Key insights

✔ Why Retail Needs Agentic AI – Explore how AI agentic retail is stepping in to bridge the traditional retail systems gap, offering real-time, autonomous decision-making capabilities.

✔ Top Agentic AI Use Cases in Retail – Get into practical applications of AI agents in the retail sector, including customer experience enhancement, channel optimization, assortment planning, SKU management, and POS vendor selection.

✔ The Future of Retail with Agentic AI – Understand the transformative potential of agentic AI in retail, from maintaining continuous customer context to orchestrating seamless operations across physical and digital platforms.

Today, the conversations around AI have fundamentally changed. Retailers are no longer asking if AI can help bridge the physical-digital divide—they're implementing agentic AI solutions that are already delivering transformative results.

Here's what's happening right now. Retailers are actively adopting and evaluating agentic AI in retail where we already see 43% of retailers piloting with autonomous AI, and another 53% are evaluating its uses

But why the urgency?

To achieve the goals, retailers are developing a rich agentic environment marked by a vivid tapestry of AI agents that maintain context across the entire customer journey. Why? Because traditional systems simply can't keep pace with the constantly evolving customer journey and the velocity of the data it subsequently generates for the retailers to handle.

Hence Agentic AI—systems that can take autonomous actions toward business goals rather than simply responding to queries—are uniquely positioned to maintain perfect context throughout the customer experience.

Now what makes these retail agents legit, is its ability to act as an invisible thread connecting every touchpoint in the customer journey. They don't just observe the journey—they actively orchestrate it, removing friction and creating seamless experiences across physical and digital realms.

And when you look at it this shift from predictive to agentic AI represents the difference.

The difference between knowing what might happen next versus having systems that can independently respond to what happens next is a total game changer.

Top 4 Agentic AI Use Cases in Retail: How AI is Transforming the Retail Industry

So with that being said, let’s walk you through top four use cases where retailers are already deploying these autonomous AI systems – and the results that might convince even the most skeptical executives that we've entered a new era.

1. Customer Experience and Service

Let’s take you through a scenario: A customer adds items to their cart at lunch, see the delivery date as tomorrow, gets distracted, and later tries to check out on your app—only to find that actual delivery date differs from one that was shown initially. Frustrated, they contact support, but a chatbot keeps asking for the same info.

Sound familiar? It should. It's costing you customers right now.

Nearly half of all shoppers (49%) abandon purchases due to friction in the ordering process. That's not just an inconvenience – it's haemorrhaging revenue.

Here's where agentic AI in retail flips the script on traditional customer service:

a. Continuous Contextual Understanding: Unlike chatbots with the memory of a goldfish, these agents maintain persistent awareness of customer interactions across all channels. When someone browses online then walks into your store, the agent already knows what they were looking at and can guide store associates accordingly.

b. Proactive intervention: At NRF 2025, we saw the potential of the agents and how it identifies potential friction points in real-time. Imagine a customer searching for a product that's out of stock online but available nearby. Instead of showing "out of stock" and losing the sale, the agent proactively messages: "This item is available at your local store. Would you like me to reserve it for pickup in 30 minutes?"

c. Autonomous Problem resolution: The real game-changer? Autonomous problem resolution. When a package is delayed, the agent doesn't just send an apologetic email – it automatically reroutes the package, applies a discount code, and notifies the customer of the resolution.

d. Continuous learning and adaption: Perhaps most importantly, these agents improve with every interaction. Each customer interaction becomes a learning opportunity, allowing the system to refine its approach for future encounters.

2. Channel Optimization

While customer experience enhancement sets the stage for a more personalized shopping journey, channel optimization ensures that these experiences are seamlessly executed across every touch point, both online and offline.

But let's be brutally honest about "omnichannel" for a moment. For most retailers, it's not a symphony – it's a battlefield.

Your e-commerce promotions cannibalize store sales.

Store teams hold onto inventory that would sell faster online.

Your social commerce push creates fulfillment headaches in the warehouse.

The marketplace strategy undercuts direct-to-consumer margins.

And endless cross-functional meetings lead to watered-down compromises that please no one.

By the time your teams finally align on a cross-channel strategy, the market has already moved on.

And at this point of time most leaders might incline towards ‘we need to update our technology for better decision-making’ pitch. But the fact is that the root cause isn’t even technology – it’s decision velocity.

Now what these Agentic AI systems for retail do is it doesn’t just connect your channels – they actively orchestrate them as a unified business:

• When store traffic drops unexpectedly in (let’s say in Chicago due to weather), the agent automatically shifts promotional emphasis to mobile channels in that region


• When competitive analysis shows an online marketplace under pricing you on a key product, the agent dynamically adjusts online pricing while maintaining in-store margins where competition is different


• When inventory starts building up in certain stores, the agent redirects it to fulfillment centres supporting e-commerce where demand is higher


• Instead of batch processing these decisions weekly, the agent makes thousands of micro-adjustments daily

3. Assortment Planning

Now facts being said, even now most retailers today rely on a lethal combination of historical data, category-level heuristics, and merchant intuition when planning their assortments. It works but this approach now inevitably leads to merchandise selections that are too reactive to last year's patterns, too slow to adapt to emerging trends, too uniform across different markets, and too prone to both stockouts and overstocks simultaneously.

Now the old approach of quarterly assortment reviews is being replaced by our Assortment Observer agent's data-driven planogram capabilities. The system continuously monitors performance and alerts merchants within hours when adjustments are needed. It identifies non-obvious relationships between products that traditional analysis would miss, and provides specific, actionable planogram recommendations based on current conditions.

These dynamic planograms ensure your shelves are always optimized for the right mix of products in the right places, reducing both stockouts and overstocks.

4. SKU planning

That "strategic" inventory planning process you've spent years refining? It's fundamentally broken.

This might’ve hurt but it's true. When it comes to business settings, particularly the inventory management process, 63% inventory accuracy means retailers are making important decisions based on inadequate information. And this problem scales dramatically when you consider that an average retailer company manage thousands of SKUs across hundreds of locations. This complexity at scale ends up getting the 'best' out of our optimization efforts.

And it becomes inevitable to avoid this scenario if not dealt with properly because each SKU-location combination requires specific decisions about:

• Initial buy quantities
• Reorder timing
• Store-level allocation
• Lifecycle management
• Markdown timing
• Substitution relationships

Naturally, traditional systems force you to define broad rules and parameters that apply across entire categories. But every SKU has its own unique demand pattern, price elasticity, and lifecycle trajectory. Treating them all the same is like having one prescription for every medical condition.

And now this is exactly what agentic AI systems for retail and our AI agents excel at – handling complexity at scale considering the unique needs of the situation. And hence what makes agentic SKU planning revolutionary is its ability to make decisions with SKU-level granularity at massive scale. Here's the architectural blueprint that's transforming retail inventory management:

What makes this system revolutionary isn't just each agent's individual capabilities—it's how they work together. When the Stock Tracking Agent identifies an emerging trend, it signals the Inventory Flow Agent to adjust allocation priorities while the Pricing Agent optimizes commercial terms.

The retail revolution is just the beginning

Now that we have seen the power of agentic AI in retail industry, there’s one thing that has become clearer now more than ever – That this is just the start. Because what’s happening in retail is just the first domino in a much larger transformation. The same agentic principles revolutionizing customer experiences and operational efficiency in retail are rapidly expanding into CPG, pharmaceutical, and countless other industries. This isn't coincidental—it's the natural progression of an AI evolution that's fundamentally changing how businesses operate.

The future isn't something to prepare for tomorrow—it's something to implement today. Organizations partnering with Polestar Analytics aren't just adopting technology; they're building the adaptive intelligence that will define market leadership in an increasingly complex business landscape.

The agentic revolution has begun. Will you lead it, or follow?