One Question Every Retailer Should Ask Before Launching AI Agents

Retailers today are under significant pressure, with 71 percent of consumers expecting personalized interactions. To fulfill this expectation, many retailers are jumping into artificial intelligence agent solutions. And it’s not surprising. AI agents promise efficiency, personalization, and the ability to act like independent team members. In fact, 75 percent of retailers believe AI agents are essential to compete.

Yet research shows that AI agents fail to complete their tasks 70 percent of the time, suggesting that incorporating cost-effective and high-value AI agents isn’t so simple. The real question for every retailer is whether the investment addresses a genuine business challenge or is it just another shiny tool in search of a problem.

AI is Only as Strong as its Foundation

AI’s capabilities depend on the quality and structure of the data, algorithms and design it’s built on. It sits under the “mothership” of analytics, which spans from simple equations to deep learning. For retailers, this isn’t just a technical detail; it’s an operational reality. Clean customer data, accurate inventory records, and reliable analytics pipelines are what make AI agents useful at scale.

The mistake many retailers make is starting with the most advanced tool instead of the right one. Expensive and often “flashy” solutions like AI agents may sound essential, especially when tech companies promote them as the next competitive necessity, but without a solid data and process backbone, those tools rarely deliver. It’s not about whether you have an AI agent; it’s about whether the agent is solving a real business challenge.

Get the groundwork right and the payoff is meaningful efficiency, personalization, and a competitive edge. Skip it and the result is wasted investment and disappointed customers.

Ways to make AI agents operationally actionable include:

• Audit your data first. Before deploying AI, make sure your customer, sales, and inventory data are complete and accurate. A small percentage of errors can multiply across automated decisions.
• Start with high-impact use cases. Pick areas where AI can create measurable return on investment quickly, like demand forecasting or personalized promotions.
• Align technology to operational goals. Ask, “Will this tool actually solve a business problem?” If not, don’t proceed.
• Define metrics up front. Track performance with key performance indicators relevant to retail operations (e.g., conversion lift, stockouts avoided, and customer retention) so you know if AI is working.

Why Humans Still Matter

AI agents can crunch numbers faster than any team — tracking sales patterns, predicting demand, and recommending customized products to customers. That’s extremely valuable. However, the harder work in retail isn’t just efficiency; it’s deciding what makes your brand different from its closest competitors.

This is where the Heisenberg principle of uncertainty applies: data can get you close, but it can’t pin everything down. An AI agent can tell you which sweater is selling best, but it can’t decide the story you tell around it or how you build an experience that keeps customers coming back. Merchandising, brand positioning, and knowing when to take a creative risk are still human calls.

This is where retailers often stumble. They expect AI to be the differentiator, when in reality it's just the amplifier. The strategy — the “why this matters to our customer” — still comes from people. Pair the two and you get scale plus originality. Rely on AI alone and you risk looking like everyone else.

Before retailers leap headfirst into leveraging AI agents, they need to stop and think. They must define whether the solution solves their business problems. And if so, lauded retailers will apply it smartly, leveraging AI for scale and humans for innovation and strategy.

Guillermo Delgado Aparicio is the global AI leader at Nisum, a technology consulting partner.