Last month, I needed to find a lightweight, battery-operated lawnmower for my elderly neighbours. Something easy to manoeuvre, reliable, and available for collection near their home within the week.

What followed was a depressing tour of generic ecommerce experiences. Endless filtering through "corded vs cordeless," scrolling past petrol-powered machines they'd never be able to start, comparing weights buried in specification tables, and manually checking store availability for each model. Not one site asked the obvious questions: Who's using this? What's your situation? What actually matters to you?

Twenty minutes later, I described the same need to ChatGPT. It immediately grasped the context—elderly users need lightweight, easy-start options—and provided three specific recommendations with reasoning, weights, and suggestions for where to find them locally.

The contrast was embarrassing. An AI with no memory of my previous searches understood my parents' needs better than retailers who've been serving customers like them for decades.

UX has become lazy

This isn't an isolated failure. Somewhere along the way, we stopped asking "what does this user actually need?" and started asking "what colour should the button be?"

User experience design has been reduced to selecting from pattern libraries and copying successful templates. Teams now download Shopify themes, tweak the colours, and call it user-centred design. We've convinced ourselves that because ecommerce patterns are "established," we no longer need to understand our users.

The evidence is everywhere. Fashion retailers serve the same "new visitor" experience to someone buying their fifth black jumper. B2B suppliers make electricians navigate the same generic category pages as DIY enthusiasts. Luxury brands treat repeat customers identical to bargain hunters browsing for the first time.

We've traded user understanding for interface consistency, and called it progress.

The death of user understanding

This laziness didn't happen overnight. As UI patterns became commoditised, product teams pushed user research further into the background. "It's just ecommerce" became the rallying cry—as if understanding your customers was optional once you had a search bar and some filters.

As Jakob Nielsen observes, "UX practitioners today echo the complacency of their predecessors in the mid-90s. They seem content to tinker with their personas and the deluge of deliverables from our constantly expanding methodology toolbox while designing yesteryear's UX instead of focusing on next-generation UX."

Take any major retailer. A customer buying winter coats gets the same faceted search experience whether they're a new parent looking for something practical, a cyclist needing weather protection, or someone replacing a coat they've owned for years. The system knows nothing about context, nothing about intent, nothing about the person.

We justified this by calling it "scalable UX." In reality, we'd simply stopped listening.

The principles of interaction design—understand the user, identify their mental models, create systems that match how they think—became afterthoughts. Personalisation became a "nice to have" bolt-on feature, not fundamental to how we design experiences.

Most ecommerce sites make no meaningful effort to understand users beyond remembering whether they shop in men's or women's clothing. That's the extent of our personalisation ambition.

The cliff edge approaches

Here's the problem: users are about to remember what good service feels like.

AI agents are teaching people to expect systems that actually listen. When someone asks ChatGPT for help, it asks follow-up questions. It remembers context. It tries to understand what they actually need, not just what they typed.

These aren't abstract future possibilities. Right now, someone can ask an AI agent: "I'm an electrician. I need a consumer unit from Screwfix that's available for collection within 10 miles of SG13 8AZ tomorrow. It needs to be at least 10-way and IP2XC rated. Give me options and help me compare."

The agent delivers a complete report with recommendations, availability, pricing, and the option to place a click-and-collect order.

Compare that to the current Screwfix experience: search "consumer units," scroll through generic results, manually filter by IP rating (it's the 10th filter in a long list), check boxes for 10, 11, 12, 13, 14 ways, then click each product individually to check store availability.

The difference isn't just efficiency—it's that one system tries to understand what you need, while the other makes you navigate its database structure.

The reckoning

Users' expectations are shifting rapidly for the first time in years. Google results that seemed perfectly adequate suddenly feel "comically irrelevant and one-dimensional" compared to conversational search. The same shift is coming for ecommerce.

When AI agents can provide genuinely personalised shopping experiences—understanding context, remembering preferences, asking clarifying questions—your generic category pages will look embarrassingly tone-deaf.

This isn't about technology replacing humans. It's about technology finally doing what we should have been doing all along: listening to users and responding to their actual needs.

Know what game you're playing

The choice isn't simply "understand users or die." It's more nuanced: decide what kind of commerce you're in, then optimise accordingly.

Functional commerce: If you're selling solutions to problems—tools, supplies, utilities—accept that you're building for efficiency, not exploration. Users want to get things done and leave. AI agents will handle these transactions because they're fundamentally chores. Your job is to make your products and services easily accessible to these agents through APIs, structured data, and clear specifications.

Experiential commerce: If you're selling products that users enjoy choosing—fashion, lifestyle, luxury—then genuine user understanding becomes your only sustainable advantage. When users choose direct interaction over agent delegation, it's because they value the experience itself. Generic UX patterns won't compete with truly personalised, contextual design.

The Screwfix example reveals the deadly middle ground: they're clearly functional commerce—electricians aren't browsing for fun—but they've built consumer-facing UX that forces professionals to navigate irrelevant faceted search. They think they're in the "browsing" business when they're actually in the "procurement" business. That's why the AI agent easily outperforms their own website.

The battleground: Many purchases could swing either way. Brands with genuinely personalised, user-centric experiences might keep users engaged directly. Those with lazy, pattern-library UX will lose users to agents who understand their needs better than the brand's own website does.

The reckoning hits hardest in this middle ground—where brands pretend they're experiential while delivering commodity UX.

Beyond the grid

The solution isn't to panic-buy AI features or bolt chatbots onto existing experiences. It's to honestly assess what game you're playing, then design accordingly.

For functional commerce: Stop pretending users want to "discover" your industrial equipment or professional supplies. Build efficient, data-rich interfaces that agents can navigate easily. Invest in APIs, structured product information, and clear specifications. Make it trivial for an AI to understand your inventory, pricing, and availability.

For experiential commerce: This is where user research and genuine personalisation become essential. When users choose to engage directly rather than delegate to an agent, they're telling you the experience itself has value. Generic UX patterns won't compete with brands that genuinely understand what makes each customer tick.

Start asking the right questions again. What does this specific customer need right now? Are they here to get something done or to enjoy the process of choosing? How can we serve them better than a generic experience—or better than an AI agent could?

The brands that survive the next wave won't be those with the most sophisticated AI—they'll be those that understand which game they're playing and design brilliantly for it.

The grid was never the problem. Our willingness to accept it was.

The reckoning is coming. The question isn't whether AI will change customer expectations—it's whether you'll be ready when it does.