AI & Tech Intelligence · Retail
AI in Retail and E-commerce: What It Does, and What the Data Shows
AI in retail, by the numbers: McKinsey sees $240-390bn in value, personalization lifts revenue 10-15%, and Klarna's AI handles two-thirds of chats. Sourced.
TL;DR — McKinsey estimates generative AI could unlock $240-390 billion in annual economic value for retailers, equal to an industry-wide margin increase of 1.2 to 1.9 percentage points (McKinsey, 'Generative AI in retail: LLM to ROI', 2024).
McKinsey · McKinsey · McKinsey · McKinsey | 2,834 words · 17 sections | data: CSV + JSON
Executive summary
AI in retail has moved from pilots to profit-and-loss impact, but the value is concentrated in a handful of use cases. The single largest sizing comes from McKinsey, which estimates generative AI could add $240-390 billion in annual value for retailers, on top of the $400-660 billion it earlier attributed to retail and consumer packaged goods across all AI (McKinsey, 2024; 2023). Where does that value land? Overwhelmingly in personalization, demand forecasting, pricing and customer service. Personalization is the most proven: McKinsey finds it typically lifts revenue 10-15%, and 71% of consumers now expect it. AI demand forecasting is the highest-ROI operational lever, cutting forecast errors 20-50% and out-of-stock losses up to 65%. Generative AI is the newest wave, powering product-content generation (Walmart used large language models to create or improve 850 million pieces of catalog data), conversational shopping assistants (Zalando, live in 25 markets) and customer service (Klarna's assistant handled two-thirds of chats in month one, worth an estimated $40 million in 2024 profit). Adoption is wide but early: NRF's 2025 survey found most retailers still spend 5% or less of tech budgets on AI. The open questions are governance and fairness -- algorithmic and personalized pricing sit squarely in the sights of EU consumer law and the AI Act. Net: the technology's retail ROI is real and measurable, but it is unevenly captured, concentrated in personalization, forecasting and service, and increasingly shaped by regulation.
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Key findings
Generative AI could add $240-390 billion a year for retailers
McKinsey's 2024 report 'Generative AI in retail: LLM to ROI' estimates generative AI could unlock $240 billion to $390 billion in annual economic value for retailers, equivalent to a margin increase across the industry of 1.2 to 1.9 percentage points. This is additive to McKinsey's earlier 2023 estimate that AI and generative AI could generate $400-660 billion a year across retail and consumer packaged goods. The value is concentrated, not evenly spread: customer engagement (marketing and personalization) and operations (supply chain, forecasting) capture most of it. These are potential-value estimates based on use-case analysis, not realized results, and depend on adoption and execution.
Source: McKinsey · 2024 · confidence: Medium-High
Klarna's AI assistant did the work of 700 agents in month one
Klarna reported on 27 February 2024 that its OpenAI-powered AI assistant, live for one month across 23 markets in 35+ languages, handled two-thirds of its customer-service chats -- 2.3 million conversations, the equivalent workload of 700 full-time agents. Klarna said it cut average resolution time from 11 minutes to under 2, reduced repeat inquiries by 25%, matched human agents on customer-satisfaction scores, and expected the assistant to drive a $40 million profit improvement in 2024. This is a company disclosure. Notably, by 2025 Klarna publicly rebalanced its approach and reintroduced human agents for complex cases -- a useful caution that first-month metrics are not steady-state.
Source: Klarna · 2024 · confidence: High
Personalization typically lifts retail revenue 10-15%
McKinsey research finds personalization most often drives a 10-15% revenue lift, with company-specific results ranging from 5% to 25% depending on sector and ability to execute. It also finds that faster-growing companies drive 40% more of their revenue from personalization than slower-growing peers, that 71% of consumers expect personalized interactions, and that 76% get frustrated when they do not receive them. Recommendation engines -- the backbone of e-commerce personalization -- are the most mature AI application in retail, deployed by Amazon, Netflix, Zalando, Shopify merchants and virtually every large online store.
Source: McKinsey · 2023 · confidence: High
2024-2026: generative AI moves into content, catalog and conversation
The newest shift is from predictive AI (recommendations, forecasting) to generative AI in customer-facing and content workflows. Zalando scaled its generative-AI shopping assistant to all 25 markets in October 2024 and reported it drove 52% longer session duration than a generic chatbot; in Q4 2024 roughly 70% of Zalando's editorial campaign imagery was AI-generated, cutting production time from 6-8 weeks to 3-4 days and imagery cost by up to 90% (Zalando; Business of Fashion, 2024). Walmart disclosed using large language models to create or improve more than 850 million pieces of catalog data -- work it said would otherwise have required 100 times the head count (Walmart, 2024). Meanwhile McKinsey's State of AI 2025 found 88% of organizations now use AI in at least one function, but only about a third have scaled it, and just 39% report any enterprise EBIT impact -- a reminder that retail's headline value figures remain largely unrealized potential.
What is AI in retail, and why now?
AI in retail is the use of machine learning and, increasingly, generative AI to automate and optimize commerce decisions -- what to recommend, how much to stock, what price to set, and how to serve customers. It spans predictive systems (recommendation engines, demand forecasts, fraud detection) and generative systems (product-content creation, chatbots, visual search). McKinsey estimates AI and generative AI could generate $400-660 billion a year across retail and consumer packaged goods, with generative AI alone adding a further $240-390 billion for retailers (McKinsey, 2023; 2024).
Why now? Three forces converged. Data: e-commerce and loyalty programs produce the behavioral signals AI needs. Models: since 2023, large language models made product content, search and service economically automatable at scale. Adoption: McKinsey's State of AI 2025 found 88% of organizations use AI in at least one function, up sharply from prior years. Retail is a natural early adopter because its decisions are high-volume, data-rich and directly tied to margin.
But the value is concentrated, not universal. The same McKinsey survey found only about a third of organizations have scaled AI across the enterprise, and just 39% report any measurable EBIT impact. In retail, the proven returns cluster in four areas -- personalization, forecasting, pricing and customer service -- which the rest of this report examines in turn.
How does AI personalize shopping and recommend products?
AI personalizes shopping by predicting what each customer is most likely to want, using recommendation engines trained on browsing, purchase and demographic signals -- and McKinsey finds this typically lifts revenue by 10-15% (range 5-25%). Recommendation systems are the most mature AI use case in retail, powering 'customers who bought this also bought' modules, personalized homepages, email targeting and search ranking across Amazon, Zalando, Shopify stores and most large e-commerce sites.
The scale of the effect is significant but often overstated with a single number. Amazon is frequently cited as generating around 35% of sales from its recommendation engine; that figure traces to a 2013 McKinsey estimate and should be read as dated and directional rather than a current, audited metric. What is better documented is the demand side: McKinsey finds 71% of consumers expect personalized interactions and 76% are frustrated when they do not get them, and that faster-growing companies earn 40% more of their revenue from personalization than slower-growing peers.
Generative AI is now extending personalization from ranking products to conversation. Zalando's AI assistant lets shoppers ask questions like 'what should I wear to a birthday in Barcelona in November?' and factors in occasion, weather and location; Zalando reported it drove 52% longer session duration than a generic chatbot and rolled it out to all 25 markets in 2024 (Zalando, 2024). The frontier is 'agentic' shopping, where assistants search, compare and check out on the customer's behalf.
How does AI improve demand forecasting and inventory management?
AI improves demand forecasting by learning nonlinear patterns from many signals -- sales history, promotions, weather, local events, web traffic -- and McKinsey finds it can cut forecast errors by 20-50% and reduce lost sales from out-of-stocks by up to 65%. This is retail's highest-ROI operational use case because forecast accuracy drives everything downstream: inventory levels, working capital, markdowns and waste.
The operational gains are concrete. McKinsey's supply-chain work links AI forecasting to lower warehousing costs (5-10%) and reduced safety stock, freeing working capital without raising stockout risk. Walmart is the reference case: it disclosed building generative-AI and machine-learning engines that forecast demand, plan inventory placement and reroute stock, already live across markets including Mexico, Canada and Costa Rica, describing systems that 'even anticipate' supply-chain changes (Walmart, 2024-2025).
For grocery and fresh categories, better forecasting also cuts spoilage and food waste -- a sustainability as well as a margin story. The limitation is data quality: AI forecasts degrade during genuine demand shocks (pandemics, viral trends) where history is not predictive, which is why leading retailers pair models with human planners rather than fully automating replenishment.
What is AI dynamic pricing, and where does it get risky?
AI dynamic pricing uses algorithms to adjust prices in near real time based on demand, competitor prices, inventory and time -- a practice the European Commission found in its 2017 e-commerce inquiry was already used by roughly one-third of online retailers to track and match rivals' prices automatically. Airlines and ride-hailing pioneered it; e-commerce and marketplaces now apply it at SKU level, with Amazon reprice thousands of times a day.
The economics are attractive: dynamic pricing captures willingness-to-pay and clears inventory more efficiently. But two distinct practices must be separated. Competitor-based dynamic pricing (same price for everyone, adjusted over time) is broadly legal. Personalized pricing (different prices for different individuals based on their data) is legally and reputationally hazardous -- and where regulators are focused.
The risks are fairness, discrimination and trust. Algorithms must not discriminate against protected groups, and personalized pricing can erode consumer trust fast if perceived as exploitative. EU law now requires that when a price is personalized through automated decision-making, the consumer must be told (Consumer Rights Directive, as amended by the 2019 Omnibus Directive). The EU AI Act (Regulation 2024/1689) and the proposed Digital Fairness Act (consultation launched July 2025) signal tightening scrutiny of algorithmic pricing across the single market.
How is generative AI used in retail -- content, chatbots and visual search?
Generative AI in retail creates product content, powers conversational shopping and enables visual search -- the newest and fastest-growing category of retail AI, which McKinsey sizes at $240-390 billion in annual value potential for retailers (McKinsey, 2024). Unlike predictive AI, generative models produce new outputs: descriptions, images, chat responses and code.
Product content and catalog is the clearest early win. Walmart used large language models to create or improve more than 850 million pieces of catalog data -- product titles, descriptions and attributes -- work it said would have required 100 times the head count without AI (Walmart, 2024). Zalando generated roughly 70% of its Q4 2024 editorial campaign imagery with AI, cutting production from 6-8 weeks to 3-4 days and imagery cost by up to 90% (Business of Fashion, 2024). H&M created AI 'digital twins' of models in 2025.
Conversational commerce and visual search are the customer-facing layer. Generative assistants (Zalando, Amazon's Rufus, Shopify's Sidekick) answer natural-language queries and guide discovery; visual search lets shoppers photograph an item to find similar products. These tools are early but scaling fast -- the risk is factual accuracy ('hallucinated' product claims), which retailers mitigate with retrieval grounding against verified catalog data.
How does AI transform customer service and fulfilment?
AI transforms retail customer service by automating high-volume, repetitive inquiries -- Klarna's AI assistant handled two-thirds of its chats within a month of launch, doing the work of 700 agents and cutting resolution time from 11 minutes to under 2 (Klarna, 2024). NRF's 2025 survey found 36% of retailers use AI to enhance customer communication and 21% deploy AI-driven avatars for service (NRF, 2025).
The Klarna case is the most-cited data point and the most instructive. The first-month results were striking -- 2.3 million conversations, satisfaction on par with humans, an estimated $40 million profit improvement in 2024 -- but by 2025 Klarna rebalanced toward a hybrid model, reintroducing human agents for complex cases. The durable lesson: AI excels at tier-1, high-frequency queries (order status, returns, refunds) and struggles with edge cases requiring judgment or empathy.
In fulfilment, AI optimizes warehouse operations, routing and labor planning. Walmart's generative-AI supply-chain engines align labor and transportation and reroute inventory to keep products moving; computer vision assists shelf-availability checks and quality inspection. The pattern across service and fulfilment is the same as forecasting: automate the routine, keep humans on the exceptions.
What are the main AI use cases in retail?
The main AI use cases in retail cluster into six categories -- personalization, forecasting, pricing, content generation, customer service and fulfilment -- each with a distinct mechanism and documented benefit. The table below maps what each does to a real example and its measured or sourced benefit.
| Use case | What AI does | Benefit / example (source) |
|---|---|---|
| Product recommendations | Predicts likely purchases from behavioral data | 10-15% revenue lift from personalization (McKinsey) |
| Demand forecasting | Learns demand patterns from many signals | Forecast error down 20-50%; lost sales down up to 65% (McKinsey) |
| Inventory & supply chain | Plans stock placement, reroutes inventory | Walmart gen-AI engines live in multiple markets (Walmart, 2024-25) |
| Dynamic pricing | Adjusts prices in near real time | ~1/3 of online retailers auto-track rivals (EU Commission, 2017) |
| Content generation | Writes product copy, generates imagery | Walmart improved 850M catalog data points (Walmart, 2024) |
| Conversational shopping | Answers natural-language queries, guides discovery | Zalando assistant: +52% session duration (Zalando, 2024) |
| Customer service | Automates tier-1 inquiries via chatbots | Klarna AI: 2/3 of chats, 11 to under 2 min (Klarna, 2024) |
How widely have retailers adopted AI, and what is the impact?
Retail AI adoption is broad but shallow: NRF's 2025 survey of retail AI leaders found 47% use AI for tailored product suggestions and 86% have AI-governance policies, yet 77% still allocate 5% or less of their technology budget to AI. Across all sectors, McKinsey's State of AI 2025 found 88% of organizations use AI in at least one function, but only about a third have scaled it. The table below summarizes the leading adoption and impact figures by source.
Read the table as a maturity curve: usage is near-universal, but scaled deployment and measured financial impact lag well behind the pilots. That gap between adoption and impact is the central story of retail AI in 2026 -- the value McKinsey sizes at hundreds of billions is largely still potential.
| Metric | Figure | Scope (source, year) |
|---|---|---|
| Organizations using AI in a function | 88% | All sectors (McKinsey State of AI, 2025) |
| Retailers using AI for product suggestions | 47% | US retail AI leaders (NRF, 2025) |
| Retailers with AI-governance policies | 86% | US retail AI leaders (NRF, 2025) |
| Retailers spending 5% or less of tech budget on AI | 77% | US retail AI leaders (NRF, 2025) |
| Gen-AI value potential for retailers | $240-390bn/yr | Global (McKinsey, 2024) |
| Organizations reporting any enterprise EBIT impact | 39% | All sectors (McKinsey, 2025) |
What are the risks of AI in retail, and how is it regulated?
The main risks of AI in retail are algorithmic bias, data privacy, pricing fairness and factual reliability -- and in the EU these are increasingly regulated rather than left to self-governance. Personalized pricing, recommendation bias and generative 'hallucinations' each carry distinct legal and reputational exposure, which is why 86% of retail AI leaders now maintain governance policies (NRF, 2025).
European law is the tightest. When a price is personalized through automated decision-making, the EU Consumer Rights Directive (as amended by the 2019 Omnibus Directive) requires disclosure to the consumer. The EU AI Act (Regulation 2024/1689) adds transparency and risk obligations, and the proposed Digital Fairness Act (consultation launched July 2025) targets manipulative and unfair digital practices. GDPR governs the customer data that personalization and pricing depend on. The table below maps each risk to its mitigation.
| Risk | Why it matters | Mitigation / regulation |
|---|---|---|
| Algorithmic bias | Recommendations/pricing can disadvantage protected groups | Fairness audits; anti-discrimination law; AI Act |
| Personalized pricing | Different prices per person erodes trust; may be unlawful | Mandatory disclosure (EU Omnibus Directive) |
| Data privacy | Personalization relies on sensitive behavioral data | GDPR consent and data-minimization |
| Gen-AI hallucination | Chatbots may state false product/price claims | Retrieval grounding against verified catalog |
| Over-automation of service | AI mishandles complex/emotional cases | Human-in-the-loop hybrid (per Klarna 2025 reversal) |
How are European and Nordic retailers using AI?
European and Nordic retailers are among the most visible AI adopters in commerce, led by Swedish-linked firms Klarna, H&M, Zalando (Berlin, Nordic-heavy market) and IKEA. Klarna's OpenAI-powered assistant became the sector's most-cited customer-service case, handling two-thirds of chats and an estimated $40 million in 2024 profit impact before rebalancing toward a hybrid model in 2025 (Klarna, 2024-2025).
In fashion, Zalando scaled its generative-AI shopping assistant to all 25 European markets in 2024 and generated around 70% of Q4 2024 editorial imagery with AI, while Sweden's H&M created AI digital twins of models in 2025 (Zalando; Business of Fashion, 2024). IKEA has integrated generative-AI tools into customer-facing design and shopping journeys. These are disclosures and press reports, not audited outcomes, but they establish that Nordic and European retail is at the frontier of generative-AI deployment.
The regulatory context is distinctively European: the EU AI Act, GDPR and the proposed Digital Fairness Act mean Nordic retailers innovate inside tighter guardrails on data, pricing transparency and automated decisions than US peers. The Nordic advantage is high digital maturity and consumer trust; the constraint is that personalization and dynamic pricing must be built compliance-first.
What is the outlook for AI in retail?
The outlook is that AI's retail value shifts from pilots to realized P&L impact, concentrated in personalization, forecasting and service -- but McKinsey's data shows most of the $240-390 billion potential is still unrealized, with only 39% of organizations reporting any EBIT impact so far (McKinsey, 2024-2025). The gap between adoption (88% using AI) and impact (a third scaled) is the defining challenge of the next few years.
Three trends will shape 2026-2028. First, agentic commerce: AI assistants that search, compare and check out autonomously, shifting the interface from browsing to delegation. Second, generative content and search becoming default, as catalog copy, imagery and on-site search go AI-native at Walmart-and-Zalando scale. Third, regulation catching up, with the EU AI Act's high-risk obligations phasing in and pricing transparency enforced.
The honest forecast: retail AI is neither hype nor a solved problem. The ROI in forecasting and personalization is proven and repeatable; the value in generative and agentic commerce is real but largely prospective; and the winners will be retailers that operationalize AI across the enterprise -- not just pilot it -- while staying inside tightening consumer-protection rules.
Scoreboard (machine-readable data)
Every headline indicator with its value, period, source and confidence. Free to reuse under CC BY 4.0.
| Indicator | Value | Period | Source | Conf. |
|---|---|---|---|---|
| Gen-AI annual value for retailers (upper est.) | 390 USD billion | 2024 | McKinsey | Medium-High |
| Industry-wide margin lift from gen AI (upper) | 1.9 percentage points | 2024 | McKinsey | Medium-High |
| Organizations using AI in a function | 88 pct | 2025 | McKinsey | High |
| Revenue lift from personalization (upper) | 15 pct | 2023 | McKinsey | High |
| AI demand-forecast error reduction (upper) | 50 pct | 2024 | McKinsey | Medium |
| Klarna chats handled by AI, month one | 66 pct | 2024 | Klarna | High |
Methodology & verification
This report synthesizes primary and named-institution sources on AI in retail and e-commerce, attributing every statistic to its source, year and scope. Sizing estimates come from McKinsey ('Generative AI in retail: LLM to ROI', 2024; 'The economic potential of generative AI', 2023; 'State of AI', 2025; personalization research 2021-2023). Adoption figures come from the National Retail Federation's 2025 survey of 56 US retail AI leaders and McKinsey's global State of AI survey. Company-specific results (Klarna, Zalando, Walmart, H&M) are corporate disclosures or reputable press reports (Business of Fashion), clearly labeled as such rather than independently audited. Value-potential figures are use-case estimates of possible impact, not realized results. Where a widely cited figure is dated or weakly sourced (e.g. the '35% of Amazon sales' claim), we flag it explicitly. Every URL was checked as of 30 July 2026; McKinsey pages block automated requests but resolve in-browser at the cited canonical URLs.
Data dictionary
| Field | Type | Description |
|---|---|---|
| genai_value_retailers_usd_bn | number (USD billion) | McKinsey estimate of annual economic value generative AI could unlock for retailers ($240-390bn range; scoreboard shows upper bound). |
| personalization_revenue_lift_pct | percentage | McKinsey estimate of typical revenue lift attributable to personalization (10-15% typical, 5-25% full range by sector and execution). |
| forecast_error_reduction_pct | percentage | McKinsey estimate of the reduction in demand-forecast error achievable with AI-driven forecasting (20-50% range). |
Frequently asked questions
What is AI in retail?
AI in retail is the use of machine learning and generative AI to automate and optimize commerce decisions -- product recommendations, demand forecasting, pricing, content creation and customer service. McKinsey estimates AI and generative AI could generate $400-660 billion a year across retail and consumer packaged goods, with generative AI adding a further $240-390 billion for retailers (McKinsey, 2023; 2024).
How does AI personalize product recommendations?
AI personalizes recommendations by predicting what each shopper is most likely to buy from browsing, purchase and demographic signals, using recommendation engines. McKinsey finds personalization typically lifts revenue 10-15%, and 71% of consumers now expect personalized interactions. Amazon, Zalando and most large e-commerce sites run these systems (McKinsey; Zalando, 2024).
How much does AI improve demand forecasting?
AI can cut demand-forecast errors by 20-50% and reduce lost sales from out-of-stocks by up to 65%, according to McKinsey -- retail's highest-ROI operational use case. It also lowers warehousing costs 5-10% and frees working capital by reducing safety stock. Walmart runs generative-AI forecasting engines across multiple markets (McKinsey; Walmart, 2024).
Is AI dynamic pricing legal in the EU?
Competitor-based dynamic pricing (same price for all, adjusted over time) is broadly legal; the EU found about a third of online retailers already auto-track rivals' prices (2017). Personalized pricing -- different prices per individual -- must be disclosed under the EU Consumer Rights Directive as amended by the 2019 Omnibus Directive, and faces tightening scrutiny under the AI Act and proposed Digital Fairness Act.
How is generative AI used in retail?
Generative AI creates product content, powers conversational shopping assistants and enables visual search. Walmart used large language models to improve 850 million catalog data points; Zalando generated ~70% of its Q4 2024 editorial imagery with AI, cutting cost up to 90%. McKinsey sizes generative AI's retail value potential at $240-390 billion a year (Walmart; Zalando, 2024; McKinsey, 2024).
Can AI replace retail customer service agents?
AI can automate high-volume tier-1 inquiries but not fully replace agents. Klarna's AI handled two-thirds of chats in month one -- the work of 700 agents -- and cut resolution time from 11 minutes to under 2 (Klarna, 2024). But by 2025 Klarna reintroduced humans for complex cases, confirming a hybrid model where AI handles routine queries and humans handle exceptions.
Which retailers use AI most effectively?
Amazon (recommendations, pricing), Walmart (forecasting, catalog, supply chain), Klarna (customer service), Zalando (generative shopping assistant and imagery) and H&M (AI model imagery) are the most-documented cases. Shopify and IKEA have embedded generative AI in merchant tools and shopping journeys. Nordic and European firms are notably at the frontier (company disclosures, 2024-2025).
How many retailers have actually adopted AI?
Adoption is broad but shallow. NRF's 2025 survey found 47% of US retailers use AI for tailored product suggestions and 86% have AI-governance policies, yet 77% still spend 5% or less of their tech budget on AI. Across sectors, McKinsey found 88% use AI in a function but only about a third have scaled it (NRF, 2025; McKinsey, 2025).
Glossary
- Recommendation engine
- An AI system that predicts and suggests products a shopper is likely to want, based on behavioral and purchase data. The most mature AI use case in retail; drives a typical 10-15% revenue lift. ↗
- Demand forecasting
- Using AI to predict future product demand from historical and external signals, to optimize inventory. AI can cut forecast errors 20-50% and out-of-stock losses up to 65%. ↗
- Dynamic pricing
- Algorithmic adjustment of prices in near real time based on demand, competitors and inventory. Competitor-based pricing is broadly legal; personalized pricing must be disclosed under EU law. ↗
- Generative AI in retail
- AI that creates new outputs -- product copy, imagery, chatbot responses, visual search -- rather than only predicting. McKinsey sizes its retail value potential at $240-390bn a year. ↗
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@techreport{affarslivet_ai_in_retail,
title = {AI in Retail and E-commerce: What It Does, and What the Data Shows},
author = {{Affärslivet Research}},
year = {2026},
note = {Version 1.0},
url = {https://xn--affrslivet-s5a.com/en/reports/ai-in-retail}
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Sources
- McKinsey — Generative AI in retail: LLM to ROI (2024)
- McKinsey — The economic potential of generative AI (2023)
- McKinsey — The State of AI (2025)
- McKinsey — What is personalization? (Next in Personalization)
- NRF — Retail AI Trends 2025
- Klarna — AI assistant handles two-thirds of customer service chats (Feb 2024)
- Business of Fashion — Zalando uses AI to speed up marketing campaigns (2024)
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