.webp)
Here’s how retail enterprises are using AI to make the most of it.
Recently, at the National Retail Federation 2025, Doug Herrington, CEO of Worldwide Amazon Stores, said, “AI in retail is becoming transformative, and we really haven't had a technology revolution as large as this since the start of the internet.”
When the man running the world's largest online retailer says something like that in public, it's worth sitting up and paying attention. But for many, the reality on the ground is a bit different. While 88% of retailers have now integrated AI into at least one business function, only 39% can actually point to a significant impact.
The reality is that the "ChatGPT era" was a decent warm-up, but it's no longer up to scratch for the three non-negotiable pain points defining this year:
- The loyalty churn. With 73% of consumers willing to walk after a single friction-filled experience, "good enough" is no longer good enough.
- Operational blind spots. Fragmented data across supply chains means most retailers are still guessing when it comes to real-time inventory.
- Agentic commerce is here. Autonomous AI agents are already browsing and buying on behalf of consumers. If your infrastructure can't talk to them, you're effectively invisible.
The secret to staying ahead isn't just "more AI," it’s Agentic AI. We are moving from standalone models to integrated systems that don’t just suggest products, but orchestrate the entire lifecycle from predictive stocking to autonomous checkout. In fact, the global AI in retail market is projected to grow at a CAGR of over 23.9% through 2030, signaling how rapidly enterprises are adopting these technologies to stay competitive.
Key takeaways:
- The ChatGPT era was the warm-up. Agentic AI is the main event. Retailers seeing real commercial impact in 2026 are using AI to autonomously manage inventory, resolve customer issues end-to-end, and make thousands of pricing decisions.
- AI in retail is only as powerful as the problem it's solving. Retailers seeing real returns start with a specific operational pain point, whether that's dead stock, loyalty churn, or cost-to-serve, and build from there.
- AI in retail doesn't mean starting from scratch. You don't need to rip out your ERP to see meaningful results. You need the right layer of intelligence sitting across it.
What is AI in retail?
Ask that question in 2022 and the answer was chatbots and recommendation engines. Decent enough, but hardly transformative.
In 2026, that definition has been completely turned on its head. AI in retail today means systems that don't just respond; they reason, plan, and act. They monitor conditions in real time, make decisions autonomously, and execute tasks across the entire retail value chain without waiting to be nudged at every step.
Retailers in 2026 are transitioning from reactive personalization strategies to predictive engines that analyze real-time data, such as weather patterns, local events, and inventory levels, to forecast customer intent before consumers even recognize their needs.
The technology driving this shift is Agentic AI. Agentic AI workflows are autonomously managing pricing, inventory, and promotions, making merchandising a truly real-time, adaptive system. Think of it as the difference between a sat-nav that reads out directions and a self-driving car. Same destination, entirely different level of intelligence.
In fact, retailers like Walmart are deploying AI agents tailored for retail-specific tasks, using their own data and LLMs to drive real business impact across customer experience and operations.
What are the 7 top use cases of AI in retail?
Use case 1 - End the inventory guessing game with autonomous merchandising
For most retail enterprises, the most significant drain on the P&L is the silent killer of inventory misalignment. Sitting on mountains of "dead stock" in one region while another store screams for stock is a classic retail headache.
Traditional systems are notoriously reactive; they report what happened last Tuesday. By the time a human analyst sifts through the data, the opportunity has usually bolted.
The shift to Agentic AI pays for itself by replacing passive dashboards with an autonomous "merchandising brain." These agents monitor "soft" signals, such as local weather shifts, viral social trends, and competitor price drops, around the clock. Because the system is always ON, it triggers redistribution orders across your network before the shelf even goes empty.
The commercial impact is immediate. According to McKinsey, retailers who pivot to these "Self-Healing" supply chains see up to 90% faster inventory redistribution. When an AI agent tracks SKU velocity in real-time, it initiates subtle price adjustments or reroutes slow-moving stock to high-demand hubs independently
In an industry where "cash is king," that speed is the difference between a profitable quarter and a complete muddle.
Use case 2 - Slashing cost-to-serve with autonomous resolution
The biggest drain on retail margins is the post-purchase “hiccups”. Most enterprises still rely on traditional bots that simply deflect tickets by pointing customers toward FAQs. In 2026, that is no longer up to scratch. Modern retail leaders are pivoting to Agentic CX, where the goal is to resolve the issue entirely without human intervention.
The commercial gap here is stark. While a standard chatbot might handle a simple "Where is my order?" query by providing a tracking link, an AI agent connects directly to your ERP and logistics APIs to actually sort it out. If an order is delayed, the agent doesn't just offer an apology; it can independently issue a partial refund or reroute a replacement from a nearby store to hit a delivery window.
This level of autonomy is driving a 30-40% reduction in support operating costs. By moving from "conversational" to "transactional," you remove the human bottleneck from routine tasks like returns processing or account updates.
Ultimately, Agentic CX turns a cost centre into a loyalty engine. When you resolve a customer’s problem before they even have to ask, you’re protecting the lifetime value of your most profitable shoppers.
Use case 3 - Increasing basket size through predictive intent
For years, "personalization" was just a polite way of pushing ads for the things customers have already bought. In 2026, the standard has shifted from reactive recommendations to predictive Intent.
AI no longer just looks at a customer's past purchases, but senses their current needs and solves their next problem. The financial upside of predictive personalization is massive.
When you move from generic suggestions to intent-based merchandising, you’re looking at a potential 15% bump in top-line revenue.
For instance, if a customer is eyeing up a premium tent, the system understands they aren't just "buying a product," they’re "planning a trip." It autonomously bundles the tent with a bespoke bundle of gear, such as sleeping bags, a lantern, and more, and offers a session-specific "complete the kit" discount.
This way, you’re solving their entire problem in one go and significantly driving up the Average Order Value (AOV).
Use case 4 - Protecting margins with Dynamic Pricing
In a world where costs are fluctuating and competitors are just a click away, static pricing is a fast track to treading water. Retailers have traditionally relied on blanket seasonal sales that give away profit to customers who would have paid full price.
In 2026, the leaders have pivoted to AI-driven dynamic pricing to protect their margins in real-time. In fact, retailers using AI for price optimization are seeing a 2-5% increase in overall gross margin. This isn't about arbitrary price hikes; it’s about real-time adjustments based on stock levels, competitor moves, and even local demand surges.
If you’re sitting on excess stock of a specific SKU, the AI can trigger a surgical, local discount to move it quickly, rather than waiting for a site-wide "fire sale" that eats into your total profit.
By automating the thousands of tiny pricing decisions that a human team simply can’t manage, you ensure your business is always reacting to the market at light speed.
Use case 5 - Closing the data gap with Computer Vision
For too long, the physical shop floor has been a data black hole compared to its digital counterparts. While e-commerce tracks every click, brick-and-mortar stores have largely been left flying blind.
In 2026, Computer Vision has fixed this, turning the high street into a live, measurable data feed. By using AI to "see" how customers navigate aisles, retailers are seeing a 10-15% bump in conversion rates. The system identifies "dead zones" where footfall drops or flags when a queue is getting long enough to make shoppers throw in the towel.
It also kills off "phantom inventory" the stock that the system says is there, but is actually misplaced or left in a fitting room. AI agents monitor shelves in real-time, alerting staff the moment a display needs a top-up. This ensures your team stays on the ball, spending their time closing sales rather than hunting for "lost" shirts.
Ultimately, this turns the physical store into a high-performance machine. You stop guessing which layouts work and start using real-time behaviour to ensure your most expensive real estate is finally pulling its weight
Use case 6 - Eliminating search friction with Multimodal AI
The days of customers struggling with the right keywords are effectively over. In 2026, the traditional search bar is being replaced by Voice and Visual Search. This shift is about meeting the customer where they are, whether they have a photo of a "drip" or just a vague verbal description.
The business impact is significant. Retailers implementing multimodal search are seeing conversion rates climb by up to 20%. This is because you’re removing the mental load of the shopping process.
For instance, if a customer sees a pair of boots in the street, they can simply snap a photo and find the exact match (or a similar alternative) in your inventory instantly. It turns every real-world moment into a potential point of sale.
This isn't just a fancy gimmick; it’s about capturing intent before it evaporates. A customer can say, "Find me a waterproof jacket that’s breathable enough for a summer hike," and the AI understands the context, filters the technical specs, and presents the best options.
By moving beyond the limitations of text, you ensure your brand is accessible to a new generation of shoppers who find typing out search queries too much.
Use case 7 - Empowering staff with AI copilots
For years, the digital transformation of retail stopped at the breakroom door. While the head office enjoyed advanced analytics, store associates were often left with clunky legacy hardware and paper manifests.
In 2026, modern retailers are equipping their frontlines with AI Copilots. Rather than replacing frontline staff, these tools sit alongside them surfacing real-time product knowledge, order history, personalized upsell prompts, and resolution guidance in a single interface, exactly when it's needed.
An associate helping a customer on the shop floor no longer has to disappear into the stockroom or call a manager. The answer is already there.
Retailers deploying AI copilots for frontline teams are seeing a 20-30% reduction in average handle time and a measurable uplift in first-contact resolution.
More importantly, AI Copilots solve the industry’s perennial "talent drain" by removing the frustration of mundane administrative tasks. By augmenting human staff with real-time intelligence, you ensure the face of your brand is informed and agile.
What are the benefits of AI in retail?
The case for AI in retail isn't theoretical anymore it's showing up in P&Ls. As of 2025, 87% of retailers report that AI has had a positive impact on revenue. Some of the key advantages of using AI in enterprise retail include
1. Personalized customer experiences
AI analyses real-time behaviour, purchase history, and intent signals to serve up recommendations that actually make sense and make sales.
According to Deloitte, 80% of customers are more likely to purchase from a brand that offers a personalized experience and AI is the only way to deliver that at scale across millions of shoppers simultaneously
For instance, one of the world's largest consumer electronics and home appliance brands is using AI agents built on the Kore.ai Platform to help its online shoppers with the necessary product information and support to increase online sales revenue
2. Streamlined operations
In 2026, 68% of retailers plan to apply AI to inventory management and supply chain optimization. This is because the cost of getting it wrong, through overstocking, stockouts, or misallocated fulfilment, is no longer acceptable in a margin-compressed market. AI brings predictability to what has historically been retail's biggest operational headache.
3. Reduced cost to serve
AI has contributed to an average 31% improvement in customer satisfaction and retention over the last 12 months, largely by resolving issues faster, deflecting routine queries, and freeing up human agents for the conversations that actually need them. Less friction for the customer, lower operating costs for the business.
4. Insights into customer behavior
AI doesn't just collect data; it makes sense of it. Sentiment analysis, buying pattern recognition, and behavioural analytics give retailers a live, granular picture of what customers actually want. That intelligence feeds better merchandising, sharper marketing, and fewer costly misses.
Conclusion
The window for treating AI as a pilot project has officially closed. The retailers pulling ahead in 2026 are embedding AI into the core of how they operate, serve customers, and make decisions.
From autonomous merchandising and dynamic pricing to frontline copilots and predictive personalization, the use cases are no longer hypothetical. They're live, they're measurable, and they're showing up in quarterly results.
The question isn't whether AI will reshape retail. That ship has sailed. The question is whether your business will be the one doing the reshaping or the one being reshaped.
Kore.ai's Agentic AI platform is purpose-built for retailers who are serious about closing that gap. We provide end-to-end agentic solutions that connect your data, your teams, and your customer touchpoints into something that actually works together.
If you're ready to move from surface-level AI to the kind that genuinely moves the needle, let's talk
FAQs
Q1. What is AI in retail, and how has it changed in 2026?
AI in retail refers to the use of artificial intelligence to automate, personalize, and optimize every layer of the retail operation, from customer-facing experiences to backend supply chain decisions. What's changed in 2026 is the shift from reactive, rules-based automation to agentic AI.
Q2. What is Agentic AI and why does it matter for retailers?
Agentic AI refers to systems that can autonomously plan, make decisions, and execute multi-step tasks. For retailers, this is a meaningful step change. Rather than an AI that tells you stock is running low, an agentic system identifies the issue, triggers a redistribution order, adjusts the pricing on slow-moving SKUs in another region, and updates your logistics partner, all without human intervention.
Q3. How long does it take to see ROI from AI in retail?
It depends on the use case, but most retailers deploying AI in customer service or inventory management report measurable impact within six to twelve months. The fastest returns typically come from automating high-volume, repetitive workflows returns processing, order queries, stock replenishment where the baseline cost is already well understood.
Q4. Do I need to replace my existing systems to implement AI in retail?
Not necessarily. Most enterprise AI platforms, including Kore.ai, are designed to integrate with existing ERP, CRM, and logistics infrastructure. The goal is to add intelligence on top of what you already have, not rip it out and start again.
Q5. What's the biggest mistake retailers make when deploying AI?
Starting with the technology rather than the problem. Retailers who deploy AI to tick a box, or chase a trend, rarely see meaningful returns. The ones who succeed define a specific pain point first, such as a leaking margin, a service bottleneck, or a personalization gap, and then find the right AI application to fix it.
