AI for Ecommerce: How It’s Transforming the Future

Ecommerce is one of the leading adopters of artificial intelligence (AI), with use cases from personalized product recommendations and enhanced customer service to streamlined workflows, smart logistics, and sales/demand forecasting. Organizations that adopt AI business strategies generate an average of 10-12% extra revenue.

With more and more consumers gravitating toward online shopping (21% of retail purchases in 2025 will be made online), it’s more important than ever for ecommerce brands to adopt AI if they hope to keep up with consumer expectations. 

In this article, we look at how AI allows retailers to evolve their customer journeys and create personalized experiences that keep shoppers coming back for more. We’ll also consider how AI helps with internal operations to improve overall competitiveness, as well as look ahead at what the future of ecommerce looks like for those who successfully adopt AI.

Main Types of AI for Ecommerce

Before we continue, consider these three incredible statistics related to AI in ecommerce:

• By 2032, the ecommerce AI market is expected to reach $45.72 billion 
• 84% of ecommerce businesses place AI as their top priority
• AI for ecommerce delivers more than a 25% improvement in customer satisfaction, revenue, or cost reduction

These statistics help demonstrate the growing importance of AI in the ecommerce industry, and the potential benefits it can provide for both businesses and consumers. AI can help businesses have better customer interactions and deliver personalized recommendations, while also streamlining internal processes to make shopping more efficient. 

Let’s learn more. 

Personalized Product Recommendations

Just as the way people shop has changed, so have their expectations. Today’s customers want to receive a personalized experience when shopping online, and when retailers deliver that experience, they’re rewarded with a 40% increase in revenue. With only 1 in 10 retailers admitting to fully implementing personalization across all channels, it’s a huge untapped opportunity. 

So, how does AI deliver personalized product recommendations?

AI distills insights from past customer behavior data — such as searches, clicks, and purchases — by feeding it into data-filtering tools, which use algorithms to recommend the most relevant items to a particular customer. 

It’s most often seen on websites, where retailers highlight sections that are “Inspired by your shopping trends,” suggest related add-on items in a cart, or share location-relevant content based on where the customer is.

Personalized product recommendations enhance the shopping experience because they help customers find what they want quickly, as well as suggest additional products they may benefit from.

From the retailer’s perspective, it can greatly boost customer loyalty and provide opportunities to cross-sell or upsell. According to research by McKinsey, the business impact of using AI for personalization alone results in:

• 10-30% more efficient marketing and cost savings
• 3-5% increased customer acquisition
• 5-10% higher satisfaction and engagement

Smarter Searches

In addition to personalized product recommendations, AI also enables retailers to understand the intent behind a shopper’s search query. When the average ecommerce bounce rate is between 20-45%, smarter searches are shown to reduce this number by offering up more relevant results.

So, how does AI search for ecommerce enable personalized searches that know exactly what a customer wants?

Like personalized product recommendations, AI identifies patterns in both online and offline data to understand customer intent. Machine learning algorithms take the analysis further to make the data contextual. For example, if a shopper searched for “hats,” and the AI was able to determine they have an upcoming wedding, it might return results for fascinators rather than woolen winter hats.

Furthermore, because AI technology is constantly “studying” the user, it learns about individual preferences and can make more accurate recommendations. So, for a search like “best holiday clothes,” the results would show clothing from the shopper’s favorite brand(s) and appropriate for a city break, which has just been booked.

This hyper-targeting can tackle every retailer’s nightmare: abandoned carts. Globally, the average shopping cart abandonment rate is 70.19%. Through smarter searches, which understand the shopper’s intent, retailers can show the right product in the right place and at the right time to prevent abandoned carts and boost conversions.

Logistics and Forecasting

While AI offers a lot to enhance the customer experience, it can also have a significant business impact behind the scenes. As the adoption of ecommerce continues to spread, retailers can use AI to manage the complexities inherent to their operations by optimizing warehouse processes and revolutionizing supply chain management.

For AI to support logistics and forecasting, it’ll pull data from various sources, including transactional data, behavioral data, demographic data, and ecommerce data (e.g., how a customer reacted to a promotional email). By applying techniques like machine learning, data mining, optimization algorithms, and neural networks, retailers can analyze vast amounts of data in real time to identify patterns and make predictions. This is particularly useful for AI use cases in ecommerce like:

• Inventory management — analyzing historical sales data, current market trends, and social listening insights to generate accurate demand forecasts 
• Seasonality predictions — accurately predicting demand for one-off, infrequent, or annual events (like Black Friday)

Additionally, AI allows retailers to automate repetitive tasks and streamline workflows, which can significantly reduce the time and cost associated with warehouse operations. This is helpful for things like:

• Supply chain management — McKinsey research shows AI adopters have improved logistics costs by 15%, inventory levels by 35%, and service levels by 65%
• Delivery — 99% of consumers say fast delivery is important when making online purchases, which is why 42% of retailers are working on how to offer same-day delivery

Conversational Shopping

AI-powered chatbots currently handle 70% of online customer conversations. However, following the launch of generative AI, the value of the ecommerce sector has ballooned to $5.92 trillion as retailers rush to level up their current chatbots with new functionality. Generative AI in ecommerce can now power conversational commerce online, which frees up commerce-driving teams to work on less menial tasks. 

For shoppers, AI assistants will respond to more complex queries at any time of the day or night, share product recommendations based on retargeting campaigns, and provide real-time updates for accurate package tracking. And, advanced conversational shopping solutions will go beyond the chat box and engage your customers across your entire site, from product pages and the checkout page to directly within the search bar. 

AI’s deep learning algorithms can determine individual preferences to provide appropriate recommendations. For example, by analyzing customer reviews, the technology could understand that garment sizes run large and recommend a shopper purchase a size down as they try to add a new sweatshirt to their cart. 

Similarly, conversational shopping assistants trained with natural language processing (NLP) can tailor recommendations to a specific shopper at a specific point in the buying journey. Imagine a parent is booking a party for their child’s birthday. The assistant could recommend they also book a cake now to avoid disappointment, share details of a local bakery, and even suggest various styles of numbered candles to coincide with the child’s age.

The Big Trend To Look Out for in AI Ecommerce

The biggest trend on the horizon? Agentic AI. AI agents are essentially digital assistants that can work autonomously. They use large language models (LLMs), NLP, and machine learning to not only carry out tasks, but also reason and learn to optimize their processes. 

AI agents are already transforming the way brands personalize the shopping experience, as well as how teams work. By giving agentic AI a goal, they can figure out the best way to reach that goal without needing constant manual intervention. In other words, AI agents free your teams up to focus on more strategic, high-impact areas instead of getting stuck in the weeds. 

Agentic AI is powering conversational shopping, but it also does so much more. It can hyper-personalize search results by taking every click, search, purchase, return, and preference into consideration. It can personalize your marketing campaigns in real time across every channel. And, it can help your marketing team autonomously create marketing campaigns in a fraction of the time while also generating actionable insights and identifying key opportunities. 

The Future of AI in Ecommerce

In order to embrace AI in a meaningful way for your business, you’ll need an all-in-one agentic platform like Bloomreach. Whether you need AI-driven autonomous search, autonomous marketing, or conversational shopping, our agentic AI technology enables you to deliver customer experiences so personalized, they feel like magic. 

Here are some of the impactful results that ecommerce brands have driven with Bloomreach:

• The Thinking Traveller increased booking inquiries by 33%
• 4Home achieved 800% ROAS (return on advertising spend) 
• Bensons for Beds increased ecommerce sales by 41% YoY