E-commerce and retail are among the clearest places where AI delivers quantifiable value, since the business itself is essentially a large collection of predictions and trade-offs. What this customer clicks on next. What price will they take today? What Inventory needs to be where and when? Which orders will be returned? Which shipments are late? Which items are quietly disappearing from the shelves or from a node of fulfillment? Which support tickets will escalate? Which promotions will cannibalize margin rather than grow volume?
But it is also one of the easiest places to do things with AI that aren’t helpful, because the incentives are strong and the data is rich. Conversion can be optimized, and trust can be broken at the same time. You can maximize price and invite regulatory scrutiny. You can optimize inventory, and you can have stockouts because the model is “right on average,” but wrong at the tail. You can use automation to serve the customers and produce confidently wrong answers at scale.
So this article performs two functions. It provides an explanation of 10 categories of AI solutions that consistently provide ROI for e-commerce and retail (both online and omnichannel) and describes in detail how each one works, what data it requires, what a success metric looks like, what commonly goes wrong, and how to transition from a production system to a demo.
I’ll be blunt about one thing right upfront: in retail, the model is rarely the difficult part. The challenge is the decision system around it. Instrumentation, experimentation, feedback loops, monitoring, governance, and integration with actual workstreams.
Personalization is the “parent category” for many retail AI successes because it influences nearly every lever of revenue generation: product recommendations, ranking, email, push notifications, homepage modules, category sorting, bundles, loyalty offers, and even support content. When it’s done right, it increases the conversion, average order value, and repeat purchase rate as it alleviates the cognitive load of shopping. When done badly, it becomes noise, or worse, invasive noise.
The key takeaway is that personalization is not a single model. It is an orchestration layer that determines which experience to present based on user actions, context, and business policies. Sophisticated algorithms weigh several factors: long-term preferences (brands, price range, size, style), short-term intent (what they searched for on this site), context (time, device, location), and constraints (product availability, shipping windows, budget, risk of returns). The simplest case is “people who viewed X also viewed Y.” The functional equivalent is “for this user, in this situation, with these limitations, show a ranked list of items that results in the maximum probability of a good outcome.”
A helpful research-based framing is that personalization is usually beneficial, but the dispersion is large, and execution maturity influences this. McKinsey has aggregated results showing that personalization typically results in a 10–15% revenue lift (although there is significant variation across industries and maturity levels) and can also increase marketing ROI and reduce cost of acquisition when implemented correctly. That “if done right” is hiding a lot of work. The reason it varies so much is that personalization goes wrong when it’s trained on noisy events, released into the wild with no guardrails, or optimized for the wrong metric (clicks instead of long-term value).
A convenient way to think about personalization is to think about three layers. The first is candidate generation, in which you produce a few hundred plausible items in a matter of milliseconds from a massive catalog. The second is ranking, where you order those items for this user at this moment. The third is re-ranking with constraints, where business rules like “don’t show out-of-stock items,” “don’t show fragile items if the shipping promise is tight,” “respect category diversity,” “don’t show the same brand 10 times,” “don’t show items with high return probability,” and “protect margin” are applied. This is why a team that talks only about “a recommender model” usually hasn’t implemented a true retail personalization system.
Success is not “offline accuracy” in production. It’s lifted online, in controlled experiments, in conversion rate, revenue per session, add-to-cart rate, average order value, repeat purchase, and sometimes long-term metrics such as customer lifetime value. You also want negative metrics: bounce rate, time-to-first-click, complaint rates, and opt-out rates for customization.
One more issue that should be considered fundamental rather than optional by researchers and product teams: personalization is closely tied to “surveillance pricing” issues if personal data is used to determine prices or offers on an individual basis. The FTC in July 2024 initiated a market study using 6(b) orders examining “surveillance pricing” intermediaries and the potential role of personal data in individualized pricing. In January 2025, FTC staff released an issue spotlight and press release on how personal data, including detailed behavioral signals, may be used to personalize prices and highlighted concerns around transparency. That is true even if you’re “just personalizing content,” because teams tend to migrate from content personalization to offer and price personalization and lose track of what the governance implications are.
A key implementation detail that distinguishes mature teams from less mature ones is how they deal with feedback loops and bias. Event logs in retail suffer from selection bias as well, since you only see clicks for items you displayed. If you train on clicks and don’t control for exposure, you reinforce popularity, and that drives the catalog into a small number of products. You need exploration and experimentation, and not just exploitation. Usually, this means some degree of controlled randomization in small doses and close monitoring so you can learn without damaging customers.
In e-commerce, search is very often the highest-intent surface, and even minor gains compound into massive revenue. The thing is, the intent of the customers is chaotic. Users search with fuzzy terms (“wedding guest dress”), incorrect terms (“charging cable for iPhone 15” while what they really want is USB-C), or multiple constraints (“black shoes, wide fit, under $100, next-day delivery”). When you have synonyms, long-tail queries, and ambiguous intent, classic keyword search fails.
Contemporary search AI consists of three main components. The first is intent detection, usually using embeddings and transformer models to represent queries and products in a common space. Second is retrieval, which finds candidates fast. Third is ranking, which sorts the results based on an aggregate score of relevance, personalization, inventory availability, delivery promise, and business constraints. Good teams treat search as a learning system, continuously collecting judgments of relevance for queries/results. Every query is training data if you’re measuring outcomes honestly.
One very obvious real-world play is Amazon’s bet on generative AI shopping assistants and AI-enabled discovery. Amazon revealed “Rufus,” a generative AI shopping assistant based on its product catalog as well as other sources, to respond to shopping queries, compare products, and help users find new items within the Amazon shopping experience. This is significant because it suggests that “search” is evolving into a hybrid experience: classic search results coupled with conversational assistance and comparative summarization.
Visual search is also evolving into an everyday discovery tool. Amazon debuted Lens Live, enabling users to simply point a camera at an object and access a menu of matching products, adding a new layer between physical-world browsing and online purchase. Visual search systems can be thought of as computer vision plus retrieval at scale. The difficult part is not recognizing an object. It’s matching a shoppable catalog, dealing with variants, and also handling ambiguous items.
On the platform side, Shopify’s approach also highlights the importance of AI discovery for merchants. Reuters described Shopify’s release of an AI tool that creates full online stores based on a few keywords, illustrating how genAI is being applied to accelerate store creation and content generation. The business press also mentioned Shopify acquiring an AI search company (Vantage Discovery) in the hope of enhancing AI search and discovery for merchants. If you’re not even on Shopify, the signal still stands: discovery AI is becoming table stakes.
The stumbles in implementing this approach are predictable. “Related” results can be the output of a semantic search when the results are not “What the user asked for,” and this is felt to be inappropriate at high intent. Conversational assistants can hallucinate product specs or compatibility. Visual search can overmatch “similar-looking but incorrect” products. The solution “is not ‘better prompts.’” The solution is grounding, constraints, and evaluation. When it comes to conversational shopping, you want the assistant to reference catalog attributes and reviews, not make up lore about products. For search ranking, you want to track not just click-through rate but downstream purchase and return behavior, as search engines may thank you for “interesting” results that don’t convert.
Pricing AI is one of the most profitable and risky sectors in retail.
It’s profitable because retail pricing is a constant optimization. Demand fluctuates by season, competitor prices vary daily, inventory ages, shipping costs change, and promotions interact in nonlinear ways. AI can be used to predict price elasticity, to calculate cannibalization, to estimate promotion lift, and to decide the timing of a markdown so that you can cut inventory with less margin loss.
It is risky because “AI pricing” could become personalized pricing using personal data, which raises fairness issues and will likely be subject to regulatory scrutiny. A concrete recent example is Instacart pausing AI-based price experiments on its platform, after media criticism and regulatory scrutiny, Reuters reported, following a story that some customers were charged more for groceries than others on the platform for the same items. This is the sort of headline risk that emerges when pricing models are opaque or when testing is not disclosed.
Regulators have their sights explicitly set on this space. The FTC has begun issuing 6(b) orders in July 2024 to a number of companies that provide pricing and personalization tools, as it seeks to learn more about the mechanics of “surveillance pricing” and the kinds of data that are involved. In January 2025, the staff of the FTC highlighted the use of behavioral signals for individual pricing and raised concerns about consumer transparency. There is also state-level legislative and litigation activity on algorithmic pricing disclosures, with news reports that New York was sued by the National Retail Federation over its law requiring disclosure of algorithmic pricing.
So what should a “safe and effective” priced AI system truly look like? It begins with segmentation at the product and market level (not at the person level, unless you have a very clear legal and ethical path). It is based on a statistical management system to base prices and promotions on aggregated signals (such as demand trends, competitor prices, stocks, and seasons). It creates limits such as a minimum price floor, maximum price change per day, and a “do-not-change” rule for the most sensitive categories. It features an experimentation framework that can assess uplift without silently segmenting the population.
The most important modeling detail is how you estimate elasticity and substitution. The effect of price changes is not only to reallocate demand for a single SKU. They reallocate demand between similar SKUs, brands, and sizes. They also tie in with shipping thresholds and bundling. So a naive single-SKU regression is frequently meaningless. Mature methods now exist, including hierarchical models, causal uplift modeling, and constrained optimization, which respect both inventory and margin.
Measurement should include trust metrics and not just revenue. Monitor complaints, churn, refunds, and price-match claims. Price AI can be “right” financially and still hurt brand perception.
Demand forecasting is the driving force for inventory, replenishment, staffing, and the planning of fulfillment. In e-commerce, it also influences delivery promises and marketing spend decisions. The problem with forecasting is that retail demand is so lumpy and so hierarchical in nature. You forecast at the SKU-store-day level, but the decisions are made at the category, region, warehouse, and channel levels. Promotions create spikes. Weather creates spikes. Trends create sudden shifts. Competitor changes shift demand. Out-of-stocks censor demand signals because “no sales” could mean either “no demand” or “no inventory.”
A major research milestone in retail forecasting is represented by the M5 Forecasting competition, which concentrated on forecasting unit sales of time series for a big retailer (Walmart). The released result is the forecasting of 42,840 time series in a hierarchical structure paper that is widely referenced and considered a benchmark in retail forecasting research, as it represents realistic retail complexity rather than toy datasets. This is important because it is a core lesson: the best retail forecasting systems are ones that use a mixture of techniques, properly evaluated, taking the hierarchy and business constraints into account.
In practice, forecasting teams rely on a combination of classical time-series-based methods, gradient boosted trees (GBTs) with feature engineering, and deep learning models (such as sequence models), depending on latent space richness and latency constraints. The “right” model isn’t as important as the “right forecast pipeline.” You need clean calendars, promotion flags, price history, event data, and, in many cases, weather. You also need a way to deal with the new product (“cold start”) by analogy to products that already exist.
A production forecasting system should provide more than a point forecast, not only uncertainty. In retail, uncertainty is the distinction between “we stock enough” and “we run out of stock.” This is why forecasting evaluation should consider quantile accuracy, bias, and stability, in addition to mean error.
Inventory forecasting is where prediction becomes currency.
The true goal is not “minimize stockouts” or “minimize inventory.” It is possible to achieve service level targets at a minimum total cost, including holding cost, stockout cost, markdown cost, and logistics cost. The most advanced AI inventory systems combine demand forecasts with lead times, supplier reliability, distribution constraints, and substitution patterns. They also manage “multi-echelon inventory,” inventory at the level of suppliers, distribution centers, retail stores, and even micro-fulfillment nodes, and at the store level.
Retailers are more and more calling this an AI system instead of a spreadsheet process. Walmart Global Tech has portrayed its AI-based inventory management system that leverages historical data and predictive analytics to place items across its distribution, fulfillment centers, and stores, highlighting strategic placement for seasonal peaks. Amazon has outlined AI-based demand forecasting and logistics enhancements for such supply chain planning and delivery, suggesting comparable investment in forecasting and network placement.
A key enabler of omnichannel inventory precision is item-level visibility enabled by RFID and other sensing technologies. The Inditex 2018 annual report does explicitly refer to using RFID to unify store and online stock, with increasing inventory management efficiency as an objective. Analysis from Accenture has also highlighted RFID as a facilitator in achieving greater inventory accuracy and omnichannel functionality. Or, said another way, a lot of the AI optimization relies on having a “truth layer” on where the inventory really is.
This category collapses when teams overestimate the quality of their data and the predictability of the operations. Lead times are not constants; they are distributions. Supplier delivery performance is dynamic. Returns flow back unpredictably. Store receiving and cycle counts are imperfect. If your restock model assumes perfect execution,n your replenishment model will be telling you to make unreachable transfers and maintain unrealistic amounts of safety stock.
Replenishment is a should be considered as a closed-loop system in the execution. You create recommended orders and transfers. They are carried out by people and systems. You spot deviations. You upgrade lead time models and safety stock. You also track out-of-stock duration, on-shelf availability, and lost sales estimates so you can demonstrate value.
For the e-commerce space, “the product” isn’t just the catalog. It’s the delivery promise. Customers recall late deliveries and damaged products more than they recall your recommendation algorithm. This is why logistics AI is now a staple category within retail AI.
There are two main levels. The first level is network planning: where should inventory be located to achieve promised delivery times at minimum cost? The second level is execution: pick paths, packing, linehaul, last-mile routing, and dynamic exception handling.
Walmart has introduced AI-powered logistics products for route efficiency, including trailer packing and route planning, as a means to reduce miles and increase efficiency. Walmart Global Tech has also depicted an “AI network” that is coordinating holiday deliveries by focusing on forecasting demand, optimizing placement, and recognizing risks early on as a continuously learning fulfillment network.
Much of Amazon’s public communication has also featured AI for delivery mapping, demand forecasting, and robotics. Amazon detailed generative AI mapping technology and an AI-driven demand forecasting model underpinning its supply chain, as well as “agentic” AI functionality for robotics. News coverage on Amazon’s warehouse robot “Vulcan” focuses on tactile sensing and AI that is trained on touch and force data to be able to handle a sizable portion of items, illustrating how far warehouse automation has evolved beyond simple conveyor automation.
The relevant implementation detail here is that logistics AI is prediction and constrained optimization. Predictive model to predict demand, labor requirements, and delay risk. Optimization models determine how to distribute inventory, how to assign orders to nodes, and how to plan routes. If you think of it as “just machine learning,” you miss the optimization problem. If you look at it as “just optimization,” you ignore uncertainty and drift.
Success is measured by cost per order, on-time delivery rate, split shipment rate, pick productivity, packing efficiency, damage rate, and customer contact rate for “where is my order.” This type of category often pays for itself as it reduces direct logistics costs and indirect customer service costs.
The volume of traffic and the emotional impact make retail customer service costly. Most queries are the same (“Where’s my order?”, “How do I make a return?”, “Do you have a size guide?”). But the tail-end cases are challenging and require empathy and policy nuance.
Generative AI is starting to find its way into retail support, but the pattern that works is not “let the bot do everything.” The ideal pattern is a grounded assistant that fetches policy and order details, drafts responses, and escalates when the case is sensitive or ambiguous.
Best Buy publicly announced the deployment of a generative AI-based virtual assistant for customers on web, app, and phone support, which is available as a self-service support option as well as an agent-assist feature for customer care agents. This is a good study in that it shows a real retail approach: don’t try to replace humans overnight, just make support faster and more consistent.
The main concern is hallucination. A support agent who confidently provides an incorrect return deadline, an incorrect warranty policy, or an incorrect delivery date can lead to financial damage and loss of trust. So the rollout has to have grounding in authoritative data sources, and the model has to be restricted in tool power (it shouldn’t have the blanket ability to submit orders), and the submission of errors should be logged and reviewed.
You also have to design for adversarial conduct. The support channels of retail are abused for refunds, social engineering, and policy violations. Your assistant is part of your fraud surface now if it can be tricked into issuing credits or leaking account info.
Measures of success are containment rate (tickets resolved without human intervention), time to resolution, user satisfaction, and critical safety measures such as wrong-answer rate on policy questions and quality of escalation.
E-commerce fraud is more than just payment fraud. It also covers account takeover, promo abuse, fake reviews, affiliate fraud, refund abuse, and return fraud. That’s why it adds up to so much. If you clamp down in one place, fraud moves elsewhere. A sophisticated retail fraud program views fraud as a lifecycle adversary, not a single model.
At the payment level, risk scoring and fraud prevention solutions are well established and widely available. Stripe promotes Radar as an AI-based fraud system for online payments, with features such as adaptive risk scoring and network-scale signals. The network-level data reporting also offers an insight into how big the stakes are, with Reuters reporting that Visa stopped a massive amount of fraudulent transactions in 2023, representing AI-driven defense at the network scale.
But for those running e-commerce sites, the biggest ”hidden fraud” is often actually policy abuse: promo codes, refunds, and returns. The signals of fraud in this case are behavior: an unusually high frequency of returns, identity mismatches, repeated claims of “item not received,” suspicious patterns of addresses, and cycles of repeated orders and returns.
Implementation is not simply “train a model.” It is constructing an abuse graph. Fraudsters recycle their use of phones, emails, devices, addresses, payment instruments, and social accounts. Entity resolution and graph features frequently outperform single transaction-based models, as they can expose rings.
The success is measured by chargeback rate, refund abuse rate, promo abuse rate, false positive rate (how many good customers do you punish), and how much customer friction costs you. The best systems are run for long-term profitability and trust, rather than short-term fraud minimization.
Returns represent one of the biggest sources of cost in e-commerce, and one of the key determiners of whether a customer comes back. They also create a second supply chain in reverse, one that has its own bottlenecks and fraud.
The size is enormous. NRF and Happy Returns predicted total retail returns in 2024 at $890 billion and assumed 16.9% of annual sales would be returned. The NRF and Happy Returns report for 2025 projected returns of approximately $849.9 billion with a return rate of 15.8%, suggesting that this is a persistent structural challenge and not merely an aberration. These figures are significant because they suggest that minor enhancements in return routing, grading, resale, or fraud deterrence can translate to millions of dollars for major retailers.
Return fraud is one of the fastest-growing subtypes. Reuters noted UPS-owned Happy Returns is rolling out an AI tool (“Return Vision”) that compares returned items to purchase images and uses pattern signals to highlight suspicious returns, indicating how reverse logistics companies are leveraging AI to detect fraud. This is a tangible indication that returns fraud is being approached as a “computer vision + behavioral analytics” problem and not just living in the land of manual inspection.
Returns optimization has three broad AI opportunities. The first is decisioning: whether the customer should return, exchange, or keep the item with a refund or partial refund. “Keep it” decisions can cut shipping and handling costs on small or low-value items, but they need to be tightly controlled to prevent abuse. The second is routing: route the return to the most appropriate node for inspection and resale by item type, demand, and capacity. The third is grade and disposition: resell, refurbish, recycle, or liquidate by using AI-based inspection combined with the prediction of resale value.
Implementation challenges are primarily in execution. If your “keep it” policy is too lenient, social sharing becomes an abuse case. If your fraud detection is too strict, you punish honest customers and increase churn. If you have bad routing, you increase cycle time and reduce resale value.
Success measures include the rate of returns, time for processing a return, amount recovered by resale, cost per return, losses from fraud, and whether customers repurchase after a return.
Even pure e-commerce companies are more and more omnichannel, and a lot of retailers have stores and an online presence. The store is also a fulfillment node (BOPIS, ship-from-store) and not just a sales channel. That means that shelf availability and inventory accuracy are key not only to store outcomes but also to e-commerce outcomes.
Computer vision can recognize out-of-stock, misplacement, and planogram violations based on shelf images. Research continues to advance in the academic literature, including papers that posit deep learning methods for out-of-stock detection in retail shelf images. It matters because monitoring shelves is a fundamental retail loss: if the product is not there, the sale doesn’t take place, and signals of demand are muddled.
A second enabler is RFID and perpetual inventory sensing, which complements vision. Gap Inc. revealed Old Navy’s collaboration with RADAR to implement AI-based RFID technology and provide store employees with up-to-the-minute inventory data to enhance the customer experience, illustrating how retailers are combining sensing + AI to advance in-store inventory visibility.
The successful implementation pattern is event-driven execution. The system doesn’t simply identify “gap on shelf.” It creates a replenishment task, prioritizes tasks by expected lost sales, and closes the loop by checking that the shelf was replenished. If you stop at detection, you don’t get ROI.
This classification also extends to loss prevention and shrinkage, with computer vision assisting in identifying suspicious activity and inventory irregularities. So that is sensitive ground. It takes a clearly defined policy, privacy protection, and diligent human review to ensure no harmful results.
Key results for success include on-shelf availability, duration out of stock, pick accuracy for BOPIS/ship-from-store, labor productivity, and shrinkage loss mitigation.
Throughout these ten fixes, the same engineering truths keep recurring.
Firstly, you need an event substantiation. Retail AI relies on behavioral events – views, searches, clicks, add-to-cart, purchases, returns, customer service contacts, inventory movements, and fulfillment events. Without clean event schemas, consistent IDs, and time-synchronized logs, your models and experiments will be fragile and untrustworthy. Most “AI failures” in retail are really failures of instrumentation.
Secondly, you need to be disciplined with experimentation. Personalization, search ranking, pricing, and promotions are all areas where offline evaluation falls short. You want controlled experiments with constraints. You also have to measure downstream effects, not just immediate clicks. A Search that forces more clicks, but returns more items, is not a win. Pricing that increases margin but churns more customers is not a win.
Thirdly, you want feedback loops and monitoring. Retail demand varies by season and culture. Fraud evolves. Inventory truth varies. When you ship an AI system and don’t monitor drift, you degrade slowly. Your monitoring should include model metrics (calibration, error, confidence distributions) and business metrics (conversion, margin, stockouts, returns, and complaint rates). Then if they diverge, you investigate.
Fourthly, you want governance in trust-sensitive domains. It’s the cost that is the big one. The FTC’s surveillance-based pricing study and public pronouncements demonstrate that the regulators are paying attention to the use of personal data to customize prices (and offers). You should probably assume this scrutiny is going to ramp up, not down, particularly as AI makes personalization easier. A realistic governance policy is to be explicit about which personalization you will apply (content, ranking, offers) and which you won’t (individual pricing based on sensitive personal data), and document that policy.
Fifthly, you must have integrated “closed-loop ops.” The detection of the inventory on the shelf has to create a task and check completed. Fraud detection led actions and tracked results. Returns routing should be linked to reverse logistics capacity. Forecasting needs to drive replenishment and staffing. If AI results are still in dashboards, you don’t have ROI.
And finally, you have to treat the “exceptions” as first-class data. Retail has many edge cases: a flash sale, an influencer burst, a supplier failure, a weather event, a carrier disruption, and a fraud wave. True amusement lies in how the system performs under strain and not in its regular operation.
