If you're running a retail operation with hundreds of stores, you're already using AI whether you know it or not. Stock alerts, customer review engines, price recommendation tools - that's AI in its early form.
It's a new class of operations.
This past week, I was a guest at Manhattan Associates' Momentum conference in Las Vegas. Over the next several weeks, I will report on what I learned.
Jeff Beadle, Senior Director, gave one of the most fascinating sessions on Agentic AI. He laid out a path of the evolution of intelligent automation, and to help explain it to you, I am going to use an unlikely training manual: The Terminator.
Yeah, that Terminator.
Leather jacket, shotgun, "I'll be back," Austrian accent, you know the guy.
Before he chases anyone, what does he do? He walks into a phone booth, pulls out a paper phone book, and looks up Sarah Connor. Why?
Because his programming didn’t include updated data streams. He’s cutting-edge hardware, sure, but limited to whatever his creators gave him. Sound familiar?
To help you understand where your operation sits on this spectrum and where it's headed, here's how AI evolves through five distinct stages.
Stage 1: Basic Automation - The Robotic Leg
In the earliest stages, automation isn’t smart. It’s mechanical. Think of the Terminator’s leg. It lifts, it steps, it balances - but only because it was told how to. There's no decision-making, no adaptation. Just a motor responding to commands.
In retail, this is a conveyor belt in the distribution center, your light sensor that turns on when someone walks into a fitting room, or a system that prints out inventory reports nightly at 2 a.m.
In all those cases, the automation performs just one action. It does it well, but it can’t think.
Next, let’s say the Terminator is programmed to shoot when it sees a specific target. Now we’ve added sensors, maybe a bit of logic. If this, then do that.
In your store, that might be an auto-reorder that kicks in when SKU velocity drops below a threshold. Or software that flags a shrinkage pattern in a certain location. It’s smarter than pure automation, but it’s still bounded. No planning. No goals. Just a better trigger.
This is also where tools like Sidekick Rex, our AI roleplay coach inside SalesRX+, live. Rex gives structured feedback based on what an associate says—or forgets to say—during a roleplay. It’s fast, targeted, and helpful. But it’s not adapting on its own or learning context. Rex won’t plan your sales strategy. He sharpens what’s already been trained. Rex is still a rules-based engine, not a free thinker. Just a very efficient one.
Now the Terminator talks. It analyzes. It says, “Sarah Connor lives at this address” and asks for confirmation before heading out.
This is the stage where AI helps humans do more and faster. Based on sales or weather, it might suggest what items to move to the endcap. Or recommend better fulfillment routes during a spike in demand. It’s not in charge, but it’s a heck of a sidekick.
It’s where a lot of AI tools live today: helping, advising, but not deciding. Still a human in the loop to authorize.
Now we’re getting interesting.
In Terminator 2, our leather-jacketed friend isn’t just reacting. He’s setting goals: protect the kid, eliminate threats, adapt to changing circumstances. He learns. He uses tools. He chooses routes.
This is Agentic AI: You assign a mission, and it figures out how to get there.
In a retail setting, this is the AI you tell, “Ensure 95 percent on-shelf availability across stores this quarter.” It takes in POS data, talks to your ERP, works with supplier schedules, and starts making decisions. Not just reacting but acting.
It can look at tens of reports a human could never do, much less in real time.
It may reroute deliveries. It might throttle promotions in low-stock regions. It learns from performance and adjusts. You’re not giving it every step- you’re giving it the outcome.
Now, imagine our Terminator realizes the mission is bigger than one machine. He needs a drone for aerial recon, a hacker to infiltrate a database, and a decoy to draw fire.
He assembles a team—each agent with a specialty, working in sync, all pursuing the same objective.
In AI, this is the future we’re racing toward. One agent optimizes staffing schedules based on foot traffic and weather. Another predicts shipping delays and reroutes containers. Another adjusts pricing dynamically by region and competition.
Each is autonomous, but they coordinate. You give the mission: “Increase margin by 5 percent without lowering sell-through.” The agents figure out how. That’s where we’re going.
Because you already get it.
You know the movie. You understand the stakes. And you instinctively get how much more powerful a system becomes when it can plan, learn, and collaborate.
That’s the shift happening in retail—but most people are still stuck at Stage 2, tinkering with tools that trigger on "if-then" logic and calling it AI.
That’s not intelligence. That’s automation.
Let’s walk the same ladder, but this time using retail examples you’ve seen, deployed, or are actively trying to scale.
This is the warehouse belt system or the automatic door sensor.
Or the nightly batch job that runs your SKU report and emails it to the ops manager by morning. It saves time. It does a repetitive job. But it’s blind to context.
Here’s where AI starts nudging in. Maybe your system flags stores with low turns. Or recommends reorders based on velocity.
It’s useful, but static. It doesn’t understand why demand is up, or that weather delayed the shipment, or that markdowns in one region caused the spike in another. It’s still just reacting to what already happened.
Now we’re at dashboards that offer insights: “This store is underperforming in denim. This product is over-indexing with Gen Z in urban centers.”
Your team still has to make the call, but they’re better informed. It’s not decision-making AI, it’s suggestion AI.
This is where most enterprise retail AI tools live today. And we still will need those same tools.
Now it gets fun.
Imagine you tell your system: “Optimize our back-to-school strategy across all regions.” The AI doesn’t just spit out reports. It builds regional forecasts, adjusts POs, recommends staff allocations, and even runs local A/B tests on digital signage.
All while feeding results back into the loop so it learns.
Agentic AI doesn’t just advise. It does it within the confines you dictate.
These agents don’t just work for you. They work with each other- like a team to become a mission-driven system.
The result: faster decisions, fewer errors, better margins, and most importantly, scalability across hundreds or thousands of stores without drowning in micro-decisions.
This is for the operators with scale. With complexity. With a tech stack that resembles a Frankenstein monster stitched together over a decade of bolt-on solutions.
You don’t need another dashboard.
You need AI that works like a team of experienced employees, not interns who can only follow instructions.
Let's recap...
The Takeaway: Don't Get Left Behind
The gap is widening. Some retailers are building rule-based automations and calling them strategy.
Others are training agentic systems to manage entire verticals autonomously.
You don't need more tools. You need a system that thinks.
The question isn't whether AI is coming.
Which version are you running: the robotic leg or the coordinated team already solving problems you haven't seen yet?