PLAYBOOK ATLASPlaybook Atlas

E-commerce

AI in e-commerce: explore 30 applications.

Help shoppers find eligible products and help teams measure the resulting purchase journey.

Explore catalog quality, search, merchandising, personalization and checkout workflows for online stores and marketplaces.

Plan your first AI move See the 30 applications

Where the 30 applications sit

Explore the domains
  1. 14Customer Engagement
  2. 10Sales Optimization
  3. 8Merchandising
  4. 4Marketing
  5. 3Pricing Strategy

01Why AI now

Why e-commerce is moving on AI, in three figures.

Each figure links to its source.

  1. Retail AI market: $40.74B by 2030

    Grand View Research forecasts the global artificial-intelligence-in-retail market will reach $40.74 billion by 2030, with personalization, image search and online retail among the growth drivers.

    Source: Grand View Research

  2. 56% of sites inadequately support search

    Baymard's 2026 benchmark found that 56% of evaluated e-commerce sites and apps failed to adequately support users' search needs.

    Source: Baymard Institute

  3. Nearly half of e-commerce firms use AI

    DHL's 2025 survey of 4,050 e-commerce businesses across 19 markets found that almost half had integrated AI into their operations.

    Source: DHL Group

How to evaluate a first project

Choose a specific friction point such as an unsuccessful search or an unavailable recommendation. Check catalog facts, stock, locale and eligibility at decision time. Compare the change with the existing storefront using conversion, margin, returns and customer-experience measures appropriate to the experiment.

02Capability map

Where e-commerce puts AI to work, domain by domain.

Pick a domain to see its applications. Each one opens a full report.

Inside Customer Engagement

Applications in Customer Engagement

What the use cases ask AI to do

Share of the e-commerce use cases, by the kind of work the AI does.

  1. Reasoning

    Analyzes, predicts and decides: scoring, forecasting, classifying.

    60%

  2. Generation

    Creates: text, images, code and other content.

    32%

  3. Agentic

    Acts: plans, uses tools and completes multi-step tasks.

    8%

Under 1% each: perception and optimization.

03Workflow ideas

Ways AI could change the work, in e-commerce.

Atlas groups these applications by the changes they propose to a workflow. These preliminary classifications help you explore ideas; each application still needs a review of its sources and operating requirements.

Workflow categories in this catalog

Share of the 23 applications assigned a category.

How to evaluate an application

  1. Start with a bounded task

    Identify the inputs, the person responsible, and the decision or output the workflow must produce.

  2. Check the support for each method

    Look for source-backed details about the specific implementation, including its limits and human review.

  3. Measure the pilot

    Record the current baseline, agree on acceptance criteria, and test on representative cases before expanding.

Explore applications

04What gets built

The approaches e-commerce uses most, and when to skip them.

Knowing when not to use one matters as much as knowing when to.

  1. 01 · 8 applications

    RecSys

    AI pattern for predicting and ranking items a user is likely to prefer.

    Use it when

    • You have a large or growing catalog of items (products, content, ads, jobs, etc.) and users struggle to manually discover relevant options.
    • You can reliably log user interactions with items (views, clicks, purchases, likes, completions) and maintain a stable user/item identity.
    • Personalization or relevance is a key driver of business value (engagement, revenue, retention, satisfaction).

    Skip it when

    • Your catalog is very small or static, and users can easily browse all options without assistance.
    • You lack sufficient interaction data or cannot reliably track user behavior due to privacy, technical, or regulatory constraints.
    • User identity is highly transient or anonymous, and you cannot build meaningful histories or segments.
    RecSys, in depth

  2. 02 · 6 applications

    API-Wrapper

    Thin application layer over a managed AI API.

    API-Wrapper, in depth

  3. 03 · 4 applications

    Workflow Automation

    Use AI inside orchestrated workflows to automate multi-step business processes.

    Use it when

    • When you have repetitive, rule-heavy workflows that still require human judgment or document understanding.
    • When the process spans multiple systems (ERP, CRM, ticketing, email) and manual coordination is slow or error-prone.
    • When large volumes of semi-structured or unstructured data (emails, PDFs, forms) must be processed consistently.

    Skip it when

    • When the process is highly creative, exploratory, or non-repeatable, with no stable sequence of steps.
    • When decisions are extremely high-stakes (e.g., life-critical medical decisions, major legal judgments) and regulations require full human control.
    • When you lack reliable access to the necessary systems or data sources to complete the workflow end-to-end.
    Workflow Automation, in depth

05The applications

30 e-commerce applications, each with a full report.

The widest first, by the number of use cases each covers. Every report shows what changed, how it works, who has done it and what can go wrong.

  1. Ecommerce Conversion Optimization HubThis application area focuses on using data and automation to systematically increase online sales conversion, average order value, and margin across ecommerce stores. It spans dynamic and personalized pricing, product discovery and recommendations, merchandising automation, and large-scale content generation for product pages, ads, and on-site experiences. Rather than operating as isolated tools, these capabilities work together to remove friction from the customer journey—from search and browsing to cart and checkout—while tuning offers and experiences in real time. AI and advanced analytics enable this by continuously learning from shopper behavior, competitive signals, and operational constraints such as logistics and shipping costs. Models power dynamic pricing for thousands of SKUs, generate and optimize creative assets and copy for multiple channels, and improve product search and recommendations using richer semantic and commonsense understanding of products and queries. The result is smarter, always-on optimization of the ecommerce funnel that would be impossible to manage manually at scale.Batch → RT77 use cases
  2. On-Site Personalization EngineEcommerce AI personalization engines use customer behavior, context, and product data to generate highly tailored product recommendations, content, and offers across the shopping journey. They power intelligent shopping assistants, dynamic merchandising, and checkout relevance to increase conversion rates, average order value, and customer lifetime value. By automating large-scale, real-time personalization, they reduce manual merchandising effort while improving shopping experience quality.Batch → RT19 use cases
  3. Visual Product SearchThis AI solution powers image- and multimodal-based product search, letting shoppers find items by snapping a photo, uploading an image, or using rich visual cues instead of text-only queries. By understanding product attributes, style, and context, it delivers more relevant results, boosts product discovery, and increases conversion rates while reducing search friction across ecommerce sites and apps.Manual → Vision14 use cases
  4. Demand and Inventory Forecasting IntelligenceThis AI solution predicts product- and category-level demand across channels, then optimizes pricing, inventory, and logistics decisions around those forecasts. By unifying signals from shopper behavior, historical sales, promotions, and external factors, it powers smarter replenishment, dynamic pricing, and personalized recommendations. Retailers and brands use it to cut stockouts and overstocks, lift conversion and basket size, and improve gross margin and cash flow efficiency.React → Pred13 use cases
  5. Trend and Assortment Signal IntelligenceEcommerce AI Trend Intelligence aggregates signals from customer behavior, pricing data, inventory flows, and logistics performance to uncover emerging demand and operational patterns. It powers smarter decisions on assortment, dynamic pricing, upsell paths, and inventory positioning, enabling retailers to grow revenue while minimizing stockouts, overstock, and fulfillment costs.Silo → Int9 use cases
  6. AI Visual Merchandising OptimizationThis AI solution uses AI to optimize how products are visually presented and discovered across ecommerce sites—from automated photo editing and on-site merchandising to visual search and SEO-driven product discovery. By continuously testing and refining images, layouts, and search experiences, it increases product visibility, improves shopper engagement, and lifts conversion rates across online stores.Recommendation Systems8 use cases
All 30 applications, A to Z

Work with us

The catalog shows what is possible. A sprint decides what you should fund.

Reading every report still leaves the hardest question open: which move is worth funding first, here, given your constraints. That is the sprint: two weeks, fixed scope, and a candid answer either way.

Book a 30-minute callHow the sprint works

Two weeks Fixed scope A candid fit-or-no-fit answer

  1. 01

    Three ranked opportunities

    A decision-ready shortlist scored against value, feasibility, evidence, risk, and time to proof.

  2. 02

    One pilot-ready workflow

    A bounded operating design with actors, handoffs, data needs, controls, and success measures.

  3. 03

    A defensible 90-day plan

    Build-versus-buy guidance, ROI assumptions, risk treatment, sequencing, and executive decisions.

06Rules & standards

Rules and standards to check, with their scope and sources.

Selected US and EU references for these workflows. Applicability depends on jurisdiction, organization, data and intended use. The planning notes are Atlas editorial guidance; they are not official implementation-time estimates or an exhaustive list of obligations.

  1. Truthful advertising and substantiated claims

    Law or regulation

    United States · FTC jurisdiction

    Advertising claims must be truthful, non-deceptive and supported by evidence. These principles also matter when AI drafts product descriptions, recommendations or campaign copy.

    FTC: Truth in Advertising

    Source checked

    Project planning note

    Review generated product claims, comparisons and promotions against evidence and the actual offer.

  2. Digital Services Act · online platforms

    Law or regulation

    European Union · covered intermediary services

    The DSA applies different duties by service type and size. Covered online platforms have advertising transparency duties and restrictions on certain profiling-based ads; a retailer is not automatically an online platform.

    EUR-Lex: Regulation (EU) 2022/2065, Articles 19, 26 and 28

    Source checked

    Project planning note

    Determine whether the service is a marketplace or another covered intermediary before applying platform-specific duties.

Your next step

Find your first move in e-commerce.

Ask the workspace which move fits your company. Add your size, systems and constraints, and it works from these 30 applications to a plan your team can act on.

Plan your first AI move

Rather talk it through? Book a 30-minute call