What Retailers Really Want From AI in 2026

The era of AI curiosity is officially over. Retailers have spent the last 18 months running pilots, attending demos, and sitting through endless vendor pitches. Now they want answers to a much harder question: what AI actually moves the needle?

A recent survey of more than 100 brands and retailers ahead of Shoptalk 2026 made one thing crystal clear - AI is the number one priority, but the patience for hype has run out. Retailers want proof, not promises. And that distinction changes everything about how AI tools need to show up for ecommerce businesses right now.

The Shiny Object Phase Is Over

For the past few years, AI in retail was treated like a science fair project. Try it, talk about it, put out a press release. That cycle is done. The retailers showing up to industry events today are asking sharper questions: What is the actual ROI? How long until it's live? What does quality output look like at scale?

This is healthy. The AI tools that genuinely solve real problems - reducing friction, answering customer questions, driving conversions - are going to separate themselves from the tools that exist purely to check a box on a technology roadmap.

For store owners, the message is simple: don't adopt AI because it sounds modern. Adopt it because it solves something specific. And right now, the most specific, high-value problem most ecommerce stores face is converting the traffic they already have.

The Four Things Retailers Actually Want AI to Do

1. Make Data Work Harder

Retailers are sitting on mountains of customer data and getting relatively little out of it. AI's promise here is real - unlocking behavioral patterns, predicting intent, and delivering personalized shopping experiences that feel genuinely relevant rather than algorithmically random.

The stores winning with this approach aren't just showing "customers also bought" widgets. They're using AI to understand where a shopper is in their journey and serving the right product, the right message, and the right nudge at exactly the right moment.

2. Automate the Manual Work

Customer support is the obvious starting point. According to Tidio, 88% of online shoppers had at least one conversation with a chatbot in 2022 - but the key word is "conversation." Retailers are clear that they don't just want automation for automation's sake. They want automation that actually serves customers well.

This is where the difference between a basic chatbot and a genuine AI shopping assistant matters enormously. A bot that deflects questions is annoying. An assistant that answers them accurately - and then guides a shopper toward a purchase - is a revenue driver.

3. Optimize the On-Site Experience

Site search, product discovery, and on-page personalization are all on retailers' radar. Ecommerce conversion optimization isn't a new priority, but AI is changing what's possible. Rather than static rules - "if a user visits X, show Y" - agentic AI can adapt in real time based on individual behavior, inventory changes, and buying intent signals.

The retailers most excited about this aren't just looking to automate existing processes. They want AI that gets smarter over time, not just faster. That distinction - intelligence versus speed - is crucial.

4. Drive Real Personalization at Scale

Personalization has been a buzzword for a decade, but most stores are still delivering a fairly generic experience. AI-powered product recommendations done well can change that - but only when they're built on quality data and trained to understand context, not just clicks.

A shopper browsing winter coats at 11pm in December has very different intent than someone casually exploring the same category in July. AI that recognizes those signals and responds accordingly is what retailers are hunting for - and what their customers have quietly started expecting. As we've explored in our post on what customers now expect from AI, the bar is rising fast.

The Honest Problem Most Stores Are Ignoring

Here's the tension that doesn't get discussed enough: most ecommerce stores have legacy tech stacks that weren't built for AI. Old product data, inconsistent tagging, siloed customer information - all of it creates friction for any AI tool trying to deliver meaningful results.

This isn't a reason to avoid AI. It's a reason to start with tools that work with what you have, rather than requiring a complete infrastructure overhaul before they can function. The best AI tools for ecommerce right now are the ones that integrate cleanly, learn quickly from existing data, and start delivering value in days - not quarters.

The retailers who stall because they're waiting for perfect data conditions will still be waiting when their competitors have already closed the gap. The warning signs that you need AI now are often already visible in your store analytics - you just need to know where to look.

What "Good AI" Actually Looks Like for Ecommerce

Based on everything retailers are asking for, the profile of genuinely useful ecommerce AI looks like this:

That's not an aspirational feature list. That's the baseline that serious retailers are evaluating vendors against right now. Anything less than this isn't worth the implementation cost.

Where LISA Fits Into This Picture

LISA was built precisely for this moment - when retailers have moved past curiosity and need AI that delivers measurable results without a six-month implementation project.

As an AI shopping assistant for ecommerce, LISA works directly on your store to answer customer questions in real time, deliver intelligent product recommendations, and guide shoppers from browsing to buying. It's not a chatbot with a script. It's a conversational commerce tool that understands your catalog, learns from your shoppers, and gets better over time.

For store owners who are tired of watching traffic arrive and leave without converting, LISA addresses the exact problems that retailers identified as their top priorities - personalization, automation, site optimization, and customer experience - without requiring you to rebuild your entire tech stack first.

The retailers who come out ahead in the next two years won't be the ones who adopted AI earliest. They'll be the ones who adopted it smartest - choosing tools that solve real problems and deliver real returns. That's the standard LISA is built to meet.

This article was inspired by Jon Sherry.

Ready to see what AI actually looks like when it works? Try LISA free on your ecommerce store and find out why retailers are moving from AI experiments to AI that earns its place in the tech stack.