AI Reduces the Number of Screens Merchandisers Need
One of the biggest UX failures in traditional ecommerce systems is fragmentation. Data lives in one place. Rules live in another. Previews require switching to the frontend. Performance metrics sit in a separate analytics dashboard. To make one informed decision, merchandisers bounce between dozens of different screens.
AI doesn't fix this by itself, but smart UX paradigms combined with AI can.
Syntheum collapses this fragmented workflow into a single workspace. Real-time product previews sit next to KPI sliders. Drag-and-drop controls let you rearrange sort order while watching conversion estimates update on the fly. Want to prioritize margin over velocity? Slide a dial. The preview updates instantly.
This matters because every screen transition is a context switch. And context switches kill productivity. When a merchandiser has to remember "okay, I was looking at conversion rates, now I need to go check inventory levels, then come back and adjust the boost rule" - that's cognitive overhead that adds up across hundreds of daily decisions.
Good ecommerce UX design for merchandisers should feel like a cockpit, not a scavenger hunt. Everything you need to make a decision should be visible in one place. AI can power the recommendations, but the UX has to present them in a way that's actually usable. This is the human-first design philosophy at the core of everything we build.
The screen-reduction metric is worth tracking. If your merchandising team currently uses 8 tabs to do their job, and a new tool gets that down to 2, you've probably improved their UX. Not because fewer tabs is inherently better, but because fewer tabs usually means less friction between insight and action.
UX Principles for AI Tools Used by Non-Technical Teams
Here's where a lot of AI-powered merchandising tools fail: they're built by engineers for engineers. The interface assumes you understand how the model works, what the confidence scores mean, and why the system made a particular recommendation. That's fine if your merchandiser has a data science background.
E-commerce design best practices for AI tools should prioritize transparency without requiring technical literacy. That means showing the "what" and the "why" in plain language.
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Bad example: "Ranked by ML model v2.3 using weighted conversion probability."
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Better example: "Ranked by AI based on which products are most likely to sell this week."
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Even better: "This jacket is ranked #2 because it has the highest conversion rate among in-stock items in this price range."
Another principle: let humans override without penalty. One of the fastest ways to kill trust in an AI system is to make the user feel like they're "breaking" something when they disagree with a recommendation. Good UX for AI tools makes overrides easy, visible, and reversible. Pin a product to the top? Fine. The system notes the override, keeps learning, and doesn't punish you for having an opinion.
"Don't dump a wall of AI recommendations on the user," says Dearing. "Call out the highest-impact items first. Let users approve or dismiss them one at a time. When you present 50 recommendations you're creating decision paralysis. The merchandiser doesn't know where to start, what matters most, or what the cumulative effect will be. But sequentially… you turn complexity into actionable decisions. Each action builds confidence. Each approval or dismissal teaches the system."
Show your work incrementally. Surface the mission-critical suggestions first. Let users approve or dismiss them one at a time. Build trust through small wins, not overwhelming data.
Frequently Asked Questions
Why does merchandiser UX matter if customers never see it?
Because the tools your merchandisers use directly shape the catalog your customers browse. Slow, clunky admin interfaces lead to outdated product placements, missed promotional windows, and sloppy conversational shopping execution. When merchandisers can move fast and make confident decisions, the storefront stays sharp.
Can AI completely replace manual merchandising decisions?
No - and it shouldn't try to. AI is best used as a recommendation engine that surfaces options and predicts outcomes. The merchandiser still decides what's right for the brand, the campaign, or the customer segment. The goal is faster, better-informed decisions, not fully automated ones.
How do I know if my current merchandising tools have a UX problem?
Count the screens. If your team needs more than two or three tabs open to make a single merchandising decision, that's friction. Other warning signs: frequent copy-pasting between systems, reliance on tribal knowledge to find features, and merchandisers building workarounds in spreadsheets because the tool doesn't do what they need.
What's the biggest UX mistake in AI-powered merchandising tools?
Hiding the reasoning. When a system like Shopify or Salesforce ranks products or makes recommendations without explaining why, merchandisers lose trust. They either ignore the AI entirely or accept it blindly - neither outcome is good. Transparency builds confidence, and confidence drives adoption.
How long does it take for merchandisers to adapt to AI-assisted workflows?
Depends on the UX. If the tool is intuitive and explains itself clearly, most merchandisers get comfortable within a few weeks. If the interface is dense or the AI logic is opaque, adoption stalls. The learning curve is a UX problem, not a training problem.
What Good Operator UX Actually Looks Like
So what does this add up to in practice? Good UX for merchandisers in AI-powered systems comes down to a few things: fewer tabs, faster feedback loops, transparent logic, and easy overrides.
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Fewer tabs means collapsing fragmented workflows into unified workspaces. No more bouncing between analytics, rule-builders, and preview environments.
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Faster feedback loops means showing the impact of changes before they're published. Real-time previews, simulated outcomes, projected KPIs - all visible without leaving the editing screen.
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Transparent logic means explaining AI decisions in terms humans care about. Not "confidence scores" - sales, margin, inventory, trends.
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Easy overrides means letting merchandisers take control when they want to, without friction or fear of "breaking" the model.
When you design for merchandisers - not just shoppers - you build systems that people actually want to use. And in ecommerce, where operator efficiency directly impacts catalog quality, that UX investment pays off on the customer side too.
The tools your merchandisers use shape the experience your shoppers get. Bad operator UX leads to slow updates, missed opportunities, and sloppy execution. Good operator UX means your catalog stays sharp, your promotions launch on time, and your AI recommendations actually get reviewed instead of ignored.
UX for ecommerce isn't just about the storefront. It's about every interface in the system - including the ones customers never see.
Bottom Line
Good merchandiser UX comes down to this: fewer screens, faster feedback, and AI that explains itself. If your team can make a confident decision without opening five tabs or decoding cryptic model outputs, you're on the right track.
The payoff shows up everywhere - faster catalog updates, better use of AI recommendations, and a storefront that actually reflects what your merchandisers intended. Design for the operator's merchandising UX, and the customer experience follows.
About Syntheum.ai
We help e-commerce retailers implement agentic ecommerce merchandising solutions that go beyond basic automation. By integrating truly intelligent systems into merchandising strategies, we help businesses unlock their full potential - delivering efficiencies that improve operations and redefine what’s possible in online sales.
Empower Merchants with Ease and Intelligence
Syntheum is the Semantic Merchandising Platform for Agentic Commerce - powering onsite search, conversational shopping, and AI discovery through one merchandising brain your team controls.