The 2 AI Mistakes Costing Retailers Customer Trust

Retail doesn't get a slow season anymore. E-commerce keeps compressing the calendar, driving nonstop demand across channels, and the pressure to keep up is relentless.

In response, retailers are turning to artificial intelligence. Gartner found that 91 percent of service leaders are under pressure to implement it to improve both efficiency and customer satisfaction. However, a recent Qualtrics survey finds that nearly one in five consumers saw no benefit from using AI for customer service.

Something is missing the mark, but the problem isn’t the AI itself.

Retailers are successfully using automation to lighten workloads, accelerate resolution, and scale support. Yet many struggle with implementation, tackling the wrong problems or missing the moment when human intervention is what a customer actually needs.

1. You May Be Starting With the Wrong Problem

There are plenty of problems AI can solve for retailers. The harder task is knowing which to solve first.

AI works best on tasks that are repeatable and high volume. Every day, contact centers field questions about order statuses. Because the workflow for this question is both consistent and frequent, it's ideal for automating. When automated, the customer receives a faster response than a human could provide, and the employee workload shrinks in a meaningful way.

However, when retailers skip this foundation of tier-one problems and automate the more complex problems too soon, they risk losing customer trust.

A frustrated customer with a return dispute or a nuanced question doesn’t want to get stuck in an automation loop. At that moment, they need someone with the full context who can read the room. But when brands have instead deployed automation too quickly or carelessly, they unintentionally disrupt the experience, abandoning customers with AI when they may need human support.

This mistake is costly. Capacity's Closure Index found that just one unresolved experience is enough for 33 percent of customers to stop using a brand altogether. By implementing AI out of order or doing too much too fast, brands could drive customers away.

2. When AI Has to Step Aside

Gartner reports that 64 percent of customers prefer companies didn't use AI in their customer service at all, and most point to the same concern: the more AI, the harder it is to reach a person when needed.

That concern is worth taking seriously, but it doesn't tell the whole story.

Customers aren't anti-AI. According to one study, over half actually prefer using AI agents when they want immediate help. What they’re firm on is having a human available when the interaction gets complicated and requires nuance. According to the Closure Index, 85 percent of respondents say seamless AI-to-human escalation is important to reach true resolution.

When brands miss this moment and fail to hand off to a human or give agents full context, customers are left repeating themselves, arguing their case, or disengaging. In a tight competitive retail environment, a fumbled handoff can end the relationship.

Finding the Right Balance Between AI and Human Support

Retailers seeing strong results solve both mistakes at once. They've identified the right problems to automate, built escalation paths, and designed the handoff moment as a core feature of the experience.

Brands like Pacsun and DSW have found this balance. Since deploying AI agents, 85 percent of Pacsun's customer inquiries are now handled by virtual agents. And with AI support, DSW saved $1.5 million in annual support costs, reduced handle time by 19 percent, and improved CSAT by 30 percent.

Neither retailer got those results from implementing AI everywhere, all at once. Instead, they solved the right problems, in the right order, making sure a human was always within reach when needed.

David Karandish is founder and CEO of Capacity, an enterprise SaaS company headquartered in St. Louis, MO. Capacity is a support automation platform that uses AI to deflect emails, calls, and tickets so internal and external support teams can spend more time doing their best work.