Transforming Retail: What to Consider Before Deploying AI Agents

Imagine a customer support agent who never sleeps, learns faster than any employee, and can juggle dozens of complex tasks at once. Sounds like science fiction, right? It’s not. Welcome to the world of agentic artificial intelligence — a new frontier in retail innovation.

AI was a major theme at the NRF Big Show this year in New York. Many retailers shared their experiences about how they had started to use AI tools to drive business growth, while vendors were keen to show off new and innovative AI solutions. But unlike last year, when the new use cases were around generative AI, agentic AI was the buzz phrase in 2025.

Agentic AI is highly autonomous and able to handle complex tasks that require reasoning, problem-solving, and adapting to new situations. AI agents, on the other hand, are typically built to handle specific tasks. This article will explore some use cases of AI agents and what to consider before you deploy them into your business.

What is an AI Agent?

Think of an AI agent as a virtual team member, akin to a travel agent or personal assistant. You provide it with a task or set of tasks — e.g., booking a trip based on your preferences — and it handles the details, managing multiple sub-tasks along the way.

In a retail context, an AI agent could handle complex operations like optimizing order fulfillment, handling customer service inquiries, or personalizing the shopper’s journey based on real-time data. Unlike traditional automation, which excels at predefined workflows, AI agents are adaptive. They can handle ambiguity and exceptions, and make decisions in scenarios where deterministic workflows fall short.

The result? Faster resolutions, improved accuracy, and an elevated experience for both employees and customers. However, AI agents aren't a plug-and-play solution.

Related story: Technology That Helps Retailers Maintain Customer Trust During the Back-to-School Shopping Rush

How Should You Approach Using an AI Agent?

It’s more like onboarding a new hire. Just as you wouldn’t give a new employee full autonomy from day one, you shouldn’t expect an AI agent to perform flawlessly without guidance.

Here’s a high-level onboarding process:

1. Train the agent. Start by feeding it your brand’s guidelines, rules and priorities. Make sure it understands your goals and the nuances of your operations.
2. Observe and monitor. Keep a close eye on the AI agent’s performance during the initial stages. Identify gaps, provide feedback, and adjust its parameters as needed.
3. Iterate and improve. AI agents learn over time, but only if they’re given the right inputs. Treat them like evolving team members, continuously refining their capabilities to meet your needs.

By thinking of AI agents as new hires rather than tools, you’ll set the right expectations for how they can be used.

When Should You Use Deterministic Workflows vs. AI Agents?

While the promise of AI agents is exciting, they’re not always the right solution. Deterministic workflows — i.e., those based on pre-defined rules and logic — are often more cost effective for high-volume, repetitive tasks (e.g., automating order confirmation emails or managing inventory restocks).

However, when it comes to complex, dynamic processes, deterministic workflows can hit their limits. Consider scenarios like:

• Order Fulfillment Exceptions: An AI agent could decide how best to handle delayed shipments or split orders based on priority and service-level agreement requirements.
• Customer Support Escalations: Instead of following rigid escalation paths, an AI agent can adapt to real-time feedback and suggest resolutions tailored to the situation.

For these cases, agentic AI shines. Why? Because it can navigate ambiguity and achieve complex goals efficiently.

Employee-Facing Use Cases First

AI agent adoption is likely to start internally. With employee-facing use cases leading the charge. Here’s why:

1. Cost vs. Value: Rolling out customer-facing AI can be expensive and risky, especially if the technology isn’t yet mature. Therefore, best practice is to deploy AI agents to assist employees, such as call center staff, so they can resolve issues faster and with greater accuracy.
2. Early Adopters: Contact centers are often the first to embrace new technology, especially tools that can reduce call handling times. An AI agent could assist with onshore teams, providing a high return on investment even if it’s not yet financially viable for offshore centers.

Starting with employee-facing use cases allows brands to refine their AI agent strategies, creating a strong foundation for future customer-facing implementations.

Questions to Ask When Evaluating AI Agent Solutions

As you evaluate AI agent solutions for your business, ask the following questions to ensure the technology meets your needs:

1. How trustworthy are the outputs? Look for solutions that prioritize data accuracy, reliability and transparency. Can you trust the AI agent to make critical decisions?
2. Is it adaptive enough? The retail landscape changes quickly. Your AI agent must be able to learn and evolve in response to new challenges.
3. Is it proactive enough? Can the AI agent identify opportunities or risks before they become problems?
4. Is it achieving complex enough goals? For scenarios that go beyond simple tasks, ensure the AI agent can handle nuanced decision-making.
5. Is it autonomous enough? Strike the right balance between autonomy and control. You want the AI agent to perform independently but with mechanisms for oversight.

Additionally, look for opportunities to participate in agentic AI workshops hosted by vendors, which will give you firsthand experience with the technology and help identify its potential for your brand.

Agentic AI is an exciting new space, but preparation is key. Your next hire might not be human. Are you ready?

Nicola Kinsella is the chief strategy officer at Fluent Commerce, a cloud distributed order management system.