AI in eCommerce: 10 Tasks You Can Simplify Without a Big Team
An online store rarely falls apart because of one big problem. More often, it is slowly eaten away by small routine tasks: product cards, delivery questions, category descriptions, reviews, advertising ideas, and shoppers who add an item to the cart, look at the shipping details, and disappear like a magician at a corporate party.
If the team is large, all of this can be split between departments. But sometimes the entire marketing team is one person, a cup of coffee, and a browser tab called “I’ll deal with this later.”
And this is where AI becomes useful. Not as a “make it profitable” button — that button does not exist, otherwise marketers would have long ago been arguing only about the coconut temperature in Bali. AI works as an assistant for repeatable tasks: product data, customer questions, on-site search, recommendations, email campaigns, support, and initial analytics.
Business is already moving actively in this direction. According to Salesforce, during the 2024 holiday season, AI and AI agents influenced $229 billion in global online sales, while the use of AI chats for customer service increased by 42% compared to 2023. Precedence Research estimates the AI in eCommerce market at $9.01 billion in 2025 and forecasts its growth to $74.93 billion by 2035.
We see our customers as invited guests, and ourselves as hosts. Our job is to make every important part of the customer experience a little better every day.
In this article, we’ll look at 10 eCommerce tasks that can be simplified with AI — without a large team or a huge budget.
10 eCommerce tasks that can be simplified with AI
AI works best when it has a specific task, input data, and a clear expected result: finding missing product attributes, analyzing search queries, preparing a draft response for support, identifying a stockout risk, or forming hypotheses about a conversion drop.
Let’s look at 10 tasks where AI can take part of the routine off a small eCommerce team’s plate and help with more than just “generating text” — making it faster to work with the catalog, search, support, analytics, and repeat sales.
1. Product cards and order in product data
A product card is not just a description. It is a small salesperson working 24/7. If it only says “women’s sneakers, black, comfortable,” that salesperson has clearly shown up for the shift in the wrong mood.
AI can help not just write the text, but bring the product card into a proper structure:
- extract attributes from a raw supplier description;
- find missing required fields;
- standardize units of measurement;
- flag unclear or questionable details for review;
- prepare the title, description, benefits, FAQ, and meta tags based on verified data.
In its case study with Bata, Akeneo notes that AI-powered catalog enrichment helped speed up time to market by 40% and drive a 15% increase in organic traffic. For Snowleader, AI workflows reduced manual work on product descriptions by 20% over the season. So the value of AI here is not in “prettier text,” but in faster catalog work and less manual routine.
How to use it in practice:
Export products from your PIM, CMS, or spreadsheet, add a list of required fields for each category, and ask AI to find issues before generating any text.
Mini prompt:
You are a PIM editor and SEO copywriter for an online store.
Check the product data before preparing the product card.
Input data:
— product name: [name]
— category: [category]
— supplier description: [text]
— attributes: [table]
— required fields for the category: [list]
— prohibited claims: [list]
What you need to do:
1. Find any missing required attributes.
2. Flag unclear or questionable data.
3. Do not invent properties that are not present in the source data.
4. Prepare a short description, 5 benefits, FAQ, and meta title/meta description based only on confirmed data.
5. Separately list what a human should check before publication.
A good product card starts not with beautiful text, but with clean data. Otherwise, AI will simply wrap chaos in SEO packaging.
2. Internal search and “zero-result” queries
Shoppers often search differently from how products are named in the catalog. A product card may say “45 cm dishwasher,” while the person types “small dishwasher.” The catalog may say “USB-C adapter,” while the user searches for “charger for MacBook 2021.”
If search does not understand these formulations, the store loses people who already have a clear intent to buy. They came for a specific product, but got an empty page, strange results, or the philosophical “nothing found.”
AI can analyze internal search queries: find typos, conversational wording, zero-result searches, queries with no clicks, and phrases after which users leave the site.
For example, AI can suggest that “small dishwasher” should be connected to the 45 cm width filter, “prom dress” to a buying scenario, and “charger for MacBook 2021” to compatibility checks, not just to the general chargers category.
In Constructor’s case study for White Stuff, search improvements led to a 21% increase in search conversion. And in Algolia’s case study for Staples Canada, 65% of search issues were solved through AI synonyms: the system helped connect the shopper’s language with real products in the catalog.
How to use it in practice:
Export search queries for the last 60-90 days and separately review zero-result searches, queries with no clicks, and exits after search. Then AI can group queries by intent and suggest where synonyms, filters, landing pages, or catalog structure changes are needed.
Mini prompt:
You are a site search merchandiser for an online store.
Analyze user search queries and find problems in internal search.
Input data:
— list of search queries: [queries]
— number of impressions for each query: [data]
— zero-result rate: [data]
— clicks / add-to-carts / purchases by query: [data]
— catalog categories and filters: [structure]
— list of available brands, sizes, colors, and attributes: [list]
What you need to do:
1. Group queries by intent: product, category, attribute, compatibility, gift, problem, inspiration.
2. Find queries where the site may not understand the user.
3. Suggest synonyms and query rewrite options.
4. Suggest which filters, categories, or landing pages should be checked.
5. Separately mark queries where a result cannot be confidently selected without human review.
Do not invent products, categories, or filters that are not in the catalog.
The key is not to let AI freely fantasize with search results. It should work within the real catalog: available categories, brands, sizes, colors, attributes, and stock.
3. Categories, filters, and SEO structure
Categories in an online store often suffer not only from weak copy. Sometimes the problem is deeper: there are not enough filters, subcategories are mixed together, popular shopping scenarios are not turned into dedicated pages, and the SEO description heroically tries to cover all of it with the phrase “a wide range of quality products.”
AI can help analyze demand within a category: which attributes people search for most often, which questions keep repeating, which filters users need, and where it makes sense to create a separate collection or landing page.
For example, if people in the “chairs” category often search for “for kitchen,” “upholstered,” “wooden,” “for a small apartment,” or “under $30,” this is not just a set of keywords for the text. It is a hint for the structure: filters, “how to choose” blocks, FAQ, internal linking, and possibly separate SEO pages.
How to use it in practice:
Export the list of categories, filters, search queries, landing pages, and popular attributes. Then ask AI to find where the category does not match real demand: a needed filter is missing, the FAQ is too thin, different intents are mixed together, or it may make sense to create a separate collection.
Mini prompt:
You are an SEO specialist and eCommerce structure expert.
Analyze an online store category and suggest how to improve its structure.
Input data:
— category: [name]
— current subcategories: [list]
— available filters: [list]
— search queries for the category: [list]
— popular products: [list]
— customer questions / FAQ: [list]
— pages that already exist on the website: [list]
What you need to do:
1. Group queries by intent: price, material, purpose, size, style, seasonality, compatibility.
2. Suggest which filters or subcategories may be missing.
3. Suggest blocks for the category page: introduction, “how to choose,” FAQ, collections, internal linking.
4. Mark where it may make sense to create a separate landing page.
5. Separately list which ideas should be checked by an SEO specialist before implementation.
Do not suggest pages and filters just for the sake of SEO.
Evaluate user value and the risk of duplicates.
4. Visual search and similar product recommendations
In some niches, shoppers do not know how to describe what they want in words. This is especially common in clothing, furniture, décor, jewelry, gifts, and home interior products.
They may see a photo on Pinterest, Instagram, or TikTok and think: “I want something like that.” And then the quest begins: “a beige sweater, not thick, but nice, like the one that girl wore in the video.” At this point, the search bar suffers a little too.
AI can help with visual search: the user uploads a photo, and the website shows similar products. Or a product card gets blocks like “similar models,” “complete the look,” “similar by color,” or “similar by shape.”
In Syte’s case studies, visual search showed strong results: PrettyLittleThing achieved a 2.3x increase in conversion and 269% ROI, while Nourison saw an 8x increase in CVR and a 124% increase in ARPU. The effect depends heavily on the niche, but the logic is clear: if people choose a product with their eyes, search should also be able to “look,” not just read.
How to use it in practice:
Start with 1-2 visual categories where text search performs poorly: clothing, shoes, accessories, furniture, or décor. Add “similar products” blocks to product cards, then check photo quality, product availability, and the logic behind recommendations.
Mini prompt:
You are an eCommerce merchandiser.
Help prepare the logic for a “similar products” block on a product card.
Input data:
— product: [name]
— category: [category]
— photo / visual attributes: [color, shape, style, material]
— product characteristics: [data]
— products in stock: [list]
— products that should not be shown: [list]
What you need to do:
1. Identify the main visual attributes of the product.
2. Suggest rules for selecting similar products.
3. Mark which products should be excluded from recommendations.
4. Suggest which attributes matter most: color, shape, style, material, category, or price.
5. Separately explain where human review is needed.
Do not recommend products from the wrong category just because they have a similar color.
The key is not to confuse “similar” with “random item in the same color.” If someone is looking for a minimalist white dress, they do not need to see a wedding cake just because it is also white and fancy.
Useful articles:
Prompts for analyzing funnels and conversions with AI
ChatGPT Shopping in 2026: how AI shopping will change SEO and the sales funnel for online stores
5. Size selection, compatibility, and fewer returns
Some eCommerce returns happen not because the product is bad. The shopper simply expected one thing and received another: the size did not fit, the color looked different, the accessory was not compatible, the part did not fit the model, or the furniture did not work for the room dimensions.
AI can help before the purchase: suggest the right size, warn that a model runs small, check whether an accessory is compatible with a device, or explain the difference between similar products.
In Moosejaw’s case, work with size bracketing helped reduce eCommerce returns by 24%. True Fit also notes that properly configured fit recommendations can reduce size-related returns by up to 40% and increase sitewide conversion by 1-2%. This is a good example of AI helping not to “answer beautifully,” but to reduce real business losses.
How to use it in practice:
Choose categories with a high return rate or frequent questions like “will this fit me?”. Then collect size charts, return reasons, reviews, support requests, and product data. AI can find recurring issues and suggest where to add a warning, table, compatibility block, or consultation question.
Mini prompt:
You are a product selection consultant for an online store.
Analyze the product and help identify what guidance the shopper needs before buying.
Input data:
— product: [name]
— category: [category]
— characteristics: [size, material, model, compatibility]
— size / compatibility chart: [data]
— customer reviews: [reviews]
— return reasons: [data]
— support questions: [questions]
What you need to do:
1. Find common reasons for hesitation before purchase.
2. Identify where the shopper needs guidance: size, compatibility, material, color, dimensions, or usage conditions.
3. Suggest tooltip or guidance text for the product card.
4. Mark which data should be checked by a human.
5. If there is not enough data for an accurate recommendation, state this separately.
Do not promise a perfect fit or 100% compatibility unless it is confirmed by data.
6. Customer support with order context
A regular FAQ bot answers general questions. Sometimes that is useful. Sometimes you want to ask: “Did you even understand that I have already ordered the product and it is being shipped to another city?”
A more practical scenario is an AI support assistant that can see the order context: delivery status, product, purchase date, return terms, previous support history, and store policies.
It can help the manager quickly identify the type of request, prepare a draft response, and suggest the next step: explain the delivery status, check whether an exchange is possible, clarify return terms, or pass a complex case to a human.
In Casely’s case study, the combination of Loop Tracking and Gorgias helped reduce WISMO requests by 76%. WISMO stands for “Where is my order?” — a timeless customer support classic: the customer has already bought the product, but now wants to know where the order is and why it has not yet magically appeared at the door.
In its public materials, Klarna reported that its AI assistant handled two thirds of chats, reduced repeat inquiries by 25%, and shortened resolution time to under 2 minutes.
How to use it in practice:
Start by choosing 10-15 safe scenarios that can be partially automated: order status, delivery, exchange, return, warranty, availability, and basic product questions. Then collect store policies, response templates, order statuses, and reasons for escalation to a human.
Mini prompt:
You are an AI assistant for an online store support team.
Prepare a safe draft response for a manager based on the order data and store policies.
Input data:
— customer message: [text]
— order status: [data]
— product: [name / SKU]
— support history: [data]
— delivery policy: [terms]
— return and exchange policy: [terms]
— allowed actions: [explain / replace / refund / pass to a human]
What you need to do:
1. Identify the type of request.
2. Prepare a short and clear draft response.
3. Specify the recommended action.
4. Note whether a human is needed to resolve the situation.
5. List which data you used for the response.
Do not promise a refund, compensation, free delivery, or replacement if this is not included in the store policies.
7. Abandoned carts and the next best step
The old scenario looks like this: a person abandons the cart, so we send an email saying, “You forgot something.” Then another one. A week later, the communication starts to feel like an alarm clock that has been snoozed too many times.
AI helps not just chase the shopper, but choose the next best step: who to contact, when, through which channel, with what message, and whether a promo code is needed at all.
For example, a new user can receive a soft reminder, a returning customer can get a personalized selection, and a shopper considering an expensive product may need not a discount, but an emphasis on warranty, delivery, or support.
In Bloomreach’s case study for Veke, behavior-based automated campaigns delivered a 58% increase in revenue from automated campaigns. 260 Sample Sale saw a 2.4x increase in conversion in an AI-powered campaign, while JENNY BIRD increased AOV by 58% with post-purchase offers. So here, AI helps not to “send more emails,” but to choose the right moment, channel, and meaning of the communication more precisely.
How to use it in practice:
Start with 3-4 scenarios: abandoned cart, product view without purchase, repeat purchase, and reactivation of inactive customers. For each scenario, set rules: when to send a message, who should not receive one, when to offer a discount, and when it is better not to touch the margin.
Mini prompt:
You are a CRM strategist for an online store.
Define the next best step for the customer.
Input data:
— customer status: [new / active / returning / inactive / VIP]
— recent actions: [product view / cart / purchase / return / wishlist]
— products in the cart or viewed: [list]
— product margin: [data]
— purchase history: [data]
— available channels: [email / SMS / push]
— message frequency limits: [rules]
— discount rules: [when allowed / when not allowed]
What you need to do:
1. Define the communication goal: recover the cart, suggest an alternative, increase repeat purchase, or avoid contacting the customer.
2. Choose the channel and sending time.
3. Suggest the main message idea.
4. Specify whether a promo code is needed or whether it is better to avoid a discount.
5. Separately note if it is better to leave the customer alone for now.
Do not suggest a discount automatically. First evaluate the margin, customer status, and communication history.
But sometimes the best next step is not an email, not an SMS, and not a push notification — it is a pause.
8. Recommendations and merchandising with business context
The “you may also like” block often works on the principle of: “well, here are some other products.” Sometimes it guesses right. Sometimes it suggests a garden hose next to a baby crib because the algorithm has discovered some secret connection of its own.
AI can make recommendations more useful by taking into account not only product similarity, but also availability, margin, seasonality, discounts, stock levels, and customer behavior.
For example, the system can be given different tasks:
- show products that are in stock, not beautiful but unavailable items;
- give seasonal products higher priority;
- avoid promoting low-margin products without a reason;
- help sell remaining stock;
- suggest a bundle item for the main product;
- personalize selections for new, returning, or VIP customers.
In its case studies, Nosto gives examples where applying merchandising rules led to a 15-21% increase in conversion rate (CVR) and a 15% increase in average revenue per user.
How to use it in practice:
Start with the blocks that already exist on the website: “similar products,” “frequently bought together,” “popular products,” “recommended,” category listings, or search results. Then set the rules: do not show out-of-stock items, and take into account margin, season, discounts, stock levels, and priority categories.
Mini prompt:
You are an eCommerce merchandiser.
Help configure the recommendation logic for an online store.
Input data:
— product or category: [data]
— list of products in stock: [list]
— product margins: [data]
— stock levels: [data]
— seasonality: [data]
— discounts and promotions: [data]
— user behavior: [views / clicks / purchases]
— products that should not be promoted: [list]
What you need to do:
Suggest which products should be shown in recommendations.
Explain why these products were selected.
Mark the products that should be excluded.
Split recommendations by logic: similar products, add-ons, seasonal products, and products with good margins.
List which rules should be checked by a human before launch.
Do not recommend products only because they have a similar color or name. Take into account availability, relevance, margin, and value for the shopper.
9. Demand forecasting, stock, and markdowns
AI in eCommerce can help not only with marketing, but also with more down-to-earth things: how much stock to reorder, where there is a risk of shortage, which items are sitting in the warehouse for too long, and when it is better to launch a discount.
The problem is simple: if a popular product is out of stock, the store loses sales. If a weak product sits for too long, it takes up warehouse space and freezes cash. And if the season is already over but the boxes are still there, a sad sale begins in the style of “please, literally anyone.”
AI can analyze sales, stock levels, returns, promotions, seasonality, lead times, and advertising activity. After that, it helps identify products at risk of stockout, slow movers, categories with declining demand, and items where markdowns should be planned in advance.
In AWS’s case study for Foxconn, forecasting improved forecast accuracy by 8% and delivered $553K in annual savings. More Retail increased forecast accuracy from 24% to 76%, reduced write-offs by up to 30%, and increased gross profit by 25%. Zalando also reports GMV growth of up to 22.1% in replenishment optimization. The numbers vary in scale, but the takeaway is the same: AI helps you stop guessing “seems like people will buy this” and make stock decisions based on data.
How to use it in practice:
Start by exporting sales, stock levels, returns, promotions, and seasonality data for the last few months. You do not need to build a complex ML system right away. As a first step, AI can find anomalies, products at risk of stockout, slow movers, and categories where discounts should be planned in advance — not when the warehouse is already looking at you with disappointment.
Mini prompt:
You are an eCommerce sales and inventory analyst.
Analyze product data and help identify risks related to stock, demand, and markdowns.
Input data:
— product / SKU list: [data]
— sales by period: [data]
— stock levels: [data]
— returns: [data]
— promotions and discounts: [data]
— seasonality: [data]
— lead times: [data]
— advertising activity: [data]
What you need to do:
1. Find products at risk of stockout.
2. Identify slow-moving products that may require a markdown.
3. Show categories where demand is growing or declining.
4. Mark products that should not be replenished without additional review.
5. Create a list of questions for the buyer or category manager.
6. Separately indicate where the data is not sufficient for a reliable conclusion.
Do not make an exact forecast if the data is limited or incomplete. It is better to show risks and hypotheses.
10. Initial analytics and finding weak spots
The data is in Google Analytics, CRM, ad accounts, CMS, email services, call tracking, spreadsheets, reports, and probably somewhere else that only an employee who left last year had access to.
AI can help not by replacing an analyst, but by making it faster to understand where to look first: where conversion dropped, at which checkout step people leave, which devices generate more errors, which channel brings traffic without purchases, where returns increased, or why after a website update it suddenly became “quiet at the cash register.”
For example, AI may suggest that after a checkout update, payment-step exits increased on iPhone. Or that users from a certain ad channel often add products to the cart but do not buy. Or that the growth in support questions about delivery coincided with a conversion drop at the delivery method selection step.
In Quantum Metric case studies, retailers reduced error detection time to 30 minutes and achieved a 5.3% increase in conversion after fixing checkout issues. FullStory shares an eCommerce example with a 26% conversion increase after session replay analysis. And Lululemon noted tens of millions of dollars in revenue impact after quickly identifying critical digital experience issues.
How to use it in practice:
Export funnel data for a selected period and compare it with the previous one: traffic, conversion, devices, browsers, sources, checkout steps, errors, support requests, and key website changes. Then ask AI not to “make business decisions,” but to create a list of hypotheses that need to be checked.
Mini prompt:
You are an eCommerce analyst.
Analyze the online store funnel data and help find possible reasons for the conversion drop.
Input data:
— comparison period: [dates]
— traffic by channel: [data]
— conversion by funnel step: [data]
— devices and browsers: [data]
— pages with high exit rates: [data]
— website / checkout errors: [data]
— support requests: [topics]
— website changes during the period: [list]
— advertising campaigns: [data]
What you need to do:
1. Find the segments where conversion changed the most.
2. Identify possible reasons for the drop.
3. Prioritize hypotheses: high, medium, low.
4. Specify which data supports each hypothesis.
5. Suggest what to check first.
6. Separately note where the data is not sufficient for a confident conclusion.
Do not present correlation as a proven cause. Phrase conclusions as hypotheses to be checked.
How to choose where to start
It is better to start with the processes where the team already loses the most time: product cards, reviews, typical questions, abandoned carts, ad hypotheses, follow-up emails, or return analysis.
| Task | What AI simplifies | What a human checks | KPI | Main risk |
| Product cards and product data | extracts attributes, finds missing fields, prepares the product card structure | facts, sizes, materials, availability, claims | SKU publication speed, attribute completeness, product card CR, organic traffic | invented product properties |
| Internal search | analyzes queries, zero-result searches, synonyms, and intent | result relevance, filters, categories, availability | search conversion, zero-result rate, search CTR | irrelevant search results |
| Categories, filters, and SEO structure | groups demand, suggests filters, FAQ, and landing pages | indexing, duplicates, intent, internal linking | organic traffic, rankings, category CR | pages created just for the sake of pages |
| Visual search and similar products | selects visually similar products and “shop similar” blocks | match quality, availability, category, brand logic | assisted CR, AOV, clicks on picks | “similar” only by color |
| Size, compatibility, and fewer returns | helps with size, fit, and compatibility guidance | size charts, compatibility rules, questionable picks | return rate, PDP-to-cart, support requests | confident but incorrect recommendation |
| Support with order context | prepares draft responses, identifies intent and the next step | complaints, payments, conflicts, non-standard cases | first response time, CSAT, repeat contacts, WISMO requests | the bot promises too much |
| Abandoned carts and next-best-action | chooses the channel, timing, message, and whether an offer is needed | contact frequency, discounts, margin, segments | recovery rate, revenue from automation, unsubscribe rate | smart spam |
| Picks and merchandising | ranks products based on availability, margin, season, and behavior | business rules, brand, category priorities | CTR, CVR, revenue per user, margin | optimizing for clicks instead of profit |
| Demand forecasting, stock, and markdowns | finds stockout risks, slow movers, seasonal drops, and products for discounts | stock levels, lead times, promotions, purchasing decisions | in-stock rate, stockout rate, inventory turns, gross margin | forecasting based on messy data |
| Funnel analytics | finds anomalies, problematic segments, and hypotheses for a CR drop | causality, events, tests, technical errors | CR, checkout completion, CPA, AOV, revenue recovered | nice-looking conclusions without validation |
This table is useful not only for planning, but also for staying realistic. If a store does not have clean data on products, orders, reviews, or advertising, AI will not become a magic consultant. It will work with what it has been given.
What to keep in mind before implementing AI
AI works best not as a separate toy, but as part of a clear process.
A bad option is to simply give the team access to ChatGPT and wait for the business to become more efficient on its own. Usually, this scenario leads to ten different writing styles, questionable promises in product cards, and the feeling of: “we implemented something, but what exactly is still unclear.”
It is better to start with 1-2 specific tasks: product cards, FAQ, review analysis, or abandoned cart scenarios. Each task needs input data, a prompt template, review rules, and a clear KPI.
AI can speed up work, but it should not work without control. It is a fast assistant, not an independent director of meaning, facts, and common sense.
If the process is chaotic, AI will simply speed up the chaos. And fast-moving chaos is no longer automation — it is a small corporate tornado.
