PLAYBOOK ATLASPlaybook Atlas

Retail

AI in retail: explore 36 applications.

Connect product demand, inventory and customer experience to decisions a retail team can test.

Explore replenishment, pricing, promotions, loyalty and store operations, with each decision’s constraints and outcomes visible.

Plan your first AI move See the 36 applications

Where the 36 applications sit

Explore the domains
  1. 11Customer Relationship
  2. 11Inventory Management
  3. 10Store Operations
  4. 9Merchandising
  5. 4Pricing & Promotions

01Why AI now

Why retail is moving on AI, in three figures.

Each figure links to its source.

  1. Stockout losses: $1.2T worldwide in 2023

    IHL Group estimated that global inventory distortion cost $1.77 trillion in 2023, of which out-of-stocks accounted for $1.2 trillion.

    Source: The Food Institute

  2. Gen AI could unlock $240B–$390B for retailers

    McKinsey estimates that generative AI could create $240 billion to $390 billion in economic value for retailers, equivalent to a 1.2- to 1.9-percentage-point industrywide margin increase.

    Source: McKinsey & Company

  3. Retail returns hit $890B in 2024

    Retailers estimated that 16.9% of annual sales would be returned in 2024, creating a major cost and operational burden.

    Source: National Retail Federation

How to evaluate a first project

Separate regular pricing, promotions, clearance and replenishment before selecting a workflow. Evaluate a defined product or store cohort using available stock, margin, forecast error and customer outcomes. Check the accepted change in the selling or inventory system and monitor exceptions during the trial.

02Capability map

Where retail puts AI to work, domain by domain.

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

Inside Customer Relationship

Applications in Customer Relationship

What the use cases ask AI to do

Share of the retail use cases, by the kind of work the AI does.

  1. Reasoning

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

    86%

  2. Agentic

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

    14%

Under 1% each: perception, generation and optimization.

03Workflow ideas

Ways AI could change the work, in retail.

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 30 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 retail uses most, and when to skip them.

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

  1. 01 · 12 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

    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

  3. 03 · 5 applications

    AutoML-Platform

    Managed platform for training and operating predictive models quickly.

    AutoML-Platform, in depth

05The applications

36 retail 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. Retail Demand Forecasting PlatformAI Retail Demand Forecasting uses machine learning and advanced statistical models to predict product-level demand across channels, seasons, and promotions. It supports inventory optimization, supply chain planning, and pricing decisions, reducing stockouts and overstock while improving margins and service levels. Retailers gain more accurate, granular forecasts that directly enhance revenue and working-capital efficiency.React → Pred35 use cases
  2. Retail Dynamic Pricing OrchestratorAI Retail Dynamic Pricing ingests real-time demand, competitor, and inventory data to automatically set and adjust prices across channels. It personalizes offers by segment, optimizes promotions and markdowns, and continuously tests price points. Retailers use it to grow revenue and margin while reducing manual pricing effort and stockouts.Batch → RT27 use cases
  3. Retail Shopper Behavior IntelligenceAI Retail Behavior Intelligence applies behavioral analytics and machine learning across shopper journeys, feedback, and transactions to understand, predict, and influence consumer actions in-store and online. It powers hyper-personalized experiences, autonomous shopping flows, and optimized segmentation and offers while continuously experimenting to improve outcomes. This drives higher conversion, basket size, and loyalty, while reducing wasted spend and enabling more precise, data-driven retail strategy and operations.React → Pred21 use cases
  4. Retail Price Policy OptimizationRetail Price Optimization is the systematic, data-driven setting of product prices across channels, SKUs, and customer segments to maximize revenue, margin, and sell-through while remaining competitive and fair. It continuously balances factors such as demand, inventory levels, competitor prices, seasonality, and customer willingness to pay, moving retailers beyond static or rule-based pricing. Dynamic and personalized pricing extend this by adjusting prices in near real time for specific audiences, contexts, or market conditions. This application matters because manual or spreadsheet-driven pricing cannot keep up with the scale and speed of modern retail and ecommerce. Advanced models learn from historical transactions, real-time signals, and competitor data to recommend or automatically apply optimal prices at granular levels. The result is higher profitability, reduced over-discounting and stockouts, and better alignment of prices with customer expectations—enabling retailers and B2B sellers to compete effectively in fast-moving, price-sensitive markets.Batch → RT19 use cases
  5. Agentic Shopping Journey OrchestratorThis application area focuses on end‑to‑end orchestration of retail shopping and commercial decisions by autonomous digital agents. Instead of forcing customers and staff to manually search, compare, configure, price, and transact, these systems interpret intent (e.g., “a birthday gift for an avid hiker under $100”), explore large product catalogs and market signals, and then plan and execute the optimal shopping journey across channels. They handle product discovery, basket building, checkout, and post‑purchase tasks through conversational interfaces and background task automation. On the operations side, the same agentic layer continuously optimizes pricing, promotions, merchandising, and inventory decisions. By sensing demand, competition, and inventory data in real time, it can simulate scenarios and autonomously adjust prices, offers, and recommendations to maximize both conversion and margin. This shifts retail from static, rule‑based journeys to dynamic, goal‑driven experiences that increase revenue, basket size, and loyalty while reducing service and operational labor. At its core, autonomous shopping orchestration is about turning fragmented, reactive retail processes into proactive, outcome‑optimized flows. It matters because it addresses chronic retail pain points—abandoned carts, low personalization, margin leakage, and operational bottlenecks—while enabling new business models such as cross‑merchant shopping agents and fully autonomous retail systems.Expert → AI11 use cases
  6. Retail Assortment and Product Mix OptimizerAI analyzes shopper behavior, store performance, and channel data to optimize which products are offered, where, and at what depth of assortment across stores and ecommerce. It orchestrates recommendations, personalization, and retail media to present the right products to each customer while maximizing margin, basket size, and inventory turns. Retailers gain higher revenue and profitability with leaner assortments and more relevant shopping experiences across omnichannel touchpoints.Silo → Int10 use cases
All 36 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

    Check generated promotions and recommendations against product evidence, availability and the actual offer.

  2. GDPR · personal data and automated decisions

    Law or regulation

    European Union / EEA · processing within GDPR scope

    Personal-data processing needs an applicable lawful basis and safeguards. Article 9 adds conditions for special-category data; Article 22 addresses certain solely automated decisions with legal or similarly significant effects. Consent is not the only lawful basis.

    EUR-Lex: GDPR, Articles 3, 6, 9, 22 and 35

    Source checked

    Project planning note

    Define the lawful basis and safeguards for in-scope customer profiling, especially sensitive data or significant automated decisions.

07The AI graveyard

One failure, and the lesson it paid for.

  1. Target Pregnancy Prediction Backlash

    2012 · Source

    Reputational damage

    What went wrong

    AI correctly predicted customer pregnancy before she told family. Sent targeted baby ads to teen's home. PR disaster despite technical success.

    The lesson

    Just because AI can predict something doesn't mean you should act on it. Consider customer comfort, not just accuracy.

Your next step

Find your first move in retail.

Ask the workspace which move fits your company. Add your size, systems and constraints, and it works from these 36 applications to a plan your team can act on.

Plan your first AI move

Rather talk it through? Book a 30-minute call