AI in Ecommerce: A Comprehensive Guide + 10 Use Cases

As ecommerce teams look to better leverage AI, what’s most important is ensuring you evaluate AI based on its ability to move core ecommerce metrics, such as conversion, revenue per visitor (RPV), margin, and time-to-launch.

That’s because AI in ecommerce isn’t a generic add-on. The most valuable approaches are AI-first methods and solutions that connect to your catalog and real shopper behavior, enabling continuous product discovery, reducing manual work for your team, and driving measurable business impact.

In this guide, we’ll walk through 10 practical, full-funnel AI in ecommerce use cases — from PDP support and shopping assistants to merchandising insights, Retail Media, recommendations, personalization, and more — so you can unlock AI technology’s full potential for the demands of enterprise ecommerce.

But first, let’s cover the data foundation that determines whether your AI improves over time or stalls.

Your Data Foundation Decides Your Outcomes When Using AI in Ecommerce

Most “AI in ecommerce” conversations skip the core question: What customer data does the AI system learn from, and can you trust it?

Because each enterprise's ecommerce faces unique challenges and business decisions, it’s incredibly important that full clickstream data (a.k.a. 100% real shopper data) is being ingested. Search experiences that are powered by synthetic data will fall short.

Constructor’s engine uses full, verified shopper clickstream, which is a complete record of your shoppers’ interactions that can serve as reliable training and optimization signals throughout your entire search and product discovery experience. From there, reinforcement learning uses those signals (clicks, ignores, purchases) as feedback to improve results over time.

On top of that, Constructor’s discovery reasoning engine leverages that learning to interpret context and product relationships, so the system can choose results most likely to convert, not just match keywords.

This matters to various functions on your team:

• Merchandisers need to prioritize the highest-impact optimizations with limited time and headcount. Better learning signals reduce guesswork and constant manual rule-writing

• Product managers need analytics that are actually actionable, not a pile of dashboards no one has time to interpret

How to Pick the Right Use Cases for AI in Ecommerce

It’s important to keep in mind that AI shows up in two places:

1. Shopper-facing experiences like search results, autocomplete, recommendations, and shopping assistants. These decide what to show and how to help shoppers find the right product faster
   
   
2. Team-facing workflows like product data enrichment, merchandising insights, segmentation, and forecasting. These reduce manual work and help teams focus on what moves revenue

When choosing the right solution, it’s a matter of if you want to improve the shopper-facing experience, team-facing workflows, or both. (It might be useful to consider: Where are you currently feeling the most pain, and which area would cause the most impact?)

In addition to this, another useful way to choose use cases is by funnel impact:

• Top-of-funnel: help shoppers express intent (autocomplete, assistants)
• Mid-funnel: help shoppers compare and refine (search relevance, facets, enriched attributes)
• Bottom-of-funnel: increase confidence and basket size (recommendations, pricing, availability signals)
• Post-purchase: improve retention and cross-channel personalization
• Operations: reduce manual work for teams (insights, enrichment, forecasting)

Below are 10 practical, full-funnel use cases for AI in ecommerce to help you improve product discovery for both your business and customers.

10 Use Cases for AI in Ecommerce

Here’s how to use AI in ecommerce for tangible business benefits:

1. Convert shoppers on a product detail page (PDP)

Shoppers who enter the PDP have demonstrated some level of commitment to the product. They were interested enough to click through, possibly scroll through product specs and reviews, and consider purchasing. The lack of (or unclear) information can be the determining factor in whether they convert.

Enter Constructor’s AI Product Insights Agent (PIA), an AI agent that delivers instant answers to final product questions directly on PDPs, helping get customers over the finish line.

The PIA widget can be embedded anywhere within your PDP template to enable an interactive chat experience where shoppers can ask their own questions or choose from auto-generated “frequently asked prompts” specific to the product being viewed.

It then suggests follow-up questions for both pre-generated and user-submitted queries. These follow-ups help shoppers explore topics more deeply or discover related information.

By proactively surfacing relevant questions, PIA reduces shoppers' cognitive load and helps maintain customer engagement.

Read more about the value of PIA for retailers and shoppers and further understand how it works.

2. Guide intent-aware shoppers toward better-fit product choices

Sometimes shoppers enter a site with a clear intention, but without a specific product in mind (e.g., “I need a birthday gift for my 10-year-old niece who loves building things”). This is when a strategically placed AI solution, such as the AI Shopping Agent (ASA), can help.

Consider ASA your helpful, always-on store associate with deep domain knowledge of your industry and products in your catalog. Not only this, but it’s highly tuned to the customer at hand, thanks to using full, verified clickstream data as ranking signals. As soon as a shopper enters a prompt, ASA uses natural language processing capabilities to maintain the conversation and guide them toward conversion.

ASA works across categories as well, proving useful from verticals like furniture & home goods to general merchandise. And it can be branded to fit unintrusively into your current site and its branding.

Basically, by reducing mental friction, the AI agent acts like an assistant that bridges the gap between intent and purchase.

3. Better understand and optimize merchandising decisions

Following two shopper-facing agents (AI Shopping Agent to help with product discovery, and Product Insights Agent to help shoppers surface detailed product information), Merchant Intelligence Agent (MIA) is the third agent in Constructor's suite of agents, helping retailer merchandisers to understand and optimize their merchandising decisions.

Merchant Intelligence Agent provides clear, contextual explanations for search and merchandising outcomes by helping them understand the relationships between user data, search configurations and AI decisions.

Merchandisers can ask MIA in natural language why results look the way they do and receive instant answers to shed light on the cause and effect across rankings, performance, and system settings

In addition to explaining, MIA can also propose improvements to help achieve desired system behavior and performance goals. Suggested changes still allow for human review, preserving merchandiser control and approval.

For more on MIA, feel free to reach out.

4. Show the right ads to the right user at the right time

Traditionally, retailers have had to walk a fine line when it comes to placing sponsored ads, generally balancing advertisers' needs over shoppers'. This approach not only leaves ad money on the table, but it also taints the customer experience by serving up irrelevant results.

Now there’s a better solution, one that serves the right ad to the right person at the right time.

Unlike traditional Retail Media approaches that treat sponsored products as a separate system layered on top of ecommerce, AI-native Retail Media solutions integrate advertising directly into the customer experience.

They start where shopper intent is strongest: search, product discovery, and browsing behavior. And thanks to access to real-time clickstream data, behavioral signals, and intent patterns that most RMNs simply don’t see, that foundation enables Retail Media to be optimized not in isolation, but as part of the same decisioning engine that already determines which products shoppers are most likely to engage with and purchase. More importantly, this decisioning engine always keeps the retailer’s primary business metric front and center.

Discover more about how optimized search and product discovery is the hidden retail media revenue engine without compromising customer satisfaction.

5. Fuel product recommendations

With a machine learning-powered product recommendations engine, merchandisers can more easily make targeted, on-brand product suggestions throughout a customer’s journey.

By tapping into real-time data, ecommerce teams can program the AI to surface product recommendations that prioritize their ecommerce business objectives, like revenue per visitor (RPV), profit margin, or abandoned cart rate. They can also place recommendations where shoppers are likely to interact, like the homepage, category pages, pop-ups, emails, and more.

Depending on your recommendations engine, each product interaction then fine-tunes the rest of a customer’s product discovery experience. For example, if a customer shows affinity for a certain brand in a “You Might Also Like” recommendations pod, products from that brand rank higher on category pages, in search results, in Collections, and across your ecommerce site.

For more on how to set up and maintain a recommendations engine flywheel, check out our ultimate guide to recommendations.

6. Opt for segment-of-one over basic segmentation with personalization

Retailers are at very different stages of the personalization journey. Some are still relying on manual rules and basic segmentation. Others have invested early in AI-powered personalization engines. But across the maturity spectrum, the results have often fallen short of the promise.

Even with modern platforms, many retailers still struggle to realize meaningful gains.

This failure isn’t about lack of vision or effort. The industry simply hadn’t grown up yet. Today, that’s no longer the case — retailers finally have a practical path out of the personalization trench. And we cover it in the newest edition of our Building Blocks Series, The Personalization Maturity Curve.

We take you through the complete personalization technology timeline: from rules to real-time through the five stages of the Personalization Maturity Curve. You’ll learn why early generation solutions failed to deliver, and how today’s advanced, adaptive solutions finally close the gap.

We also offer a self-assessment tool, where you’ll be able to pinpoint where your organization sits today, and discover why you don’t need to take an incremental approach to maturity. Even if you’re still using manual rules or segments, we’ll show you how to leapfrog to real-time, responsive merchandising in one step.

7. Provide a holistic discovery experience

AI tools are powerful for improving on-site customer experiences. With their use, retailers can now also create and launch data-driven marketing campaigns that show the right product or message at just the right moment, providing a connected omnichannel discovery experience. This is thanks to Constructor’s AI-native Cross-Channel & Offsite Discovery solutions.

Constructor’s engine uses on-site signals and customer behavior to drive 1:1 personalized shopping experiences across your full marketing ecosystem — from email, SMS, and paid media to mobile push and in-store kiosks.

They help ecommerce teams not only deliver tailored recommendations, but also optimize search, discovery, and engagement across both digital and physical touchpoints — all while aligning with business goals and campaign strategy. (Constructor’s email recommendations have proven to increase sales by over 320%, for instance.)

And, like other Constructor products, this is part of our reinforcement learning, which interprets shopper actions — what they click, ignore, or buy — and feeds them into a loop that strengthens the system’s intelligence over time, building a deeper understanding of what drives outcomes with every session.

8. Enrich product data

Product data is the fuel that powers your ecommerce site, shaping whether customers leave satisfied or frustrated with their experience. If your plan is to capture lasting brand loyalty, you can’t afford to bench data enrichment efforts.

Thanks to AI, your ecommerce team doesn’t have to go it alone.

Attribute Enrichment powered by Generative AI automatically helps customers find what they’re looking for faster, while exposing them to a broader range of products. This AI solution leverages text and image data from your catalog, along with full, verified clickstream data, to dynamically update attributes and categories in real time at 1:1 scale.

This personalization makes search, filtering, and product discovery less frustrating for shoppers — and is a must-have capability for ecommerce teams looking to reduce their workload, improve customer satisfaction, and still hit key business goals.

9. Manage inventory and forecast sales

With ever-evolving product catalogs, changing customer demands, and occasional supply chain disruptions, knowing how much inventory to keep on hand can be a lot to handle manually.

With an AI platform specifically designed for ecommerce inventory management, you can take the guesswork out of your inventory management process. Deep analysis of buyer data, such as buying behavior and seasonality, enables you to more accurately predict and plan stock levels.

Using AI for inventory management and sales forecasting can help you:

• Analyze previous, current, and projected sales throughput.
• Predict, report on, and quickly solve vendor issues.
• Predict changes in customer demand.
• Analyze market changes that could affect sales.

All of this can help your business better predict demand and meet it with the right product at the right time.

10. Set dynamic pricing

Just as surfacing the right product to the right customer creates interest, matching that customer with the right price drives the sale home.

Here are some clever strategies that ecommerce businesses — including B2B vendors — are using to set dynamic pricing:

• Predicting the best price for each product. Just as AI-native solutions can alter product rankings, they can also adjust the pricing of certain items based on how likely users are to purchase them at specific price points. (This includes the highest price point customers are willing to pay.) This strategy takes a macro view of customer interactions and sets the global price for that product to maximize profit or revenue
  
  
• Predicting the best price presented to each customer. By analyzing which products an individual clicks, adds to their cart, and purchases, AI can adjust an item's price to increase the likelihood of a conversion for that customer. For example, AI may choose to entice first-time purchasers or price-sensitive customers with lower rates
  
  
• Showing the best price across accounts and buyers. In B2B ecommerce, there are many variations of products, restrictions, and/or conditions to consider for each account when pricing products. With the right personalization software, you can customize how buyers across accounts interact with your products and the prices they see. You’ll also be able to govern contracts to support product assortment and account-based pricing

How to Succeed with AI in Ecommerce

AI can support almost every part of ecommerce, but the teams that see real impact stay focused on two outcomes: helping shoppers find and choose products faster and giving time-strapped teams back time.

You don’t have to turn your entire tech stack up on its head at once. Start with one high-leverage use case where you already have volume (like search, recommendations, or PDP questions). Tie it to a clear KPI, test changes, and iterate.

That’s how AI stops being “interesting” and starts being measurable.

Not sure where to start? Receive a complimentary search experience audit that outlines low-hanging fruit that you can action on today for immediate results.

Frequently Asked Questions

What does “AI in ecommerce” actually mean?

It refers to using solutions and methods powered by machine learning algorithms (and more broadly, AI algorithms) to improve shopper experiences (like search, autocomplete, recommendations, assistants) and team workflows (like product data enrichment, insights, segmentation, and forecasting).

What’s the most important requirement for AI to work well in ecommerce?

A strong data foundation. AI outcomes depend on what the system learns from and whether those signals reflect real customer behavior on your site.

What is “full, verified shopper clickstream,” and why does it matter?

It’s a complete record of shopper interactions (clicks, ignores, purchases). Those signals can be used to train and optimize discovery experiences over time, improving results beyond simple keyword matching.

How do reinforcement learning and “reasoning” show up in product discovery?

Reinforcement learning uses behavioral feedback to improve results over time. A reasoning layer helps interpret context and product relationships so ranking decisions better reflect what shoppers are likely to buy.

Which AI use case should most retailers start with?

Start where intent is strongest, and impact is easiest to measure: autocomplete, search results, recommendations, or PDP support (answering last-mile product questions). Many of our customers have seen >2X CVR with the AI Shopping Agent.

How should you measure whether an AI initiative is working?

Use ecommerce KPIs tied to the use case: conversion rate, revenue per visitor (RPV), margin, time-to-purchase, zero-results rate, reformulation rate, or add-to-cart rate (depending on the workflow), to name a few. To reliably improve those outcomes, ensure your AI is fully connected to the ecommerce system so reinforcement learning can use verified feedback (clicks, skips, purchases) to optimize over time.

How does AI improve Retail Media without hurting the shopper experience?

When Retail Media is integrated into the same decisioning engine that powers discovery, ads can be placed based on intent and behavior patterns. This optimizes revenue while keeping relevance and the retailer’s primary business metrics front and center.