AI in ecommerce: Best practices & strategies for 2025
When was the last time you browsed an online store and found a product so perfectly suited to your tastes that you couldn’t help but wonder, “How did they know?” Or had a website chatbot answer your questions so seamlessly that you didn’t even stop to consider if it was a human or a machine?
This is AI in ecommerce over the past couple years: responsive, personalized, and increasingly widespread. From recommendation engines to dynamic pricing strategies, AI has become the engine of online retail, driving efficiency and precision at a scale humans can’t match. In fact, adoption of generative AI among business leaders jumped from 55% to 75% in 2024.
AI agents are the latest breakthrough, representing a massive step change for the capabilities of AI in ecommerce. These fully autonomous, decision-making systems use real-time data and tools like APIs to execute complex tasks from start to finish. Combining intelligent action with real-time context awareness, AI agents are poised to further transform the way retailers interact with customers, use data, and optimize operations as part of agentic commerce.
This article explores six ways AI agents are enhancing AI in ecommerce, going beyond generative AI to set a new standard for personalization, operational efficiency, and profitability for online retailers.
Reinvent CX with AI agents
1. Enhanced personalization with real-time context
A full 81% of shoppers prefer brands that personalize their experience, so for retailers it pays to deliver the most contextually relevant interactions across the customer journey. Amazon pioneered the use of AI-powered recommendation engines that track behavior, analyze preferences, and tailor shopping experiences to customers with precision. Similarly, generative AI chatbots can recommend products and content based on the business data they’ve been pre-trained on.
AI agents take personalization to the next level. Instead of only considering historical data, agents also analyze the current context of the conversation, and can even retrieve outside data in real time. For example, a website shopping assistant agent can recommend products based on purchase history and preferences, but also customer sentiment from chat, as well as prior interactions stored in memory, to provide a more accurate and relevant recommendation.
What about cart abandonment? Because the AI agent can perceive that a shopper has stalled on the checkout page, it can then present the shopper with a coupon for free shipping or a discount before they bounce to help close the sale.
Using this connectivity and context-awareness, AI agents deliver the next generation of tailored experiences, effectively adapting to the behavior and preferences of individual customers in the moment to increase sales, customer engagement, and loyalty. What’s more, AI agents also learn and adapt from experience, so they self-improve their performance over time.
In short, AI agentic systems use real-time data dynamically—unlike generative AI chatbots and recommendation systems which are constrained by the limited scope of information in their knowledge base. So whether implemented as a chatbot or in a recommendation system, AI agents can provide the most appropriate experiences and recommendations to improve engagement, conversion, and customer experience.
Learn more: AI agents vs chatbots: Key differences explained
Delight customers with AI customer service
2. Enhanced CX with continuous conversations
Personalization is only half the battle for a better customer experience (CX). With customer expectations ever-climbing, AI in ecommerce is also about keeping customers satisfied post-purchase so they keep coming back. AI agents empower retailers to deliver a more seamless experience that delights and retains customers.
In recent years, generative AI chatbots have enhanced CX and operations by providing customers with immediate 24/7 help across websites, support channels, mobile apps, and more. Using natural language processing (NLP), they can answer FAQs, suggest products, and guide customers—but they cannot maintain the context of a conversation because they have no capability for memory. This creates friction around customer support, requiring returning customers to repeat themselves whenever they switch channels or end a website session.
AI agents eliminate this friction. By storing interaction data in their memory, AI agents can maintain context between conversations with customers—effectively enabling them to pick up where they left off, regardless of the channel. This not only creates a more seamless and convenient experience for customers, but it also reduces workload for human support agents if ticket escalation is ever required, as all context is preserved.
For example, an AI agent chatbot can greet returning customers and pre-populate the chat with relevant information based on recent cross-channel activity, such as return or exchange policies, alternate sizes and colors, or upsells and cross-sells. This creates a delightful sense of continuity for customers, while helping retailers to improve customer service metrics, enhance personalization, and set themselves apart from competitors.
Related reading: What are AI agentic workflows?
3. Conversational commerce with AI voice shopping
Conversational commerce is an emerging battleground in the highly competitive landscape of online retail, as 22% of consumers prefer to use voice-enabled AI assistants like Alexa and Google Assistant instead of typing,
With millions of consumers asking AI to handle their grocery lists, reorder essentials, and discover new products, retailers can implement AI-assisted voice search and shopping on key channels to increase sales and create a more seamless experience that sets them apart from competitors.
AI agents for ecommerce are perfect for voice-assisted shopping and search. Using NLP and voice recognition capabilities, they can guide customers through the shopping process with a live conversation that spans channels, helping to compare prices, features, and even place the order.
AI agents can also seamlessly transition to handling refunds, exchanges, or returns by voice or text—all without human intervention. To streamline this process further, they can incorporate AI-powered visual search or image recognition, allowing customers to upload an image to find products that match the photo, or even initiate returns for past purchases.
As voice search and shopping gain traction among shoppers, ecommerce AI agents can handle these cross-channel interactions from start to finish—delivering gains in sales, operational efficiency, and CX for retailers that gen AI can’t.
Related reading: 9 ways to use AI in customer service
Reimagine customer service with AI agents
4. Better fraud prevention at scale
AI in ecommerce can also protect retailers against fraudulent activity such as chargebacks, account takeovers, and unauthorized transactions. AI-powered fraud detection tools can analyze purchase patterns, behavioral data, IP addresses, and credit card information across millions of interactions and transactions to spot irregularities that indicate fraudsters.
AI agents go further by continuously monitoring the systems in their environment, while processing and analyzing data in real time to better spot irregularities. By serving as the ever-watchful guardian of transaction activity, AI agents enable a more proactive, effective approach to fraud prevention at scale.
Say a customer makes a $1,500 purchase using a new credit card registered in a foreign country. Given this suspicious activity, the AI agent can analyze purchase and behavioral data, then query address verification systems to detect that the shipping address doesn’t match the billing address. The agent then halts the fraudster by requiring additional verification to authorize the purchase, and flags the high-risk transaction for the fraud team.
Learn more: How to build an AI agent: The 8 key steps
5. Optimized operations with multi-agent systems
AI in retail is poised to revolutionize how the industry approaches logistics, supply chain management, and package delivery. By coordinating a team of AI agents as part of a multi-agent system, retailers can optimize complex processes and automate complex workflows that are too much from previous AI technology.
Take Amazon for example. The ecommerce giant uses a variety of specialized AI technology that all work together to predict demand, optimize inventory, and ensure timely delivery across the world.
The AI inventory system tells the recommendation system which items are available to recommend. Meanwhile, the AI delivery optimization system uses the inventory system to plan fulfillment from warehouses where stock is currently available, reducing shipping times and costs while ensuring timely delivery.
This example shows the power of multi-agent systems, but also the value of vertical AI agents—or specialized AI systems tailor-made for specific tasks or workflows in a particular domain or industry. Unlike general-purpose AI like ChatGPT, vertical AI agents are fine-tuned on only domain-specific datasets, allowing them to perform narrow tasks with unmatched precision and accuracy.
By orchestrating a team of vertical AI agents as part of a unified multi-agent system, retailers can improve operational efficiency and profitability by automating complex workflows that have been out of reach until now.
Learn more: What is a multi agent system?
Automate customer service with AI agents
6. Seamless omnichannel experiences at scale
Beyond creating new efficiencies, AI agents act as the bridge between otherwise disjointed enterprise systems and channels—making the elusive dream of a seamless omnichannel customer experience an operational reality for retailers.
By integrating AI agents across the customer journey, retailers can automate customer support at scale. Finally, customers will be able to switch between websites, channels, in-store interactions, and support calls without losing context. This way, when human support agents take over from AI agents, there will no longer be a frustrating pause for customers as teams get up to speed. Instead, all context across all channels has been maintained, delivering seamless customer experience across different languages and regions, translating to greater customer loyalty and turning support into a strategic revenue-generating advantage.
Learn more: Why enterprise-grade messaging infrastructure is key in the AI agent era
AI agents are the future of AI in ecommerce
As AI in ecommerce continues to evolve, it’s shifting from the reactive capabilities of generative AI tools toward the proactive autonomous systems of AI agents that drive agentic commerce. By automating tasks, optimizing operations, and hyper-personalizing experiences, AI agents for ecommerce are soon to be an essential technology for retailers that want to scale high-quality interactions, deliver exceptional experiences, and stay competitive in the marketplace.
If you’re looking to build AI agents for ecommerce, Sendbird can help.
Our robust AI agent platform makes it easy to build AI agents on a foundation of enterprise-grade infrastructure that ensures optimal performance with unmatched adaptability, security, compliance, and scalability.
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