{"id":15707,"date":"2025-08-05T06:49:34","date_gmt":"2025-08-05T06:49:34","guid":{"rendered":"https:\/\/dmsretail.com\/RetailNews\/your-crash-course-on-the-future-of-ai-in-retail-2\/"},"modified":"2025-08-05T06:49:34","modified_gmt":"2025-08-05T06:49:34","slug":"your-crash-course-on-the-future-of-ai-in-retail-2","status":"publish","type":"post","link":"https:\/\/dmsretail.com\/RetailNews\/your-crash-course-on-the-future-of-ai-in-retail-2\/","title":{"rendered":"Your Crash Course on the Future of AI in Retail"},"content":{"rendered":"

\"Retail<\/a><\/p>
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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.<\/p>\n

It’s a new class of operations.<\/p>\n

This past week, I was a guest at Manhattan Associates’ Momentum\u00a0conference in Las Vegas. Over the next several weeks, I will report on what I learned.<\/p>\n

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\u00a0an unlikely training manual: The Terminator<\/em>.<\/p>\n

Yeah, that<\/em> Terminator.<\/p>\n

Leather jacket, shotgun, “I’ll be back,” Austrian accent, you know the guy.<\/p>\n

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?<\/p>\n

Because his programming didn\u2019t include updated data streams. He\u2019s cutting-edge hardware, sure, but limited to whatever his creators gave him. Sound familiar?<\/p>\n

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.<\/p>\n

The Five Stages of AI Evolution in Retail:<\/strong><\/p>\n

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  1. Basic Automation<\/strong> – Simple mechanical tasks like conveyor belts and scheduled reports that follow preset commands without decision-making capability.<\/li>\n
  2. AI Automation<\/strong> – Rule-based systems that trigger actions based on thresholds, such as auto-reorders when inventory drops or shrinkage alerts in specific locations.<\/li>\n
  3. AI Assistance<\/strong> – Advisory tools that analyze data and provide recommendations to human decision-makers, like suggesting endcap products based on sales patterns or weather.<\/li>\n
  4. Agentic AI<\/strong> – Self-directed systems that pursue assigned missions autonomously, such as maintaining 95% on-shelf availability by coordinating POS data, ERP systems, and supplier schedules.<\/li>\n
  5. Multi-Agent Systems<\/strong> – Coordinated teams of specialized AI agents working together toward shared objectives, with each handling different aspects like staffing, pricing, and fulfillment optimization.<\/li>\n<\/ol>\n

    Stage 1: Basic Automation – The Robotic Leg<\/span><\/p>\n

    In the earliest stages, automation isn\u2019t smart. It\u2019s mechanical. Think of the Terminator\u2019s 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.<\/p>\n

    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.<\/p>\n

    In all those cases, the automation performs just one action. It does it well, but it can\u2019t think.<\/p>\n

    Stage 2: AI Automation – The Reflexive Trigger<\/span><\/h2>\n

    Next, let\u2019s say the Terminator is programmed to shoot when it sees a specific target. Now we\u2019ve added sensors, maybe a bit of logic. If this<\/em>, then do that<\/em>.<\/p>\n

    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\u2019s smarter than pure automation, but it\u2019s still bounded. No planning. No goals. Just a better trigger.<\/p>\n

    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\u2014or forgets to say\u2014during a roleplay. It\u2019s fast, targeted, and helpful. But it\u2019s not adapting on its own or learning context. Rex won\u2019t plan your sales strategy. He sharpens what\u2019s already been trained. Rex is still a rules-based engine, not a free thinker. Just a very efficient one.<\/p>\n

    Stage 3: AI Assistance – The Tactical Sidekick<\/span><\/h2>\n

    Now the Terminator talks. It analyzes. It says, \u201cSarah Connor lives at this address\u201d and asks for confirmation before heading out.<\/p>\n

    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\u2019s not in charge, but it\u2019s a heck of a sidekick.<\/p>\n

    It\u2019s where a lot of AI tools live today: helping, advising, but not deciding. Still a human in the loop to authorize.<\/p>\n

    Stage 4: Agentic AI – The Self-Directed Terminator<\/span><\/h2>\n

    Now we\u2019re getting interesting.<\/p>\n

    In Terminator 2<\/em>, our leather-jacketed friend isn\u2019t just reacting. He\u2019s setting goals: protect the kid, eliminate threats, adapt to changing circumstances. He learns. He uses tools. He chooses routes.<\/p>\n

    This is Agentic AI: You assign a mission, and it figures out how to get there.<\/p>\n

    In a retail setting, this is the AI you tell, \u201cEnsure 95 percent on-shelf availability across stores this quarter.\u201d It takes in POS data, talks to your ERP, works with supplier schedules, and starts making decisions. Not just reacting<\/em> but acting.<\/em><\/p>\n

    It can look at tens of reports a human could never do, much less in real time.<\/p>\n

    It may reroute deliveries. It might throttle promotions in low-stock regions. It learns from performance and adjusts. You\u2019re not giving it every step- you\u2019re\u00a0giving it the outcome.<\/p>\n

    Stage 5: Multi-Agent Systems – The Mission-Based Team<\/span><\/h2>\n

    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.<\/p>\n

    He assembles a team\u2014each agent with a specialty, working in sync, all pursuing the same objective.<\/p>\n

    In AI, this is the future we\u2019re 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.<\/p>\n

    Each is autonomous, but they coordinate. You give the mission: \u201cIncrease margin by 5 percent without lowering sell-through.\u201d The agents figure out how. That\u2019s where we\u2019re going.<\/p>\n

    Why I Used the Terminator Analogy<\/span><\/h2>\n

    Because you already get<\/em> it.<\/p>\n

    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.<\/p>\n

    That\u2019s the shift happening in retail\u2014but most people are still stuck at Stage 2, tinkering with tools that trigger on “if-then” logic and calling it AI.<\/p>\n

    That\u2019s not intelligence. That\u2019s automation.<\/p>\n

    The Journey From Conveyor Belts to Coordinated Retail Intelligence<\/span><\/h2>\n

    Let\u2019s walk the same ladder, but this time using retail examples you\u2019ve seen, deployed, or are actively trying to scale.<\/p>\n

    Stage 1: Basic Automation – Conveyor Belts and Printouts<\/span><\/h2>\n

    This is the warehouse belt system or the automatic door sensor.<\/p>\n

    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\u2019s blind to context.<\/p>\n

    Stage 2: AI Automation – Rule-Based Efficiency<\/span><\/h2>\n

    Here\u2019s where AI starts nudging in. Maybe your system flags stores with low turns. Or recommends reorders based on velocity.<\/p>\n

    It\u2019s useful, but static. It doesn\u2019t understand why demand is up, or that weather delayed the shipment, or that markdowns in one region caused the spike in another. It\u2019s still just reacting to what already happened.<\/p>\n

    Stage 3: AI Assistance – Helping, Not Leading<\/span><\/h2>\n

    Now we\u2019re at dashboards that offer insights: \u201cThis store is underperforming in denim. This product is over-indexing with Gen Z in urban centers.\u201d<\/p>\n

    Your team still has to make the call, but they\u2019re better informed. It\u2019s not decision-making AI, it\u2019s suggestion AI.<\/p>\n

    This is where most enterprise retail AI tools live today. And we still will need those same tools.<\/p>\n

    Stage 4: Agentic AI – Self-Directed Optimization<\/span><\/h2>\n

    Now it gets fun.<\/p>\n

    Imagine you tell your system: \u201cOptimize our back-to-school strategy across all regions.\u201d The AI doesn\u2019t just spit out reports. It builds regional forecasts, adjusts POs, recommends staff allocations, and even runs local A\/B tests on digital signage.<\/p>\n

    All while feeding results back into the loop so it learns.<\/p>\n

    Agentic AI doesn\u2019t just advise. It does it <\/em>within the confines you dictate.<\/p>\n

    Stage 5: Multi-Agent Unification\u00a0– The Manhattan Moment<\/span><\/h2>\n

    This is where companies like Manhattan Associates are blazing trails.<\/p>\n

    You rely on one master Agentic AI that will deploy multiple AI agents to achieve your goal:<\/p>\n