How agentic AI is reshaping retail

What is agentic AI in retail?

Agentic AI in retail refers to the use of autonomous AI systems—often called AI agents—that reason, make decisions, direct their own processes and tool usage, and act on behalf of users or retail businesses to achieve the goals set for them. In retail, this could mean an AI support agent that handles transactions, returns, and WISMO from start to finish for customers—freeing support teams to focus on high-value, customer-centric tasks like clienteling and brand storytelling.

Agentic AI can operate seamlessly across systems, using APIs to automate and optimize processes with real-time data. For example, an online retailer might deploy an agentic AI system that monitors competitor prices, product engagement, and social media activity, and continuously adjusts its pricing, merchandising, and promotions in the moment to drive sales. Or a grocery retailer could use agentic AI to manage daily and seasonal staffing levels, considering both historical patterns and real-time foot traffic to predict needs with greater accuracy.

Whether used to scale hyper-personalized experiences, optimize merchandising, or improve B2B marketing, agentic AI can reduce costs and inefficiencies for retailers while delivering more timely, relevant experiences that elevate customer experience (CX) and satisfaction.

Why is agentic AI in retail important?

Agentic AI represents a step change in how retail decisions are made and executed. Unlike traditional automation or reactive generative AI, agentic AI can act with autonomy. It perceives its environment, interprets context, processes diverse data, and adjusts accordingly in real time—all within a continuous cycle of observation, reasoning, and action.

This combination of real-time data and process control brings a new level of adaptability to retail operations. Finally, retailers can shift from reactive to dynamic systems that adjust on the fly to changing conditions, whether in the supply chain or the customer experience, for a new generation of intelligent process automation.

Agentic AI in retail goes far beyond automating routine tasks and data-driven processes to improve efficiency. It also transforms support interactions into a growth engine. Rather than simply resolving “Where’s my order?” questions on their own, AI agents can also recognize and capitalize on conversion moments. For example, ecommerce AI agents can make tailored product recommendations, nudge membership sign-ups, or trigger targeted promotions at just the right moment—all without human intervention.

Agentic AI fills a different role in each area of retail operations:

• For customers: Acts as a 24/7 omnipresent AI concierge, guiding product discovery and tailoring experiences across every channel and touchpoint, while gathering interaction data into one unified, evolving customer profile.

• For retail CX teams: Acts as a virtual collaborator, automating routine tasks and surfacing insights so teams can focus on delivering the best and most cost-effective CX.

• For retailers: Offers a scalable solution that simultaneously unites siloed systems, increases efficiency, drives data-driven decision-making, and boosts profitability.

Next, let’s explore how agentic AI differs from other forms of AI in retail—and where it’s already making an impact.

What’s the difference between agentic AI in retail and other AI?

Agentic AI is the latest breakthrough in a set of evolving and complementary AI technologies for retailers. If you’re new to AI in retail, it can be difficult to know the difference between these, so here’s a quick breakdown of how each works and its growing role in retail operations:

Core types of AI in retail

• Predictive AI: Predicts, classifies, and anticipates customer needs based on historical data. For example, Amazon’s recommendation engine analyzes user data for patterns to suggest relevant products.

• Generative AI: Produces content, recommendations, and experiences based on patterns it’s learned, such as marketing copy, chatbot responses, or personalized product descriptions. For example, Amazon’s Rufus chatbot helps shoppers find and compare products via conversational interactions.

• Agentic AI: Acts on behalf of customers or businesses toward a goal using tools and real-time data. For example, Alibaba’s AI customer service agent handles 70% of support interactions, including returns, WISMO workflows from start to finish to elevate CX and streamline operations.

To recap: predictive AI is for insights, generative AI is for content, and agentic AI is for acting in the moment—whether to hyper-personalize experiences, make instantaneous decisions, or scale intelligent automation.

Use cases of agentic AI in retail

Agentic AI in retail offers a solution to many of the industry’s most persistent challenges. In fact, according to Salesforce’s 2025 Connected Shoppers Report, 43% of retailers are currently piloting autonomous AI solutions, while another 53% are actively evaluating the potential uses of autonomous AI.

Use cases for agentic AI in retail vary from fast-to-deploy wins like CX agents to deep, systemic transformations, and these examples show the versatility and impact of agentic AI in retail:

1. Autonomous 24/7 customer support

Traditional chatbots can’t adapt to unexpected issues or resolve complex cases without escalation, which can frustrate customers and erode brand trust. AI agents for customer service, however, can anticipate customer needs based on their profile, tailor interactions in the moment based on context, and resolve issues from start to finish—even make context-rich handoffs if needed.

Example: The AI agent can detect when an order will be delayed, then proactively reach out to the customer with an apology and a discount code. This enhances the customer experience while freeing human reps to focus on nuanced, high-value cases.

2. Dynamic pricing and promotion

Static pricing strategies often leave margin on the table or price customers out due to an inability to account for real-time data. Agentic AI, however, enables retailers to continuously adjust pricing and promotions based on signals such as demand, inventory, competitor pricing, and live customer behavior, helping to capture margin while remaining competitive.

Example: Agentic AI in retail can price up high-demand items at peak times to capture value, while discounting slow-moving inventory to clear stock profitably, or updating promotion items at opportune times to preserve margins.

3. Hyper-personalized experiences at scale

One-size-fits-all marketing can miss conversions, but agentic AI in retail can help. By tailoring messages and offers based on a shopper’s profile and current behavior, location, or session context, agentic AI can deliver the most relevant, actionable experiences at each touchpoint. From product discovery to optimized marketing, ecommerce AI agents can increase conversions, engagement, and loyalty for retailers by making their CX more intuitive and rewarding at every touchpoint.

Example: A loyalty member shopper visits a store and lingers in front of running shoes, so the AI agent for retail sends a push notification with a discount on a best-selling model in their size, plus matching socks for cross-sell. And if the cart gets abandoned, the agent proactively triggers a 10% off promotion before the shopper leaves the cart to help and close the sale.

4. Manage seasonal surges

Support teams can get overwhelmed by Black Friday, holidays, and viral product launches. Without scalable systems, tickets pile up, SLAs slip, and customer frustration mounts. Agentic AI agents scale instantly to handle high volumes when built with the right agent-friendly infrastructure. And with localized AI agents, brands can ensure a consistent tone, accuracy, and resolution quality even across different languages, contexts, or product lines. This keeps retailers agile during peak periods without relying on temporary staff.

Example: A fashion brand drops a limited-edition release that drives a sudden surge of order inquiries. The AI agent handles 80% of them—verifying shipping, resolving payment issues, and explaining return policies—as human teams focus on keeping CSAT scores high throughout.

5. Inventory and supply chain optimization

Stockouts, overstocks, and supply disruptions can reduce margins and erode customer trust. Agentic AI can forecast demand across locations with greater accuracy, identify supply chain risks early, and dynamically rebalance inventory to raise margins—bringing new levels of predictability and precision in inventory and supply chain management.

Example: A grocer’s supply chain management system reroutes shipments for fresh produce around a storm delay, then updates in-store displays based on expected sell-through rates to ensure availability and reduce waste.

6. Autonomous merchandising

Merchandising often relies on historical data or manual updates, but agentic AI enhances these decisions with live data on inventory, current events, and customer behavior. This helps to create more profitable mixes and maximize revenue.

Example: A fashion retailer’s AI agent for retail swaps its homepage banners mid-day to promote products trending on social media, while reconfiguring in-store endcaps with similar in-demand items to drive sales.

7. Real-time omnichannel CX orchestration

Shopping journeys are historically fragmented across apps, websites, and in-store touchpoints—but agentic AI in retail can deliver a single, consistent, context-driven experience across platforms and channels. By analyzing user data and current context against business goals while operating seamlessly across channels, AI agents for retail make omnichannel experiences possible.

Example: A retailer integrates an ecommerce AI agent into its site, mobile app, and in-store kiosks. A customer browsing for hiking boots gets a push notification about nearby store availability. When they enter the store, an in-store AI-powered robot guides them to the boots and suggests related gear. If they leave without buying, the AI emails them an offer for free shipping, providing a more convenient and efficient experience that leads to greater conversion.

Benefits of agentic AI in retail

From better CX to greater profitability, agentic AI delivers a variety of strategic advantages for retailers. By integrating retail AI agents into core functions, businesses can unlock significant benefits like:

1. Context-driven personalization at scale

Retailers have long aspired to deliver 1:1 personalization, but siloed systems and data delays made it impossible to act in real time, resulting in generic or irrelevant recommendations.

But with omnichannel AI agents, retailers can tailor recommendations and content with unified, real-time customer profiles and live context, delivering more timely, relevant shopping experiences that boost conversion, average order value (AOV), and loyalty.

2. Faster, smarter decision-making

Retail teams often rely on guesswork and lagging data to make sales and marketing decisions, which hinders their ability to adapt and preserve margins.

By processing vast and diverse datasets in near real-time, agentic AI empowers retail teams (and connected systems) to make faster, more informed decisions around pricing, promotions, and inventory optimization to lift profitability and protect margins.

3. Improved efficiency and cost reduction

Retail operations often incur costly inefficiencies, whether from overstaffing in slow times, missed demand spikes, or unnecessary markdowns.

Agentic AI brings data-driven precision to retail processes. By continuously adapting to shifting inputs like demand shifts, competitor moves, and traffic patterns, it helps retailers to optimize resource allocation and protect margins without adding to headcount.

4. Enhanced customer experience

Retail customer journeys have long been fragmented across apps, websites, and stores—leading to disjointed interactions that feel generic and often fail to convert.

Agentic AI changes this by enabling tailored concierge-style experiences across every touchpoint. Acting like a personal shopper, AI agents for retail remember each customer’s preferences, history, and context to deliver the most appropriate experiences at every stage. The result is a new level of convenience, continuity, and competitiveness for retailers that reduces friction, drives sales and cross-sells, and strengthens loyalty with a standout CX.

5. Intelligent process automation

Manual interventions in retail logistics, staffing, and marketing are slow and error-prone, leaving retailers vulnerable to delays, inefficiencies, and rising costs.

Agentic AI, however, can recognize impending issues and inefficiencies based on evolving factors like weather, customer activity, or social media mentions, then correct them instantly. Whether acting in the moment to reroute shipments, rebalance staff schedules, or reallocate budget in response to live data, AI helps retailers to minimize disruption and loss.

6. Greater profitability

The competing pressures of margin, customer satisfaction, and operational costs make it challenging for retailers to sustain profitability.

With agentic AI, retailers can strike the right balance by simultaneously optimizing personalization, efficiency, and decision-making with accurate data across functions. This helps to unlock both top-line growth and bottom-line gains. In fact, Bain & Company estimates that AI-driven personalization with AI agents can increase retail revenues by 5–10%.

Best practices of agentic AI in retail

Even with a trusted AI framework, agentic AI projects can stumble without a clear roadmap. To set your initiative up for success, here are some best practices to keep in mind:

1. Start with clear goals

Agentic AI works best when given a well-defined goal. Maybe you want to reduce cart abandonment, optimize marketing, or reduce resolution times. Clear objectives give your AI agent a framework to operate in and help you measure success.

2. Prioritize data quality

AI is only as good as the data it runs on. Make sure you establish processes to ensure data is accurate, comprehensive, secure, and compliant. Without robust data management, your AI risks making irrelevant or wrong decisions that erode trust.

3. Pick your best use cases

Begin with high-impact use cases that deliver real value to customers and your business. Think about the repetitive customer questions you’d love to automate or the operational headaches you’d rather not deal with manually. By piloting agentic AI in these areas, you’ll build trust, prove ROI quickly, and supply AI with sufficient data to perform and improve effectively.

4. Balance autonomy with oversight

AI agents can perform tasks from start to finish, meaning guardrails are needed to ensure they don’t veer off brand or policy. For example, you might set approval thresholds for discounts or have a human approve AI’s suggestion for staffing reallocation. By defining limits on what AI agents do, and where humans are needed, retailers can unlock speed and scale while ensuring AI trust and accountability.

5. Integrate across channels

Customers don’t think in channels; they just want a fast and seamless experience. Deploy agentic AI with the mindset that it should work consistently across web, mobile, in-store, and social. This way, no matter where shoppers engage, the experience feels unified, relevant, and personalized—a true concierge experience.

6. Commit to continuous improvement

Agentic AI isn’t a ‘set it and forget it’ technology. To ensure AI performance and safety, retailers should test and evaluate AI agents, updating them with the latest data to refine them over time. Run A/B tests on specific agentic AI workflows, such as product recommendations, and track KPIs (like conversion or support resolution time) to ensure AI stays aligned with your goals and customer expectations.

7. Prioritize trust and transparency

Agentic AI must feel fair and transparent to customers, or it will be hard to maintain their trust. For instance, this might mean explaining how AI recommends products to put customers at ease Explain why they’re seeing a discount or a recommendation, (e.g., “You got this discount because you’re a loyalty member”), provide opt-outs for personalization where appropriate, and respect their data privacy to ensure ethical use of customer data through compliant, responsible AI frameworks.

Getting started with agentic AI in retail

Agentic AI promises to unlock a new standard in operational efficiency, personalization, and customer satisfaction for retailers. By shifting retail systems from reactive to proactive, agentic AI in retail leads to lower costs, higher profits, and an interconnected retail ecosystem that adapts to customers in the moment.

For retailers facing rising customer expectations and operational complexity, agentic AI isn’t just a nice-to-have innovation. It’s fast-becoming the foundation for the next generation of competitive advantage—where every shopping journey feels personal, every decision is data-driven, and every process runs on proactive intelligence that operates across systems and channels.

If your team is struggling with outdated ticketing systems, repetitive requests, and poor customer experiences, consider Sendbird’s AI agent platform and AI agent for retail.