Retailers have big questions about AI

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Retail is at a turning point. 

AI is no longer a futuristic idea or marketing buzzword—it’s a business necessity. Consumers expect intelligent, seamless, and personalized experiences at every touchpoint. The brands that deliver on those expectations will win. Those that don’t will fall behind. 

Still, when I talk with retail leaders, I hear the same concerns again and again: 

• How do we make AI feel natural, not robotic? 

• Can it really drive sales—or is it just a cost-cutting tool? 

• How do we integrate AI without blowing up our current operations? 

• And beyond the contact center, where else can AI have real impact?

These aren’t just passing questions. They’re real blockers, slowing down progress. That’s why we launched an AI Lab webinar series, and write articles like this to get information out publicly with practical, business-first answers. 

AI needs to do more than automate 

Retailers have dipped their toes into AI—automated chatbots, product recommendations, predictive analytics—but too often, these tools operate in silos. That leads to clunky experiences and limited impact. 

The mindset is shifting. It’s no longer just about efficiency. It’s about impact. AI shouldn’t only reduce costs. It should increase engagement, drive revenue, and build customer loyalty. 

Here are three principles we’ve seen drive real success: 

1. AI should sell, not just support 

Traditionally, retail AI has played defense—handling order tracking, return policies, and FAQs. But it’s time to put AI on offense. 

Think of guided selling: AI that acts like a smart associate, asking about customer preferences, budget, or style—and responding naturally. It’s the digital equivalent of a great in-store experience. 

One example: A luxury jewelry brand used conversational AI to recommend add-ons and upgrades based on a customer’s past purchases. The result? A 30% boost in upsells—with zero human agent involvement. 

The takeaway: AI can drive conversions and revenue. It just needs to be designed with that goal in mind. 

2. Proactive > reactive 

Most AI waits for customers to initiate the conversation. That’s a missed opportunity. 

Take cart abandonment. Nearly 70% of online carts are abandoned before checkout. AI can spot hesitation—lingering on the checkout page, revisiting items—and respond in real time with: 

• A one-click checkout to reduce friction 
   

• A last-minute incentive 
   

• A helpful AI assistant offering answers 
   

AI shouldn’t just respond when customers get stuck. It should help them move forward. 

3. AI that works with people, not instead of them 

The most successful retailers don’t replace humans—they empower them. 

Think about frontline staff. AI can handle the repetitive stuff so humans can focus on high-value interactions: complex purchases, emotional moments, loyalty-building conversations. 

It also works the other way. Human agents generate valuable data—about buying habits, objections, preferences—that AI can learn from and use to personalize future experiences. 

That’s the real win: a human-AI partnership that gets smarter over time and drives better outcomes across the customer lifecycle. 

Rethink the AI roadmap 

Too often, brands start with customer support because it feels “safe.” But forward-thinking leaders are broadening their lens—and seeing greater return. 

We’re working with retailers that are embedding AI into every stage of the customer journey: 

• Pre-purchase: Digital consultations, guided product discovery, preference-based recommendations 

• In-purchase: Smart upsell suggestions, checkout support, frictionless payments 

• Post-purchase: Delivery updates, service requests, loyalty rewards, re-engagement 

And here’s the kicker: these touchpoints don’t need to be siloed. The right AI platform can stitch them together into a seamless, personalized journey. 

What makes the difference 

Three things separate retailers who are winning with AI from those still spinning their wheels: 

• Start with the customer, not the tech. Don’t ask, “What can this tool do?” Ask, “Where is the customer getting stuck—and how can we help them move forward?” 

• Design for outcomes. If your AI project doesn’t tie back to a business metric—conversion, lifetime value, customer satisfaction (CSAT)—you’re flying blind. 

• Make it measurable. Set clear goals. Track impact. Optimize based on results. This isn’t about proving AI works in general—it’s about proving it works for your brand. 

Final thought: Innovation without disruption 

AI doesn’t need to blow up your tech stack. It should integrate with your existing systems, layer in intelligence, and get smarter over time. 

We call it “innovation without disruption.” You don’t have to rip and replace. You just have to start with the right mindset—and the right partner. 

AI in retail isn’t just about answering questions. It’s about asking the right ones—and making sure your tech stack is ready to answer them in ways that actually move the business forward. 

John Sabino is CEO of LivePerson. 

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