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Here’s how Agentic AI streamlines workflows and improves customer outcomes in the retail sector.
While 88% of retailers have now integrated AI into their operations, only 39% can actually point to a significant impact on their bottom line.
This “implementation gap” exists because most businesses are still stuck in the era of Passive AI. We see it everywhere – a recommendation engine here, a siloed chatbot there. While these tools optimize small slices of the business, they often require constant human hand-holding and rarely have the authority to own a workflow from start to finish.
The retailers actually winning the ROI game have already moved to AI Agents. These are autonomous collaborators that take action. By bridging the gap between seeing a problem and solving it, AI agents in retail allow for real-time resolution, involving humans only when strategic judgment is truly required.
As Doug Herrington, CEO of Worldwide Amazon Stores, noted at the National Retail Federation 2025: “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.”
To help you move beyond simply “having AI” and start seeing measurable results, let’s explore the top AI agents in retail use cases that are redefining the sector today.
Key takeaways (TL;DR)
- The “ROI gap” in retail exists because enterprises use AI to suggest actions rather than execute them. Retailers need AI agents to handle end-to-end workflows.
- In the retail sector, trends in days. AI agents eliminate the “decision latency” before the opportunity expires.
- Kore.ai’s retail AI agents act proactively across fragmented silos. They ensure the storefront, warehouse, and supply chain operate as one responsive, real-time organism.
- Retailers can convert sunk administrative costs into financial headroom by shifting from manual coordination to agent-led orchestration.
What are AI agents in retail?
AI agents in retail are autonomous software systems designed to manage commerce workflows from intent to fulfillment. They use text or voice to understand a customer’s or employee’s needs, pull real-time data from Inventory Management Systems (IMS), CRMs, ERPs, POS systems, warehouse databases, and logistics providers.
What makes them “Agentic” is not the interface, but the ownership. AI agents can:
- Interpret intent across channels (website, mobile app, in-store kiosk, contact centre)
- Pull structured and unstructured data in real time
- Apply pricing logic, promotion rules, inventory constraints, and fulfillment policies
- Decide next steps within the defined margin and operational guardrails
- Execute actions across commerce, supply chain, and service platforms
- Monitor status and follow through until completion
- Escalate exceptions with a structured operational trail
In practical terms, AI agents function like a digital team member. They take action where needed without constant manual follow-ups and adapt as markets change or supply chain conditions change. It is this ability to act autonomously that makes AI agents the perfect fit for the retail sector, where high volumes, razor-thin margins, and 24/7 customer expectations are the norm.
Why AI agents matter in the retail industry
In retail, the window to win a customer closes long before it appears on a dashboard. If you are defending a price point that a competitor has already undercut, or holding stock that has long since fallen out of trend, you aren’t “data-driven”; you are simply late.
The real crisis isn’t a lack of information; it’s the latency of action. Retailers don’t need more analytics to tell them they are losing money; they need a digital workforce that can act before the opportunity expires.
Here is why the agentic AI in retail is the only way to close the gap:
1. Bridging the reality gap
With average inventory accuracy at just 70%, “dirty data” ultimately leads to post-purchase cancellation. While traditional systems simply report these errors, AI agents interrogate the data, autonomously identifying old stock and pausing ad spend or triggering warehouse checks before a customer hits the checkout button.
2. Reducing “sunk” admin costs
Currently, up to 30% of operating budgets in retail are swallowed by admin tasks, leaving teams with no financial headroom to grow the brand. AI agents handle manual tasks, such as document processing and matching invoices, so that retailers can reallocate that portion of the operating budget toward R&D or brand expansion.
3. Eliminating last-mile anxiety
About 80% of shoppers abandon a brand after a single delivery mishap; yet dashboards report delays after they occur. AI agents act as proactive controllers, monitoring weather and carrier capacity in real-time to predict bottlenecks and autonomously reroute shipments.
Here’s how Kore.ai AI agents are transforming retail
Top 12 AI agents in retail use cases
In retail, very few workflows follow a straight line. From shifting consumer trends to sudden supply chain bottlenecks, the "ground truth" changes by the hour.
This is where AI agents come into their own. Rather than relying on rigid bots or static automation, AI agents respond as situations evolve, adjusting in real-time as new inventory data, competitor pricing signals, or customer behaviors emerge.
That adaptability is why AI agents are the key to closing the "implementation gap." There are three broad areas where retailers are seeing the most value today:
- Customer experience use cases - improving search, discovery, guided selling, and post-purchase support across digital and physical stores.
- Core retail business operations - strengthening fraud detection, vendor management, and back-office documentation that keeps the enterprise running.
- Internal team use cases - supporting merchandising, supply chain, store ops, and HR teams in executing complex logistics and planning workflows.
Reimagine customer journeys: How AI agents level up customer experience
For modern shoppers, the retail journey should feel instant and intuitive. Instead, they often encounter irrelevant results, generic marketing, and high-friction returns.
In fact, traditional engagement models are hitting their limits, with single human-handled interactions costing $6 to $14. Here’s how AI agents in customer service remove friction and keep journeys moving:
Use case 1 - AI-powered product search & discovery
The problem:
Globally, retailers lose up to $2 trillion because traditional search fails to surface relevant products. 68% of shoppers leave a site when they cannot find what they want quickly an will not return after a single frustrating search experience.
How AI agents help:
AI agents turn a search bar into a personal shopper through coordinated steps. Imagine a shopper lands on your site where they type: "I need something waterproof but stylish for a coastal wedding in October." One AI agent interprets the customer’s natural language intent, moving beyond keywords to understand context.
A second agent instantly scans the product catalog and real-time inventory to find matching attributes. Prellely, a third agent layers in personalization, filtering those results based on the shopper’s past style preferences, size, and location. Instead of a "No Results Found" page, the customer gets a curated collection.
The result:
- Higher conversion: AI-enhanced search can lift conversion rates by up to 20%.
- Increased AOV: Discovery engines increase average order value by roughly 12% by surfacing complementary products.
- Brand loyalty: Reduced bounce rates keep shoppers engaged and returning to sites.
Use case 2 - Personalized marketing & loyalty optimization
The problem:
While 76% of consumers expect brands to offer personalized experiences, only 35% of retailers currently deliver them across all channels. This failure to tailor experiences leads to lost revenue, as 80% of consumers are more likely to buy when they feel an experience is designed specifically for them.
How AI agents help:
AI agents unify behavioral, transactional, and contextual data to build a living profile of every customer.
Think of a customer who has spent the last 20 minutes browsing high-end running shoes on your app but hasn't checked out. One AI agent monitors real-time behavioral signals, such as cart additions or items favorited. A second agent cross-references this with the customer’s loyalty status and lifetime purchase history from the CRM.
A third agent then generates a context-aware offer, such as a specific discount on a favorited item that is currently low in stock, and triggers it via the customer's preferred channel (SMS, App, or Email) at the exact moment they are most likely to convert.
The result:
- Revenue growth: Effective personalization strategies can drive up to 40% higher revenue.
- Efficiency: Marketing ROI can increase by as much as 30% through improved spend efficiency.
- Lower acquisition costs: Targeted messaging can reduce customer acquisition costs by up to 50%.
Use case 3 - Intelligent commerce assist (Digital Shopping Guidance)
The problem:
Many retail promotions fail because they rely on static calendars rather than actual demand. Misaligned forecasts are responsible for a 4-5% annual loss in gross sales, and without AI, campaigns take 20% longer to execute, leaving retailers unable to respond to rapid market shifts.
How AI agents help:
Imagine a customer is looking at a high-tech espresso machine. They have one specific question: "Will this fit under my 15-inch kitchen cabinets?”
One agent greets the customer and interprets the question. The second agent scans the internal product manuals and spec sheets to find the exact dimensions (14.5 inches). A third agent confirms the item is in stock at the local store.
The result:
- Increased revenue: AI-driven planning delivers a measurable increase in revenue compared to traditional methods.
- Sell-through success: Seasonal assortments see a 6.9% improvement in sell-through rates.
- Speed to market: Campaign rollout times improve by 20%, enabling instant responses to competitors.
Use case 4 - Returns & reverse logistics experience automation
The problem:
Returns are a major drain on profit, with U.S. retailers handling $890 billion in returns, roughly 16.9% of total sales. Online return rates are even higher, often reaching 40%. These reverse logistics expenses can consume up to 7% of gross sales, turning a customer service necessity into a massive cost center.
How AI agents help:
Imagine a customer wants to return a jacket. Instead of them waiting days for a label, the process starts the second they open the app.
One agent uses Vision AI to analyze a photo of the jacket uploaded by the customer to verify its condition. The second agent checks the customer’s return history to ensure there are no patterns of fraud or "wardrobing." A third agent instantly issues a refund and routes the item to a nearby store for resale, rather than a distant warehouse.
The result:
- Cost savings: Automation and fraud detection can reduce return processing costs by 15-25%.
- Faster cycles: Return cycle times are compressed by 30-50% through AI-powered grading and routing.
- Recovered value: Retailers see a 10-15% uplift in recovered resale value via dynamic pricing for returned goods.
Reimagine workforce productivity: How AI agents support retail employees
In retail, merchandising & supply chain teams lose a staggering amount of time chasing data across legacy ERPs and spreadsheets. Here is where AI agents at work start to ease the load:
Use case 5 - Inventory management & replenishment intelligence
The problem:
Inventory distortion is a $1.7 trillion global problem. Despite heavy investment in software, the average retail inventory accuracy hovers at just 70%. High-skilled planners are often reduced to "data-shufflers," manually cross-checking stock levels across warehouses and store shelves while trying to prevent stockouts or overstocks that eat into margins.
How AI agents help:
AI agents turn inventory management from a reactive report into a proactive workflow.
Imagine a sudden spike in demand for a specific SKU in a specific region. An initial AI agent identifies the trend in real-time by monitoring POS data. It immediately checks stock levels across the entire network, such as warehouses, transit, and dark stores. A second agent evaluates the logistics: Is it faster to reorder from the supplier or trigger a store-to-store transfer? It applies business logic (shipping costs vs. margin) and executes the transfer request automatically.
Meanwhile, a third agent updates the digital storefront to reflect real-time availability, ensuring the customer never sees an "In Stock" label for a product that isn't there.
The result:
- Reduced stockouts: Proactive replenishment keeps high-velocity items on shelves.
- Higher accuracy: Real-time reconciliation across silos raises inventory precision toward 99%.
- Capital efficiency: Minimizes "dead stock" by positioning inventory where it is actually selling.
Use case 6 - Demand forecasting & allocation optimization
The problem:
Traditional forecasting is often "rearview mirror" thinking, relying on last year's data to predict tomorrow's needs. This misalignment leads to a 4-5% loss in total gross sales. When forecasts are wrong, the wrong products end up in the wrong places, leading to forced markdowns and missed opportunities.
How AI agents help:
AI agents shift forecasting from static cycles to "continuous intelligence." One agent perpetually monitors sales velocity alongside external signals like local weather patterns, social media trends, and regional economic shifts. When it detects a deviation from the baseline, it doesn't just send an alert; it suggests an adjusted allocation plan.
A second agent takes that plan and validates it against current warehouse capacity and labor availability. If the plan is within guardrails, it automatically adjusts the purchase orders or shipping schedules. Instead of a monthly "guess," the retailer has a daily, agent-led execution strategy.
The result:
- Forecasting accuracy gains: Retailers see a 35–42% improvement in forecast precision.
- Reduced waste: Fewer "wrong" products sent to stores, protecting margins.
- Agility: The ability to pivot inventory in days rather than weeks.
Use case 7 - Dynamic pricing
The problem:
Retailers relying on static pricing lose margin during peak demand and overcorrect during slow periods. Markdown price losses can reduce margins by 20-25%.
How AI agents help:
AI agents evaluate pricing continuously. One agent tracks real-time demand elasticity, competitor pricing, and inventory levels. Another applies business guardrails around margin floors, brand positioning, and promotional strategy.
A third agent executes approved price adjustments across e-commerce, POS, and digital shelf labels. As demand rises, prices adjust within defined thresholds. As inventory builds, markdown timing is optimized to protect margin without sacrificing sell-through.
The result:
- Up to 10% profit increase through adaptive pricing
- 13% uplift in sales during demand peaks
- 30% faster inventory turnover
- Reduced unnecessary markdowns
Use case 8 - Workforce scheduling & recruitment automation
The problem:
Retail faces a brutal ~60% turnover rate. According to Kore.ai’s AI for retail guide, store managers spend up to 15 hours a week manually building schedules, yet 77% of stores still report lost sales because they were understaffed during a peak hour or overstaffed during a lull.
How AI agents help:
AI agents function as an automated "Operations Assistant" for store managers. An agent analyzes historical traffic patterns and upcoming promotional calendars to predict labor needs down to the hour. It builds a draft schedule that optimizes for both labor laws and employee preferences.
When a "call-out" happens, a second agent takes over: it identifies available staff with the right training, sends an automated message via the employee app to offer the shift, and updates the payroll system once a replacement is found. The manager only gets involved to click "approve."
The result:
- Manager productivity: Reclaims hours of administrative time for floor leadership.
- Improved employee experience: Predictable, fair schedules reduce burnout and turnover.
- Sales growth: Ensures the right staff are present when the store is busiest.
Reimagine core business operations: How AI agents help with back-office processes
Behind every transaction is a web of invoices, vendor contracts, and fraud checks. Currently, 20-30% of retail operating budgets are consumed by manual paperwork and administrative tasks.
AI agents in business processes move these functions from slow, manual queues to real-time, autonomous workflows.
Use case 9 - Fraud detection in payments & returns
The problem:
With $890 billion in annual returns, retailers are prime targets for refund abuse. Manual fraud reviews are too slow for modern commerce, yet rigid automated rules often block legitimate VIP customers, damaging long-term loyalty.
How AI agents help:
Think of a high-value return request for a designer handbag initiated minutes after the item was delivered. An AI agent instantly analyzes the transaction metadata, device ID, and shipping address against known fraud databases.
Another agent cross-references the request with the shopper's lifetime return rate and purchase consistency, for example, does this customer regularly "buy and return" high-ticket items?. If the risk score is high, a third agent automatically flags the return for a physical "in-person only" inspection at a store rather than an instant refund, protecting the retailer's capital.
The result:
- Reduced shrink: Drastically lowers "refund fraud" losses.
- Lower operational costs: Cuts reverse logistics expenses, which currently eat up to 7% of gross sales.
- Precision: Ensures legitimate customers get instant refunds while high-risk cases are intercepted.
Use case 10 - Document processing & vendor workflow automation
The problem:
Retailers manage thousands of vendors, each with different invoice formats. Manually matching Purchase Orders (POs) to shipping manifests and invoices is a massive drain, leading to "human-error" overpayments and strained supplier relationships.
How AI agents help:
Imagine a shipment of 1,000 units arrives, but the digital invoice lists 1,050 units and the packing slip is handwritten. An AI agent uses OCR and NLP to "read" the handwritten slip and the digital invoice, extracting line-item totals and SKU numbers.
Another agent compares these figures against the original PO in the ERP system to find the 50-unit discrepancy. A third agent automatically approves payment for the 1,000 units received and drafts a "Discrepancy Notice" to the vendor for the remaining 50, closing the loop without human data entry.
The result:
- Budget reclamation: Reclaims 20-30% of the budget currently lost to manual document handling.
- Zero overpayments: Ensures the company only pays for what was actually delivered.
- Staff efficiency: Move teams from "data-shuffling" to strategic vendor management.
Use case 11 - Supplier coordination & procurement intelligence
The problem:
Supply chains are brittle. When a supplier faces a delay, retailers often don't find out until the "Out of Stock" sign is already on the shelf, resulting in a loss of total gross sales.
How AI agents help:
Think of a key supplier in a region suddenly hit by a logistics issue. An AI agent constantly monitors logistics feeds for signals that could impact your top-selling SKUs. A second agent immediately calculates your current "days of supply" and identifies which stores will run out of stock first.
Parellely, a third agent automatically identifies an alternative approved supplier from your database, checks their live pricing/lead times, and prepares a "Ready-to-send" purchase order for the procurement manager to approve with one click.
The result:
- Reduced stock disruption risk
- Faster procurement cycle times
- Improved supplier performance tracking
- Lower working capital exposure
Use case 12 - Retail process visibility & performance monitoring
The problem:
Retail operations generate enormous volumes of data, but visibility is often delayed. Performance issues surface only after the revenue impact is visible. Without real-time observability, bottlenecks accumulate across merchandising, logistics, and store operations.
How AI agents help:
AI agents create continuous operational oversight. One agent monitors throughput, exception rates, and automation coverage across workflows. Another detects anomalies in processing speed, return rates, or stock accuracy.
A third generates contextual alerts for operational leaders before issues cascade. Rather than static reports, teams receive live insights with recommended corrective actions.
The result:
- Faster issue detection and resolution
- Reduced operational blind spots
- Clearer ROI tracking for automation initiatives
- Continuous performance optimization across departments
Real-world case studies of AI agents in retail
Retailers are already seeing real, measurable results by deploying Kore.ai AI agents across complex customer and internal workflows. Here’s how agentic AI in retail works in practice:
Case study #1 - Scaling global IT support for 90k employees at a major confectionery leader
The world’s largest confectionery and pet care enterprise struggled to scale internal IT support for its 90,000 global employees. With a support team of just 50 agents, they faced immense pressure to handle high ticket volumes across multiple time zones and 34 different languages.
The organization replaced its legacy, static FAQ bot with an agentic IT assistant. This AI agent is integrated directly with ServiceNow, allowing it to autonomously manage tickets, check statuses, and execute service requests. When human intervention is needed, the agent hands off the case with full context, ensuring a seamless experience.
The implementation delivered significant business impact:
- 74% automation rate achieved for internal IT inquiries
- 65% self-service resolution within the first month of deployment
- Multilingual support for 34 languages, enabling 24/7 global coverage without increasing headcount
- 90% Employee Satisfaction (ESAT) score driven by faster, more accurate resolutions
Case study #2 - Orchestrating 900,000 weekly self-service sessions for a global e-commerce giant
A leading global e-commerce marketplace faced the challenge of managing massive interaction volumes from millions of buyers and sellers. Their legacy IVR and digital support systems were fragmented, leading to inconsistent experiences and high operational strain on live support teams.
The company deployed an agentic service layer to unify its voice and digital channels. These AI agents take ownership of complex, high-volume journeys, such as managing disputes, tracking refunds, and resolving shipping issues, across the entire marketplace ecosystem. By establishing a centralized intent understanding, the agents provide a consistent response regardless of whether the customer calls or chats.
The deployment delivered measurable outcomes:
- 75% containment rate across voice interactions, successfully modernizing the legacy IVR experience
- Over 900,000 weekly self-service sessions handled autonomously across digital channels
- 520,000 monthly voice calls managed by AI agents, reducing the burden on the contact center
- 85% accuracy in intent and context understanding, ensuring fewer failed self-service attempts and higher customer confidence
How does Kore.ai help bring AI agents into retail?
Kore.ai helps retailers and e-commerce brands embed AI agents into every layer of their operations. The platform is built for the high-volume, low-margin reality of modern retail, where speed, inventory accuracy, and margin protection are the primary drivers of success.
Retail-ready security and scale
Kore.ai is built with enterprise-grade security and configurable guardrails. AI agents operate within defined brand and policy constraints, ensuring that every customer interaction and back-office transaction remains compliant with data privacy laws and internal business logic.
Pre-built AI agents for retail
Launch faster with pre-built AI agents designed for customer service, order management, loyalty programs, and IT support. These agents are ready-to-deploy but fully adaptable, allowing teams to update promotions, return policies, and FAQ logic in real-time as market trends shift.
Deep commerce integrations
Kore.ai connects to the systems that run your business, such as OMS, WMS, ERP, and CRM, through 250+ pre-built connectors. This ensures agents have a full operational context, allowing them to verify real-time stock, track parcels, and reconcile invoices without manual data entry.
Multi-agent orchestration
Kore.ai enables agents to collaborate across silos. For example, a Discovery Agent can hand a customer off to a Fulfillment Agent who then coordinates with a Logistics Agent to secure a delivery slot. This ensures the customer journey moves end-to-end without stalling.
No-code and pro-code flexibility
Merchandising and operations teams can design and deploy AI-powered workflows using an intuitive visual builder. For complex requirements, such as custom fraud detection models or dynamic pricing logic, developers can extend the platform’s capabilities without disrupting existing infrastructure.
Real-time analytics and observability
Built-in tools provide visibility into agent performance, conversion trends, and inventory anomalies. Retailers can monitor outcomes in real-time, identifying bottlenecks in the "last mile" or the supply chain and refining agent behavior to protect margins instantly.
Conclusion
Retailers are facing a fundamental shift in expectations. Customers no longer tolerate a three-day wait for a refund, and internal teams expect to be freed from the administrative drudgery of chasing vendors and reconciling spreadsheets.
As the industry grapples with this relentless pressure, the winners will be those who rely less on manual coordination and more on agent-led workflows.
AI agents make this possible by taking responsibility for the "perishable decisions" that define modern retail. They bridge the gap between seeing a problem and solving it, allowing your people to focus on strategic judgment while AI agents keep the business moving in real-time.
FAQs
Q1. We have a lot of "dirty data" and legacy systems. Can we still deploy agents?
Actually, this is where agents excel. Unlike rigid integrations that require perfect data, AI agents can be trained to "interrogate" messy data. For example, if your inventory says an item is in stock, but it hasn’t sold in three days, an agent can autonomously flag this as a potential "phantom stock" error and trigger a manual check, effectively cleaning your data as they work.
Q2. How do you ensure AI agents don't make costly pricing or refund mistakes?
Security and governance are baked into the platform. You set the "operational guardrails," for example, an agent might have the authority to issue refunds up to $50, but anything higher or any pattern that looks like fraud is automatically escalated to a human manager. You retain 100% control over the logic the agent follows.
Q3. How do AI agents differ from the chatbots we already have?
Traditional chatbots are "passive"; they follow pre-set scripts and usually just point users to a link or a human. AI agents are "active." They have the authority to pull data from your ERP or warehouse systems, make decisions based on your business rules, and execute tasks like processing a refund or reordering stock without needing a human to step in.
Q4. How long does it typically take to see a return on investment (ROI)?
While long-term infrastructure takes time, many retailers see "Quick Win" ROI within 3 to 6 months by targeting high-friction areas like returns automation or IT helpdesk support. By reducing the cost-per-interaction and preventing search abandonment, the agents often pay for themselves within the first few quarters of full deployment.
