AI in ecommerce: Everything you need to know (Use cases + best practices)

Understanding AI in ecommerce

With over 33% of the world’s population now shopping online, there’s increasing demand for flexible, convenient, and seamless shopping on a global scale. Faced with rising customer expectations, costs, and operational complexity, retailers must deliver exceptional experiences to cut through the noise and win loyal customers. This is where AI shines.

AI allows online retailers to analyze and leverage vast amounts of data in real-time to improve operational efficiency, increase sales, make data-driven decisions, and gain a deeper understanding of customers—all while delivering a more personalized, seamless shopping experience across channels.

It's a win-win for shoppers and retailers, so it’s no surprise that over 70% of ecommerce businesses are now using AI in some form. These technologies are complementary when integrated effectively, and fall into three main buckets:

Core types of AI in ecommerce

• Predictive AI: Analyzes historical data like customer behavior, preferences, and purchase history using machine learning (ML) algorithms to identify patterns and make predictions.

  • Use cases: Demand forecasting, inventory optimization, dynamic pricing, audience segmentation, personalized product recommendations

• Generative AI: Interprets customer queries and automatically creates a contextually relevant output in any language using natural language processing (NLP) and ML.

  • Use cases: Customer support chatbots, automated content generation website content, marketing campaigns, and automated product descriptions

• Agentic AI: AI agents are proactive systems that perceive their environment and leverage real-time data via APIs and tools to solve problems on their own. They expand AI's scope of information and functionality to use both historical and real-time data in dynamic ways.

  • Use cases: All the above. AI agents are complementary technology that enables a new generation of context-driven personalization, process automation, and actionable business intelligence.

By integrating these intelligent technologies into existing functions, retailers can create more contextually appropriate and relevant buying experiences, automate complex tasks, and optimize operations to drive growth at scale.

Learn more: What is agentic AI? 

15 key AI use cases in ecommerce

How do you use AI in ecommerce? Research shows that AI can boost customer satisfaction, revenue, and cost savings by up to 25%, but knowing where to start can be challenging because the technology is evolving rapidly.

Here are the most impactful AI use cases in ecommerce:

1. Hyper-personalized product recommendations

Personalization is a key driver of customer engagement and loyalty, as a full 81% of shoppers favor brands that tailor their experience. AI-driven recommendation systems use machine learning (ML) and predictive analytics to track behavior, analyze preferences, and tailor shopping experiences to shoppers. Meanwhile, generative chatbots use NLP to recommend products to shoppers  based on the business data they’ve been pre-trained on.

AI agents go a step further, tailoring the individual CX based on past and present data to deliver hyper-personalized recommendations. For example, a website shopping assistant agent can recommend products based on purchase history and preference, but also the current context of the conversation, user behavior, and even prior interactions stored in memory—providing a more accurate, relevant recommendation that increases the likelihood of conversion.

By upgrading chatbots to AI agents, retailers can deliver the most contextually appropriate and relevant buying experiences based on up-to-the-second data to improves engagement, sales, and customer satisfaction.

Key AI tech: AI-driven recommendation engines like Qubit, Klevu, or Amazon Recommendations AI; AI agent builders like Sendbird

Learn more: AI agents vs chatbots: Key differences explained

2. Proactively curb cart abandonment

Cart abandonment is the scourge of online retailers, but AI agents for ecommerce can help. Combining context awareness with goal-oriented behavior, AI agents can proactively trigger personalized offers at checkout to reduce cart abandonment.

For example, an AI agent can perceive when a shopper has stalled on the checkout page, then proactively trigger a coupon for free shipping or a discount in the split second before they bounce to help to close the sale.

What if the customer does bounce? Omnichannel AI agents can follow up with customers across preferred channels like chat, email, or social media, nudging them with relevant cart reminders and marketing promotions to help salvage the sale.

Key AI tech: AI agent platform

3. Automated customer service with AI agents

Chatbots are a widely adopted form of AI in ecommerce, able to handle around 70% of customer interactions on their own. That said, AI agents are the next generation of shopping assistants, offering a more seamless support experience without the usual friction.

Like chatbots, AI agents use NLP to understand and respond to customer inquiries in a conversational way with accurate, relevant responses. Available 24/7 on websites and support channels, agents can provide immediate support, answer FAQs about shipping and returns, recommend products, even execute tasks like transaction processing.

Unlike chatbots, AI agents can remember past interactions with customers. This enables them to hold a single continuous conversation with customers that spans channels, eliminating the friction of playing catch-up with returning customers. By maintaining context between interactions, AI agents enable a more convenient, efficient service experience that elevates customer satisfaction and loyalty— while freeing human agents for more complex tasks.

Key AI tech: AI agent builders 

Related reading: 9 ways to use AI in customer service

4. Dynamic pricing optimization

AI helps retailers to maximize profits by optimizing prices to current market conditions. Using ML algorithms, AI-driven pricing tools analyze internal and external factors like product availability, demand history, production cost, and average online price to dynamically set prices for optimal profitability.

For example, Amazon uses AI-powered dynamic pricing tools to optimize prices as often as every 10 minutes, helping the retail giant to increase profits by 143% annually on average. In effect, if demand for a certain product spikes, AI bumps up the listed price automatically. Or when a competitor slashes prices, AI drops the price on your site to match.

With an AI-driven pricing optimization strategy, retailers can improve inventory management, optimize personalization, and maximize revenue in the face of ever-changing market conditions.

Key AI tech: Dynamic pricing tools like Price2Spy, Repricer, or Pricefy

5. Conversational commerce with AI voice-assisted shopping

Voice-assisted shopping and search are gaining traction among shoppers, with 22% of people saying they prefer voice-enabled AI assistants like Alexa or Google Assistant to typing. By integrating NLP and voice recognition technology into websites and support channels, retailers can offer a more convenient experience that drives sales among the millions of consumers who already use voice-enabled AI to shop and discover new products.

AI agents are perfect for voice-assisted shopping and search. They can guide customers through the shopping process from start to finish with a live conversation, seamlessly shifting from comparing prices to processing transactions to handling returns or exchanges. And since AI agents for ecommerce are omnichannel, if the customer switches channels, all previous shopping activity is preserved—helping to increase sales, operational efficiency, and CX in ways gen AI chatbots can’t.

Key AI tech: Voice recognition AI, natural language processing, AI agents or chatbots

6. Smarter search capabilities

AI enhances the search capabilities of websites and mobile apps, making it faster and easier for shoppers to find the products they want. Retailers can integrate the following AI technologies to supercharge their product discovery process:

• Natural language processing (NLP): Enables site search engines to understand the meaning and intent behind a user’s search query in any language to deliver the most relevant results—even if the search query contains variations like typos, synonyms, or slang.

• AI-assisted visual search: Enable customers to search for products by uploading an image instead of typing. Employs AI algorithms and image recognition tech to suggest items similar to what’s pictured, improving discovery among customers that don’t know the product name.

• Context-based search: Uses AI algorithms to scan external data like trending products, seasonality, and day of the week to make more relevant search results. Can also auto-fill search phrases and suggest related products to increase the likelihood of a purchase.

Key AI tech: NLP, context-based search, image recognition AI

7. Improved customer segmentation and marketing

Audience segmentation is essential to effective personalized marketing, and AI in ecommerce can help. Using ML, AI-driven tools for customer segmentation analyze user data and identify patterns across datasets, then create accurate customer segments based on shared characteristics—even discovering entirely new segments and characteristics that may have been overlooked.

This enables retailers to tailor marketing strategies and target campaigns with surgical precision, ultimately reaching, engaging, and converting audiences more effectively. In fact, well-segmented marketing campaigns have been shown to drive a potential 760% increase in revenue.

Key AI tech: AI-driven customer segmentation tools like Salesforce Marketing Cloud, Klaviyo, or BlastPoint.

8. Intuitive upselling and cross-selling

AI can anticipate which products will appeal to customers and recommend these items in opportune moments to drive upsells and cross-sells. Combining ML algorithms with predictive analytics, AI-driven recommendation systems analyze past purchases, preferences, and browsing behavior to tailor experiences across websites, chatbots/shopping assistants, and mobile apps to the tastes of individual customers.

Dynamic website content is an example of this AI use case in ecommerce, such as the “You might also like” sections on websites or the “Frequently bought together” product bundles on checkout pages. In fact, 63% of consumers say AI-powered product recommendations are highly influential on their purchases.

Key AI tech: AI-driven recommendation engines like Amazon Recommendations AI, Qubit, or Klevu.

9. Auto-generated content

With generative AI for ecommerce, retailers can automatically create highly tailored content that drives greater engagement and sales—but at a fraction of the time and cost. Based on the customer profile and current behavior, gen AI tools can create a variety of content tailored to specific experiences and audience segments, such as:

• Product descriptions

• Marketing content

• Dynamic website content

• Personalized product recommendations

Combining predictive analytics with NLP, these tools quickly create copy that’s compelling, unique, and contextually relevant. This improves the consistency of the customer experience and enhance personalization, helping to drive sales and customer satisfaction.

Key AI tech: Generative AI tools like ChatGPT, Copy.ai, or Jasper

10. Fraud detection and prevention

Protecting retailers against fraudsters in real-time is another key AI use case in ecommerce. AI-powered fraud detection tools use advanced ML algorithms to analyze transactions, behavioral data, IP addresses, credit card information, and more to detect complex patterns and irregularities that suggest fraudulent activity.

Going a step further, AI agents can take action to halt fraudulent transactions in the moment. For example, a vertical AI agent for fraud prevention on a website can require additional verification from suspicious buyers before authorizing a purchase, while flagging the high-risk transaction for the fraud team.

This proactive, real-time approach to fraud prevention helps to reduce financial losses for retailers and maintain shopper confidence by not disrupting the everyday shopping experience.

Key AI tech: Fraud detection AI tools like Kount, Featurespace, or Darktrace; AI agents specialized for fraud detection

11. Fake review detection

Customer reviews are key to building trust and driving sales, as 93% of online shoppers read reviews before making a purchase. However, research shows that around 30% of online reviews are fake, which can seriously erode trust and conversions if left up.

Using NLP and machine learning, AI can quickly analyze text patterns, writing styles, and formatting to identify and flag suspicious reviews. Amazon, for example, uses advanced language models and big data analytics to evaluate seller reviews, considering data points like previous reports of abuse, review histories, and even ad investments (which can incentivize fake reviews).

Key AI tech: NLP and ML; AI tools like FakeSpot

Best practices for AI in ecommerce

If you’re considering how to implement AI in your ecommerce business, it’s a good idea to create an AI strategy that lays out plans for implementing, monitoring, and optimizing AI. For now, here are some best practices to guide you on your journey to AI-driven ecommerce:

1. Define your objectives

Start by defining what you want to achieve with AI. Whether your focus is improving CX, automating customer service, or optimizing inventory levels, having clear goals will help you choose the right AI tools, manage scope, and measure success.

2. Focus on data

AI requires huge amounts of high-quality business data to function effectively, so ensure your datasets are accurate, complete, and relevant to your use case. You may need to establish data collection and data management systems to clean and pre-process data so AI can use it to produce accurate, relevant outputs.

3. Consider integrations and APIs

The ecommerce ecosystem includes a growing set of AI tools and components that each have a specific function, many of which are connected by APIs or native integrations. Integrating AI with existing systems is already a complex process, but without these connective elements, it becomes more time- and resource-intensive, if not impossible.

4. Assemble an expert team

You'll need the help of various AI experts to effectively manage your AI project. For example, data engineers to verify data cleanliness, and machine learning engineers to design AI models. Whether you hire in-house, upskill employees, or outsource to white-glove AI agent platform, recruit for the specific programming languages and AI tools in your project plan.

5. Monitor and optimize

It’s important to regularly monitor AI, track key metrics, and make adjustments to optimize its outputs for accuracy, relevance, and compliance. Gather customer feedback to inform improvements, and track performance to ensure AI tools are performing at their best.

6. Safeguard user data and privacy

AI in ecommerce handles a lot of sensitive customer data, so implement robust security and data privacy measures to protect users, stay compliant with relevant data protection regulations, and uphold customer trust.

AI in ecommerce is becoming an industry staple

With 84% of retailers saying AI is their top priority—the ecommerce landscape is rapidly transforming into a digitally interconnected ecosystem that centers around AI. 

As AI becomes essential to success, AI agents are emerging as a game-changer. These systems go beyond generative and predictive AI, leveraging real-time data to automate complex tasks and deliver personalized, proactive customer interactions that will set retailers apart.

In the future, AI agents will become more specialized, with vertical AI agents tailored to specific industry use cases and workflows. Additionally, the rise of smaller, more focused language models will lower the cost of creating tailored AI solutions. With AI becoming cheaper, more utility-specific, and mission-critical by the day, retailers must adopt these technologies to remain competitive.

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.

If you want to learn more about the future of AI, you might enjoy these related resources:

• AI in ecommerce: How AI agents improve CX, personalization, and operations
• 9 ways to use AI in customer service in 2025

• Rise of the A2A economy: How AI agent-to-agent interactions will reshape the world