Machine Learning in Retail: Case Studies from Walmart & Target
- Muiz As-Siddeeqi
- Oct 31
- 49 min read
Machine Learning in Retail: Case Studies from Walmart & Target
Picture this: A hurricane barrels toward Florida. Within minutes—not hours—an AI system at Walmart reroutes thousands of shipments, predicts a spike in battery and water sales by zip code, and automatically adjusts stock levels across 150 distribution centers. Seven days later, when a damaged facility comes back online, customers never noticed the disruption. That is not science fiction. That is machine learning in retail today, transforming how the world's largest companies anticipate demand, negotiate contracts, and deliver value to shoppers.
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TL;DR
Walmart uses machine learning to power demand forecasting, reduce stockouts, save 30 million unnecessary driving miles through route optimization, and automate supplier negotiations with 68% success rates and 3% average cost savings.
Target deployed a generative AI chatbot to nearly 2,000 stores in 2024, enhanced inventory management with predictive analytics, and implemented personalized customer experiences that boosted loyalty and conversion rates.
The global AI in retail market grew from $11.61 billion in 2024 and is projected to reach $40.74–$96.13 billion by 2030, driven by machine learning adoption (Grand View Research, 2024; Mordor Intelligence, 2025).
Retailers using AI and machine learning saw 8% annual profit growth in both 2023 and 2024, outpacing competitors who did not adopt these technologies (IHL Group, December 2023).
Key challenges include data quality issues, talent shortages (41% of retailers cite lack of AI/ML expertise), privacy concerns (42% of North American firms), and integration complexity with legacy systems.
Both companies achieved measurable ROI: Walmart reported 26.18% year-over-year EPS growth tied to its AI framework and 30% logistics cost savings, while Target improved inventory turnover ratios and reduced clearance sales.
Machine learning in retail uses algorithms to analyze sales data, customer behavior, and external factors like weather to predict demand, personalize shopping experiences, optimize inventory, and automate operations. Leading retailers like Walmart and Target have deployed ML systems that reduced stockouts, increased profit margins by 8% annually, automated supplier negotiations with 68% success rates, and delivered personalized recommendations that boosted customer loyalty—all documented with real machine learning in retail case studies from 2023 to 2025.
Table of Contents
What Is Machine Learning in Retail?
Machine learning in retail refers to the use of algorithms and statistical models that enable computer systems to improve their performance on specific tasks through experience—without being explicitly programmed for every scenario. In practical terms, ML systems analyze vast amounts of data (sales records, customer behavior, weather patterns, social media trends, supplier lead times) to identify patterns and make predictions.
Retailers apply machine learning across four core areas: demand forecasting, inventory optimization, personalized customer experiences, and operational automation. The technology sits at the intersection of artificial intelligence, big data analytics, and cloud computing, processing information at speeds and scales impossible for human teams.
Unlike traditional rule-based systems that follow fixed if-then logic, machine learning models continuously adapt as new data arrives. When Hurricane Ian disrupted Walmart's Florida operations in 2022, the company's ML systems automatically rerouted shipments and adjusted inventory within hours—not because engineers pre-programmed hurricane responses, but because the algorithms learned from historical disruption patterns and real-time data streams (CIO Dive, December 13, 2022).
Why Retailers Are Investing Billions in ML
The business case for machine learning in retail comes down to three measurable outcomes: revenue growth, cost reduction, and competitive advantage.
First, the financial returns are documented. Retailers using AI and machine learning technologies saw annual profit growth of approximately 8% in both 2023 and 2024, significantly outperforming competitors who did not adopt these solutions (IHL Group, December 14, 2023). That 8% profit lift translates to billions of dollars for large chains.
Second, McKinsey estimates that generative AI alone could deliver $400 billion to $660 billion in annual value to the retail sector through improvements in customer service, marketing and sales, and inventory and supply chain management (Itransition, 2025). These are not speculative figures—they are based on documented productivity gains and cost savings from early adopters.
Third, customer expectations have shifted. Today's shoppers expect personalized recommendations, real-time inventory visibility, and seamless omnichannel experiences. A 2024 Grocery Doppio analysis found that 81% of consumers want personalized offers, yet 92% feel that grocery retailers lag in delivering that personalization (Grocery Doppio, 2024). Machine learning closes that gap by analyzing individual shopping patterns and preferences at scale.
Fourth, operational efficiency drives adoption. Walmart avoided 30 million unnecessary driving miles through ML-powered route optimization, cutting fuel costs and reducing carbon emissions (Virtasant, 2024). Target reduced instances of overstock and understock significantly after implementing predictive analytics, improving inventory turnover ratios and reducing clearance sales (DigitalDefynd, March 17, 2025).
Finally, competition forces investment. When Amazon deploys sophisticated ML algorithms for dynamic pricing, personalized search, and predictive inventory placement across its fulfillment network, traditional retailers must match those capabilities or risk losing market share.
The Retail AI Market Landscape (2024–2025)
The numbers tell a story of explosive growth and mainstream adoption.
The global artificial intelligence in retail market was valued at $11.61 billion in 2024 and is projected to reach $40.74 billion by 2030, growing at a compound annual growth rate of 23.0% (Grand View Research, 2024). Other research firms report similar trajectories: Mordor Intelligence estimates the market at $14.24 billion in 2025, growing to $96.13 billion by 2030 at a CAGR of 46.54% (Mordor Intelligence, July 3, 2025). Straits Research places the 2024 market at $5.43 billion, expanding to $41.23 billion by 2033 at a 26.5% CAGR (Straits Research, 2025).
While exact figures vary by methodology, all major research firms agree on the core trend: retail AI adoption is accelerating rapidly.
Machine learning holds the largest share among AI technologies in retail. The technology accounted for 40.21% of retail AI revenue in 2024, with machine learning expected to lead adoption through 2030 due to its proven track record in demand forecasting, personalization, and fraud detection (Mordor Intelligence, July 3, 2025). Natural language processing is the fastest-growing segment, projected to expand at a 27.6%–34% CAGR as retailers deploy chatbots and voice commerce solutions (P&S Intelligence, 2024; Grand View Research, 2024).
By application, inventory and demand forecasting captured 28.3% of the AI in retail market in 2024, making it the single largest use case (Mordor Intelligence, July 3, 2025). Customer relationship management follows closely, driven by the need to boost retention and service quality through AI-powered search engines, virtual assistants, and chatbots.
North America dominated the market with 33.4%–37.4% revenue share in 2024, fueled by robust cloud infrastructure, venture capital investment, and retailers' willingness to pilot bleeding-edge models (Grand View Research, 2024; Mordor Intelligence, July 3, 2025). However, Asia-Pacific is the fastest-growing region, advancing at an 18.9% CAGR through 2030, supported by mobile-first consumers, government AI programs, and aggressive investment from both local and global retailers (Mordor Intelligence, July 3, 2025).
Enterprise adoption accelerated sharply. Survey data from the AI Index 2025 report and McKinsey studies show that 78% of organizations reported using AI in at least one business function in 2024, up from 55% in 2023. Generative AI use more than doubled, with 71% of firms deploying it in 2024 compared to 33% in 2023 (BayTech Consulting, 2025).
For retail specifically, 53% of business leaders predict AI will have the greatest impact on customer intelligence over the next two years, 50% expect it to transform inventory management, and 49% say it will revolutionize chatbots for customer service (KPMG survey of 950 decision-makers including 150 from retail, cited in CIO Dive, March 24, 2021).
Case Study 1: Walmart's Machine Learning Ecosystem
Walmart operates the world's largest retail supply chain: 10,500 stores and e-commerce sites across 19 countries, serving approximately 240 million customers weekly, supported by 100,000+ global suppliers, 150 distribution centers, and one of the largest private truck fleets (Walmart Global Tech, March 14, 2024). Managing that complexity at every day low costs requires machine learning at every layer.
Walmart's Element Platform
In March 2024, Walmart unveiled Element, a proprietary machine learning platform built to replace vendor lock-in and reduce dependency on expensive cloud services. Element is a multi-cloud, multi-region ML infrastructure that allows data scientists and engineers to switch between Amazon Web Services, Microsoft Azure, and Google Cloud without reconfiguring or retuning models (Walmart Global Tech, March 14, 2024).
The platform has been deployed across multiple clouds and regions, supporting around two dozen services and spawning workloads distributed among thousands of CPU cores and hundreds of GPUs. Teams using Element report significantly reduced time spent evaluating external vendors or multiple tools, lower overall startup time, and speedier development, deployment, and operationalization through standardized MLOps processes (Walmart Global Tech, March 14, 2024).
Element powers several critical use cases. One is channel performance analytics: Walmart provides its channel partners with an AI tool that evaluates sales data, promotions, and shelf assortments, then shares actionable insights and recommendations. The sheer volume of items sold across Walmart's network—millions of SKUs—would create an analysis and feature engineering nightmare without automated ML pipelines (Walmart Global Tech, March 14, 2024).
By 2025, Walmart transitioned from model-centric AI to a system-centric architecture, deploying a unified ecosystem of purpose-built agents to automate workflows, enhance customer experiences, and optimize supply chains. This strategic pivot positions Walmart as a leader in agentic AI, a domain projected to dominate the $1.7 trillion agentic commerce market (AInvest, August 30, 2025).
Demand Forecasting and Inventory Management
Walmart's AI systems combine machine learning models, predictive analytics, and automation to keep shelves stocked and operations lean. The company integrates data from point-of-sale systems, weather forecasts, local events (sporting events, festivals, concerts), social media trends for viral products, and supply chain inputs like supplier lead times and warehouse stock levels. This creates a 360-degree view of demand drivers far beyond what historical sales data alone can provide (Big News Network, 2024).
The models continuously learn and adapt. If a storm alert is issued in Florida, the AI anticipates spikes in water, batteries, and emergency supplies, triggering replenishment orders before the storm hits. During Hurricane Ian in fall 2022, when a Walmart distribution center stayed offline for seven days, AI allowed Walmart to reroute shipments and meet elevated post-storm demand without customers noticing disruptions (CIO Dive, December 13, 2022).
For holidays, Walmart's AI/ML engines build on a foundation of historical data (past sales, online searches, page views) and incorporate "future data" such as macroweather patterns, macroeconomic trends, and local demographics to anticipate demand and potential fulfillment disruptions. The system can "forget" anomalies like a once-in-a-lifetime snowstorm in Florida, ensuring one-time deviations don't carry over into future inventory management practices. This capability was first deployed for the 2023 holiday season (Walmart Global Tech, October 25, 2023).
Walmart uses AI to simulate Black Friday—the busiest shopping day of the year—to anticipate hiccups and customer demand throughout the year, not just during peak periods (CIO Dive, December 13, 2022). The company also uses AI and machine learning to predict when people are likely to shop, determine what products to use as substitutes when items are out of stock, and decide whether customers will opt for pickup or delivery (Supply Chain Dive, September 17, 2020).
Replenishment is automated: once forecasts are in place, AI automatically generates replenishment requests, saving manual ordering time for store managers, ensuring faster delivery of high-demand products, and reducing excess orders of slow-moving items to free up shelf space. Rather than distributing the same quantities everywhere, Walmart's AI adjusts stock at the store level, with higher allocations for locations with stronger historical sales and proportionate quantities for low-performing stores to reduce waste (Big News Network, 2024).
With greater accuracy in geographic distribution zones, Walmart can understand customer demands down to differentiations by zip codes. For example, pool toys are always available in sunny states, while warmer sweaters are stocked in colder states. If a toy is not selling well on the East Coast but is hot in the Midwest, the system can reposition inventory or divert demand (Walmart Global Tech, October 25, 2023).
Pactum AI: Automated Supplier Negotiations
In 2021, Walmart piloted an unusual application of machine learning: automated contract negotiations with suppliers. The company licensed technology from Pactum AI, a Mountain View, California-based startup, to deploy a chatbot that negotiates commercial terms with human suppliers on Walmart's behalf (Harvard Business Review, November 2022; Bloomberg, April 26, 2023).
The three-month pilot included 89 suppliers, five buyers, and representatives from Walmart Canada's finance, treasury, and legal departments, along with Pactum. Walmart focused initially on "tail-end suppliers" providing goods not for resale (fleet services, shopping carts, and other retail equipment)—contracts that typically receive cookie-cutter terms without negotiation (Talking Logistics, May 1, 2023).
Walmart set a target of 20% success rate to yield positive ROI. By the end of the pilot, the chatbot had reached agreements with 64% of the 100 tail-end suppliers invited to participate, with an average negotiation turnaround of just 11 days—well above the 20% target (Talking Logistics, May 1, 2023; Medium/QMIND Technology Review, February 5, 2023).
By 2023, when Walmart expanded the program beyond Canada to the United States, Chile, and South Africa, the success rate had climbed to 68% of suppliers approached, with each side gaining something it values (Harvard Business Review summary cited across multiple sources). The chatbot was saving Walmart an average of 3% on contracts, and 83% of suppliers found the chatbot easy to use. Perhaps most surprising: nearly three out of four suppliers (approximately 75%) preferred negotiating with the AI over a human (Bloomberg, April 26, 2023; PYMNTS, April 26, 2023).
Walmart Canada calculated a 4x return on investment from the automated negotiation platform, with 20% of confirmed suppliers reaching deals (Sourcing Journal, April 27, 2021). Pactum AI reports that on average across clients, its technology delivers a 4.2% increase in profitability, and in one single departmental use by a Fortune 500 client, Pactum unlocked working capital at a rate of $1.5 million per month (Sourcing Journal, April 27, 2021).
Walmart has since expanded the solution to other categories, including route rate negotiations for transportation and some goods for resale, with mid-tier suppliers now using the system. The chatbot is multilingual and can negotiate with 2,000 suppliers simultaneously (PYMNTS, January 11, 2023 press release from Pactum).
Agentic AI Framework
By 2025, Walmart unveiled a comprehensive agentic AI framework that shifts retail to a system-centric model. The company deployed purpose-built agents to automate workflows, enhance customer experiences, and optimize supply chains. Key agents include Sparky (for customer solutions) and Marty (for supplier negotiations), leveraging multi-agent orchestration (AInvest, August 30, 2025).
The framework drives measurable ROI: 26.18% year-over-year earnings per share growth, 30% logistics cost savings, and 68% higher contract success rates, positioning Walmart as a leader in the $1.7 trillion agentic commerce market projected by industry analysts (AInvest, August 30, 2025). Walmart's $500 million investment in AI and automation has yielded operational efficiency gains, including an 18-week reduction in fashion production timelines and a 30% cut in emergency maintenance costs (AInvest, citing ainvest.com sources, August 30, 2025).
The global AI retail market is forecasted to grow from $9.85 billion in 2024 to $40.49 billion by 2029, driven by agentic AI adoption. Walmart's early commitment to a unified AI framework gives it a first-mover advantage over competitors who focus on narrow AI tools (AInvest, August 30, 2025).
Route Optimization and Logistics
Walmart's ML-powered route optimization has saved 30 million unnecessary driving miles, a massive improvement in logistics efficiency crucial to competing with e-commerce giants like Amazon. As of March 2024, Walmart began offering this route optimization technology as a Software-as-a-Service (SaaS) solution to all businesses, monetizing its internal innovation (Virtasant, 2024).
The company's AI-driven supply chain optimization tools enhance customer satisfaction while reducing costs. Parvez Musani, SVP of End-to-End Fulfillment at Walmart U.S. Omni Platforms and Tech, told PYMNTS in July 2024 that "the integration of AI, ML, and vast computing power, coupled with an abundance of data, has transformed our approach to demand forecasting, inventory flow, and cost optimization" (PYMNTS, July 3, 2024).
Walmart's Emergency Operations Control, which includes representatives from every part of the organization, plays a vital role in disseminating information and coordinating responses. By sharing live information and implementing actions based on AI-driven simulations, Walmart can effectively manage supply chain risks and ensure customers can count on the retailer during challenging times (PYMNTS, July 3, 2024).
Quantifiable Results from Walmart
The following results are documented across multiple credible sources from 2022 to 2025:
Financial Performance:
26.18% year-over-year EPS growth tied to agentic AI framework (AInvest, August 30, 2025)
4.8% revenue uplift from generative-AI-driven merchandising (Mordor Intelligence citing internal Walmart data, July 3, 2025)
3% average savings on supplier contracts negotiated by Pactum AI (Bloomberg, April 26, 2023)
4x ROI from automated supplier negotiations in pilot program (Sourcing Journal, April 27, 2021)
Operational Efficiency:
30% logistics cost savings (AInvest, August 30, 2025)
30 million unnecessary driving miles avoided through route optimization (Virtasant, 2024)
68% success rate in supplier negotiations via AI chatbot, up from 64% in pilot (PYMNTS, April 26, 2023; Harvard Business Review, November 2022)
18-week reduction in fashion production timelines (AInvest, August 30, 2025)
30% cut in emergency maintenance costs (AInvest, August 30, 2025)
Customer and Supply Chain:
25% increase in customer satisfaction scores from AI chatbot implementation (CDO Times, June 7, 2024, citing Forbes)
Significant stockout reduction (multiple sources; specific percentage not disclosed publicly)
Higher inventory turnover rate post-AI implementation (CDO Times, June 7, 2024, citing Harvard Business Review)
Supplier Experience:
83% of suppliers found the Pactum chatbot easy to use (PYMNTS, January 11, 2023)
75% of suppliers preferred negotiating with AI over humans (Bloomberg, April 26, 2023)
Technology Deployment:
Plans to power 65% of stores with automation technologies by 2026 (AIX Expert Network, April 21, 2024)
These results demonstrate that Walmart's machine learning investments deliver measurable returns across financial performance, operational efficiency, customer satisfaction, and supplier relations.
Case Study 2: Target's AI-Driven Transformation
Target Corporation operates nearly 2,000 stores across the United States and generated forecasted revenue of $85.98 billion in 2025 (AIM Media House, June 23, 2024). The company has designated AI as a strategic priority, creating an acceleration office led by executive vice president and COO Michael Fiddelke to advance AI tools with key objectives in mind (Digital Commerce 360, August 14, 2025).
Store Companion: Empowering Frontline Workers
On June 20, 2024, Target announced plans to roll out Store Companion, a generative AI-powered chatbot, to team members at all of its nearly 2,000 stores by August—making it the first major retailer to deploy GenAI technology to store team members at scale across the U.S. (Target Corporate, June 20, 2024).
Store Companion is available as an app on store team members' specially equipped handheld devices. The tool provides immediate answers to questions about processes and procedures. For example, team members can input prompts like "How do I sign a guest up for a Target Circle Card?" or "How do I restart the cash register in the event of a power outage?" and receive instructions and resources in seconds (Target Corporate, June 20, 2024).
The tool also serves as a store process expert and coach, helping new and seasonal team members learn on the job. "Streamlining day-to-day tasks goes a long way with our team members and adds up to more time spent with guests and a better guest experience across the store," said Jake Seaquist, a store director at one of the Target stores that participated in the chatbot pilot (Digital Commerce 360, August 14, 2025).
Target's in-house technology team developed Store Companion in about six months using frequently asked questions and process-related documents from stores across the U.S. (CIO Dive, June 21, 2024). The tool began piloting in approximately 400 locations before the chainwide rollout, which was completed by August 2024 (Retail Technology Innovation Hub, August 27, 2024; WWD, June 20, 2024).
"Generative AI is game-changing technology and Store Companion will make daily tasks easier and enable our team to respond to guests' requests with confidence and efficiency," said Mark Schindele, executive vice president and chief stores officer at Target. "The tool frees up time and attention for our team to serve guests with care and to create a shopping destination that invites discovery, ease and moments of everyday joy" (Target Corporate, June 20, 2024).
Target's CIO Brett Craig emphasized that "the transformative nature of GenAI is helping us accelerate the rate of innovation across our operations, and we're excited about the role these new tools and applications will play in driving growth" (CIO Dive, June 21, 2024).
Inventory Optimization with Predictive Analytics
Target faced considerable challenges managing inventory, particularly with overstocking and understocking issues that resulted in lost sales and escalated operational costs. Handling a diverse and extensive product range across its widespread store network posed significant complexities (DigitalDefynd, March 17, 2025).
To address these challenges, Target implemented advanced AI technologies to enhance inventory management systems. The retail giant utilized machine learning algorithms to analyze extensive data from multiple sources, including past sales patterns, seasonal trends, and real-time sales data. This AI-driven methodology enhanced Target's ability to predict product demand accurately (DigitalDefynd, March 17, 2025).
The AI system was seamlessly integrated into Target's supply chain management infrastructure, enhancing efficiency and effectiveness. It utilized predictive analytics to forecast demand at both macro and micro levels, adjusting inventory distribution accordingly. The system was engineered to be self-learning, continuously refining its predictions with each new data input to ensure optimal inventory levels across all stores (DigitalDefynd, March 17, 2025).
The AI system continuously analyzes data from store transactions, online sales, and external market indicators. Machine learning refines predictions over time, learning from past inaccuracies to improve future forecasts. AI also enables real-time adjustments based on sudden changes in consumer behavior or external conditions, such as weather events or economic downturns (DigitalDefynd, March 17, 2025).
Personalization and Customer Experience
Target leveraged AI technology to develop a sophisticated personalization engine that utilizes data analytics and machine learning. This system gathers data from multiple touchpoints, including in-store purchases, online shopping behaviors, and mobile app interactions. By analyzing this data, the AI models can predict customer preferences and recommend products more likely to appeal to individual shoppers (DigitalDefynd, March 17, 2025).
The AI personalization engine was integrated across Target's digital platforms and mobile applications. It provides customized product recommendations and promotions based on previous shopping patterns and predictive analytics. For example, if a customer regularly buys baby products, the system recommends diapers or provides discounts on baby food. This technology was also employed to personalize email marketing campaigns, ensuring customers receive messages relevant to their interests and purchase history (DigitalDefynd, March 17, 2025).
In addition to personalization, Target is using generative AI to enhance hundreds of thousands of product display pages on Target.com with new review summaries and more relevant product titles and descriptions. These improvements help consumers find items they are looking for more efficiently while boosting confidence in purchase decisions (Bain & Company case study, undated).
Target's generative AI-powered Bullseye Gift Finder provides personalized product recommendations for kids based on criteria including age, hobbies, and favorite brands. The tool, available on web, mobile, and app, features thousands of gift ideas across categories like toys, tech, and accessories, allowing guests to find the perfect gift quickly (Bain & Company case study, undated).
Automated Checkout Systems
To address long checkout lines that can deter customers and diminish the shopping experience, Target implemented AI-powered automated checkout systems in several of its stores across the United States. These systems employ machine learning and computer vision technologies to automatically recognize items being purchased, calculate the total cost, and process transactions without cashier intervention (DigitalDefynd, March 17, 2025).
The automated checkout systems integrate with Target's IT infrastructure, ensuring a smooth transition and immediate functionality. These systems include advanced scanners and cameras that accurately recognize products and their prices as customers place them on the conveyor belt. The AI learns from every transaction, improving accuracy and speed over time, and adapts to new products introduced into the store (DigitalDefynd, March 17, 2025).
Quantifiable Results from Target
The following results are documented from credible sources dated 2024 to 2025:
Inventory Management:
Significant reduction in instances of overstock and understock across stores (DigitalDefynd, March 17, 2025)
Noticeable improvement in inventory turnover ratios (DigitalDefynd, March 17, 2025)
Reduction in clearance sales, indicating more efficient inventory management (DigitalDefynd, March 17, 2025)
Enhanced operational efficiency and better financial outcomes by reducing holding costs and improving sales through better product availability (DigitalDefynd, March 17, 2025)
Customer Experience:
Noticeable increase in customer loyalty, measured by repeat purchases and lengthening of average customer lifecycle (DigitalDefynd, March 17, 2025)
Higher conversion rates from targeted marketing campaigns, indicating customers are more responsive to personalized advertisements and recommendations (DigitalDefynd, March 17, 2025)
Greatly improved customer engagement and satisfaction levels through personalized shopping journeys (DigitalDefynd, March 17, 2025)
Technology Deployment:
Store Companion deployed to hundreds of thousands of team members across nearly 2,000 stores by August 2024 (Target Corporate, June 20, 2024; Retail Technology Innovation Hub, August 27, 2024)
Development time for Store Companion: approximately six months (CIO Dive, June 21, 2024)
Generative AI used to enhance product pages on Target.com for hundreds of thousands of products in 2024 (Digital Commerce 360, August 14, 2025)
Strategic Direction:
Creation of a dedicated acceleration office to advance AI tools within the organization, led by EVP and COO Michael Fiddelke (Digital Commerce 360, August 14, 2025)
While Target has not disclosed as many specific percentage metrics publicly as Walmart, the documented improvements in inventory turnover, customer loyalty, and operational efficiency indicate substantial ROI from AI investments.
How ML Works in Retail: Core Technologies
Machine learning in retail relies on several interconnected technologies:
1. Supervised Learning
Supervised learning trains models on labeled historical data to make predictions. In retail, this powers demand forecasting (predicting future sales based on past sales, seasonality, promotions), price optimization (determining optimal prices based on competitor pricing, demand elasticity, inventory levels), and customer churn prediction (identifying customers likely to stop shopping with the brand).
2. Unsupervised Learning
Unsupervised learning finds patterns in unlabeled data. Retailers use this for customer segmentation (grouping customers by behavior, preferences, demographics without predefined categories) and market basket analysis (discovering which products are frequently purchased together to optimize store layouts and promotions).
3. Natural Language Processing (NLP)
NLP enables computers to understand and process human language. Applications include chatbots and virtual assistants (Target's Store Companion, Walmart's customer service bots), sentiment analysis (analyzing customer reviews and social media to gauge brand perception), and voice commerce (enabling shopping through voice commands via Alexa or Google Assistant).
4. Computer Vision
Computer vision analyzes visual information from cameras. Retailers deploy this for automated checkout (systems that identify products without scanning), in-store analytics (tracking customer movement patterns, dwell times, and heat maps), and inventory monitoring (robots equipped with cameras that scan shelves to verify pricing and stock levels).
5. Deep Learning and Neural Networks
Deep learning uses neural networks with multiple layers to process complex data. This powers image recognition (for visual search and automated product identification), recommendation engines (analyzing complex patterns in customer behavior to suggest products), and predictive maintenance (analyzing sensor data from equipment to predict failures before they occur).
6. Ensemble Methods
Ensemble methods combine multiple ML models to improve accuracy. Walmart specifically uses ensemble approaches because "not every problem has the same solution, and AI and ML models should be able to adapt to different problems" (CIO Dive, December 13, 2022). This is particularly important in demand forecasting, where different models might excel at predicting different product categories or seasonal patterns.
Step-by-Step: How Retailers Deploy ML Systems
Here is a simplified framework for how major retailers like Walmart and Target deploy machine learning systems, synthesized from their documented approaches:
Step 1: Define Business Objectives and Use Cases
Start with a clear business problem that ML can solve. Walmart focused initially on demand forecasting to reduce stockouts and excess inventory. Target prioritized inventory optimization to reduce overstock and understock issues. Both retailers identified specific, measurable goals tied to financial outcomes.
Step 2: Establish Data Infrastructure
Build or upgrade systems to collect, store, and manage data from all sources: point-of-sale systems, online transactions, mobile apps, supply chain sensors, weather services, and external market data. Walmart invested in advanced data management systems, data lakes, and cloud-based platforms to support AI initiatives (CDO Times, June 7, 2024). Target integrated data from in-store purchases, online shopping behaviors, and mobile app interactions.
Step 3: Assemble Cross-Functional Teams
Create teams that include data scientists, ML engineers, domain experts (merchandisers, supply chain managers), IT professionals, and business stakeholders. Walmart's AI pilot for supplier negotiations included buyers, finance, treasury, and legal representatives alongside Pactum's technical team (Talking Logistics, May 1, 2023). Target's Store Companion was developed by in-house technology teams in collaboration with store directors and operations managers.
Step 4: Start with a Pilot Program
Test ML systems on a small scale before full deployment. Walmart piloted Pactum AI with 89 suppliers over three months (Talking Logistics, May 1, 2023). Target piloted Store Companion in approximately 400 stores before rolling it out chainwide (CIO Dive, June 21, 2024). Set clear success metrics—Walmart aimed for a 20% agreement rate with suppliers to achieve positive ROI.
Step 5: Collect and Prepare Training Data
Machine learning models require large volumes of high-quality, labeled data. Walmart used historical sales data, online searches, page views, weather patterns, macroeconomic trends, and local demographics for holiday demand forecasting (Walmart Global Tech, October 25, 2023). Target analyzed past sales patterns, seasonal trends, and real-time sales data for inventory optimization (DigitalDefynd, March 17, 2025). Data cleaning, normalization, and feature engineering are critical at this stage.
Step 6: Train and Validate Models
Train ML models using historical data, then validate their accuracy against a separate test dataset. Walmart's AI engines fine-tune models during training and incorporate mechanisms to "forget" anomalies like once-in-a-lifetime weather events that should not influence future predictions (Walmart Global Tech, October 25, 2023). Models should continuously learn and improve as new data arrives.
Step 7: Integrate with Existing Systems
ML models must connect seamlessly to enterprise resource planning (ERP) systems, point-of-sale systems, inventory management platforms, and supplier portals. Pactum AI integrates with Walmart's procurement systems to automatically apply negotiated terms (PYMNTS, April 26, 2023 interview with Pactum CEO). Target's personalization engine integrates across digital platforms, mobile apps, and email marketing systems (DigitalDefynd, March 17, 2025).
Step 8: Deploy with Human Oversight
Even highly automated systems require human oversight. Walmart emphasizes that "associates' feedback is a critical part of the tuning and training process" for inventory systems, and "nobody, and no robot, can replicate the intuition of our associates gained over their careers" (Walmart Global Tech, October 25, 2023). Target's Store Companion empowers team members to make final decisions based on AI recommendations.
Step 9: Monitor Performance and Iterate
Track key performance indicators continuously: forecast accuracy, stockout rates, customer satisfaction scores, negotiation success rates, conversion rates. Use dashboards to review results in real time. Walmart uses live dashboards to monitor Pactum AI negotiations and can import new KPIs and metrics to assess subsequent negotiations (Sourcing Journal, April 27, 2021). Models should be retrained regularly as market conditions change.
Step 10: Scale Gradually and Expand Use Cases
After proving ROI in one area, expand to adjacent use cases. Walmart started with tail-end supplier negotiations and expanded to mid-tier suppliers, transportation route rates, and some goods for resale (Talking Logistics, May 1, 2023). Target plans to expand AI capabilities to other areas of the supply chain, including logistics and distribution center management (DigitalDefynd, March 17, 2025).
Comparison Table: Walmart vs. Target ML Strategies
Regional and Industry Variations
Machine learning adoption in retail varies significantly by geography and market segment.
North America: Dominated by large-format retailers like Walmart, Target, and Amazon, this region leads in ML investment and deployment. North America held 33.4%–37.4% of the global AI in retail market in 2024 (Grand View Research, 2024; Mordor Intelligence, July 3, 2025). The presence of major AI vendors (Google, IBM, Microsoft), robust cloud infrastructure, and venture capital support drive innovation. Regulatory scrutiny around bias and pricing discrimination is intensifying, but transparent model governance practices help companies maintain deployment momentum.
Asia-Pacific: This is the fastest-growing region, advancing at 18.9% CAGR through 2030, supported by mobile-first consumers, government AI programs, and aggressive investment from both local and global retailers (Mordor Intelligence, July 3, 2025). In India, 80% of retailers intend to scale AI in 2025, with expectations that generative models will raise frontline productivity by as much as 37% (Mordor Intelligence, July 3, 2025). China's social-commerce titans combine live video, conversational AI, and integrated payments, creating unique ML applications. Companies like Alibaba have integrated AI for logistics, personalized customer experiences, and real-time pricing, positioning China as a leader in AI innovation (Straits Research, 2025).
Europe: The UK and Germany lead European adoption. In 2024, Tesco introduced an AI-powered loyalty program offering personalized discounts based on shopping habits, resulting in boosted engagement and sales (Straits Research, 2025). However, 69% of UK retailers planning AI/ML implementation face barriers including data preparation challenges (cited by 52% of respondents), lack of in-house expertise (41%), and lack of executive backing (35%) (Ecommerce News UK, March 13, 2024). Europe's strict data privacy regulations (GDPR) and the AI Act passed in 2024 create compliance complexities but also promote responsible AI deployment.
By Retail Format:
Grocery and General Merchandise: Walmart's success demonstrates ML's value in high-volume, low-margin environments where small efficiency gains translate to massive savings. Pactum AI notes that "highly commoditized and fragmented sectors such as grocery, general merchandise, pharmacy and home goods" are best suited for automated negotiation technology (Sourcing Journal, April 27, 2021).
Apparel: Less fragmented with more subjectivity in buying processes, making some ML applications (like automated negotiations) less suitable. However, ML excels at trend prediction and visual search. Walmart's AI-driven fashion production timeline reductions (18 weeks) show promise (AInvest, August 30, 2025).
E-commerce: Online-native retailers like Amazon pioneered ML for personalized recommendations, dynamic pricing, and predictive inventory placement. Traditional retailers are rapidly catching up—Target's digital business growth, particularly through same-day fulfillment services, drove Q1 2024 revenue performance improvements (AIM Media House, June 23, 2024).
Omnichannel: Retailers blending physical and digital channels see the greatest ML benefits. Omnichannel strategies commanded 45.7% of the AI in retail market in 2024, while pure-play online retailers are projected to expand at 19.8% CAGR to 2030 (Mordor Intelligence, July 3, 2025). ML unifies inventory visibility, personalizes experiences across touchpoints, and optimizes fulfillment routes.
Pros and Cons of Machine Learning in Retail
Pros
1. Improved Forecast Accuracy
ML models consistently outperform traditional statistical methods when exogenous factors (weather, events, prices) are present, especially when feature engineering addresses nonlinearity (ResearchGate, December 25, 2023, citing Makridakis et al., 2018). This translates to fewer stockouts and less excess inventory.
2. Cost Savings at Scale
Walmart's 30% logistics cost savings and 3% average savings on supplier contracts demonstrate substantial cost reductions (AInvest, August 30, 2025; Bloomberg, April 26, 2023). Retailers using AI and ML saw 8% annual profit growth, outpacing non-adopters (IHL Group, December 14, 2023).
3. Enhanced Customer Experience
Personalization engines increase customer loyalty, conversion rates, and average order values. Target's AI-driven personalization led to noticeable increases in repeat purchases and lengthening of customer lifecycles (DigitalDefynd, March 17, 2025). Walmart's AI recommendations helped predict demand for items like pumpkin pies during holiday seasons (Walmart Global Tech, July 11, 2023).
4. Real-Time Adaptability
Unlike static business rules, ML systems adapt to changing conditions in real time. Walmart's AI rerouted shipments during Hurricane Ian, and Target's systems adjust forecasts based on sudden shifts in consumer behavior or weather events (CIO Dive, December 13, 2022; DigitalDefynd, March 17, 2025).
5. Workforce Empowerment
Automation eliminates mundane tasks, allowing employees to focus on higher-value work. Target's Store Companion frees up team members to spend more time with guests (Target Corporate, June 20, 2024). Walmart's Parvez Musani noted that "jobs are becoming more fulfilling and high-skilled" as automation takes over repetitive tasks (PYMNTS, July 3, 2024).
6. Competitive Advantage
Early adopters gain market share. Walmart's revenue uplift from GenAI-driven merchandising and Target's improved operational efficiency position them ahead of competitors slower to adopt AI (Mordor Intelligence, July 3, 2025; DigitalDefynd, March 17, 2025).
Cons
1. Data Quality Requirements
ML models are only as good as the data they train on. Poor data quality—inconsistent, incomplete, or biased data—leads to unreliable predictions (Devfi, February 18, 2025). Only 40% of retailers have records of stock quantities at each location during order placement, and just 36% monitor stock allocations across sales channels, presenting formidable challenges to AI implementation (Ecommerce News UK, March 13, 2024).
2. Talent Shortage
41% of retailers report lack of in-house AI/ML expertise as a significant barrier (Ecommerce News UK, March 13, 2024). The rapid pace of AI advancement demands consistent learning and adaptation. 72% of IT leaders mention AI skills as one of the crucial gaps that need to be addressed urgently (Itransition, 2025).
3. Integration Complexity
Integrating ML models with legacy systems presents major challenges. Many retailers operate on outdated infrastructures that do not seamlessly support ML integration, leading to downtime and workflow disruptions (Devfi, February 18, 2025). Data must flow seamlessly across mobile apps, websites, and point-of-sale systems—a task complicated by incompatible systems and incomplete data synchronization (Medium/AppVin Technologies, November 14, 2024).
4. High Upfront Costs
Building ML infrastructure requires significant investment in cloud computing, specialized hardware (GPUs), software licenses, and skilled personnel. Smaller retailers or those lacking technical resources face disadvantages (Medium/AppVin Technologies, November 14, 2024).
5. Privacy and Security Risks
Retailers work with large amounts of sensitive customer and transaction data. Privacy and data governance risks like data leaks are the leading AI concerns across the globe, selected by 42% of North American organizations and 56% of European ones (Itransition, 2025). Cybersecurity and data privacy are currently US executives' top concerns when implementing generative AI, at 81% and 78% respectively (Itransition, 2025).
6. Lack of Transparency ("Black Box" Problem)
Deep learning models can be difficult to interpret, making it hard to understand why a model made a specific prediction. This lack of explainability raises concerns, particularly in high-stakes decisions like pricing or credit decisions. Explainable AI (XAI) techniques are emerging to address this, but implementation adds complexity (IABAC, June 6, 2025).
7. Bias and Fairness Issues
ML models can perpetuate or amplify biases present in training data, leading to unfair outcomes for certain customer segments or demographics. Retailers must implement bias testing and monitoring frameworks, debiasing techniques, and fairness metrics (Upcore Technologies, April 12, 2024).
8. Dependence on Technology
Over-reliance on ML systems can erode human judgment and institutional knowledge. Walmart emphasizes that "nobody, and no robot, can replicate the intuition of our associates gained over their careers," maintaining human oversight even with highly automated systems (Walmart Global Tech, October 25, 2023).
Myths vs. Facts
Myth 1: Machine learning will replace retail workers.
Fact: ML automates repetitive tasks, freeing employees for higher-value work. Target's Store Companion empowers team members to serve guests with more care and confidence (Target Corporate, June 20, 2024). Walmart's Musani said "jobs are becoming more fulfilling and high-skilled" as automation eliminates mundane tasks (PYMNTS, July 3, 2024). Both retailers are hiring AI talent, not reducing overall headcount.
Myth 2: Only large retailers with massive budgets can afford ML.
Fact: Cloud-based ML platforms (AWS, Azure, Google Cloud) provide scalable, pay-as-you-go access to AI tools, reducing upfront infrastructure costs (IABAC, June 6, 2025). Mid-sized retailers can leverage solutions like Blue Yonder's Luminate Platform, Manhattan Associates' SCALE, and Relex Solutions for demand forecasting and inventory optimization without Walmart-level resources (Virtasant, 2024). The key is starting with a pilot project focused on a specific problem.
Myth 3: ML models are always accurate and unbiased.
Fact: ML models reflect biases in training data and can make errors. Retailers must implement bias testing, fairness metrics, and continuous monitoring (Upcore Technologies, April 12, 2024). Walmart's AI engines include mechanisms to "forget" anomalies that should not influence future predictions (Walmart Global Tech, October 25, 2023). Human oversight remains essential.
Myth 4: Implementing ML delivers instant results.
Fact: ML deployment is a multi-year journey. Walmart spent years collecting and curating data before achieving significant accuracy (CIO Dive, December 13, 2022). Target developed Store Companion over six months (CIO Dive, June 21, 2024). Success requires establishing data infrastructure, training models, integrating systems, and iterating based on performance.
Myth 5: ML systems work perfectly right out of the box.
Fact: Models require continuous training, validation, and tuning. Walmart's AI engines fine-tune during training and continuously learn from new data (Walmart Global Tech, October 25, 2023). Target's systems are "self-learning, continuously refining predictions with each new data input" (DigitalDefynd, March 17, 2025). This ongoing maintenance is critical to sustained performance.
Myth 6: Customer data privacy is impossible with ML.
Fact: Privacy-preserving techniques like federated learning (training models on decentralized data without moving the data) and differential privacy (adding noise to protect individual records) enable ML while protecting customer information (IABAC, June 6, 2025). Retailers must implement robust data governance and comply with regulations like GDPR and CCPA, but ML and privacy can coexist.
Common Pitfalls and How to Avoid Them
Pitfall 1: Starting Without Clear Business Objectives
The Mistake: Implementing ML because it's trendy without defining specific, measurable goals tied to business outcomes.
How to Avoid: Begin with a business problem, not a technology. Walmart focused on reducing stockouts and excess inventory—clear, quantifiable objectives. Target prioritized inventory turnover and customer satisfaction. Define success metrics upfront (e.g., "reduce stockouts by 15%" or "improve forecast accuracy by 10 percentage points").
Pitfall 2: Underestimating Data Quality Requirements
The Mistake: Assuming existing data is "good enough" without investing in data cleaning, normalization, and governance.
How to Avoid: Conduct a data quality audit before building models. Walmart invested in "data cleaning and validation processes" (ResearchGate, December 25, 2023). Establish data quality standards, implement preprocessing techniques, and foster collaboration between domain experts and data scientists (Upcore Technologies, April 12, 2024).
Pitfall 3: Neglecting Change Management
The Mistake: Deploying ML systems without preparing employees, leading to resistance and poor adoption.
How to Avoid: Walmart invested in extensive training and upskilling programs, with AI training participants growing fivefold over three years (CDO Times, June 7, 2024). Target developed Store Companion with input from store directors and tested it in 400 locations before chainwide rollout (CIO Dive, June 21, 2024). Communicate benefits clearly, provide training, and incorporate feedback from frontline workers.
Pitfall 4: Overreliance on a Single Model or Vendor
The Mistake: Betting the business on one algorithm or locking into a single cloud provider with proprietary tools.
How to Avoid: Walmart built Element to avoid vendor lock-in, enabling seamless switching between AWS, Azure, and Google Cloud (Walmart Global Tech, March 14, 2024). Use ensemble approaches—combining multiple models—because "not every problem has the same solution" (CIO Dive, December 13, 2022).
Pitfall 5: Ignoring Integration with Existing Systems
The Mistake: Building ML models that cannot connect to ERP, point-of-sale, or inventory management systems, creating data silos.
How to Avoid: Engage IT teams early in the process to address integration challenges (Devfi, February 18, 2025). Adopt APIs and microservices to enable seamless interaction between ML models and legacy systems (Devfi, February 18, 2025). Plan for integration before model training.
Pitfall 6: Failing to Monitor and Retrain Models
The Mistake: Treating ML deployment as "set it and forget it" without ongoing performance monitoring or retraining as market conditions change.
How to Avoid: Implement continuous monitoring dashboards to track forecast accuracy, stockout rates, and other KPIs. Walmart uses live dashboards for supplier negotiations and can import new metrics to assess performance (Sourcing Journal, April 27, 2021). Establish a schedule for model retraining (e.g., quarterly or when performance degrades below thresholds).
Pitfall 7: Overlooking Ethical and Regulatory Considerations
The Mistake: Deploying models without addressing bias, fairness, and compliance with data privacy regulations.
How to Avoid: Implement ethical AI frameworks and conduct regular audits emphasizing transparency, fairness, and accountability (Devfi, February 18, 2025). Stay updated on regulatory requirements like GDPR, CCPA, and the EU AI Act (Upcore Technologies, April 12, 2024). Test models for bias using fairness metrics before deployment.
Pitfall 8: Scaling Too Quickly Without Proving ROI
The Mistake: Expanding ML across the organization before demonstrating success in a pilot.
How to Avoid: Start small. Walmart piloted Pactum AI with 89 suppliers over three months before expanding to thousands (Talking Logistics, May 1, 2023). Target piloted Store Companion in 400 stores before the chainwide rollout (CIO Dive, June 21, 2024). Prove ROI in one area, then scale gradually to adjacent use cases.
Challenges: The Hard Truths About ML Adoption
Despite the documented successes, machine learning adoption in retail faces persistent challenges.
Challenge 1: Data Availability and Quality
Only 40% of retailers have historical records of stock quantities at each location during order placement, and just 36% monitor stock allocations across sales channels (Ecommerce News UK, March 13, 2024). This deficiency in historical insight into inventory levels and the absence of order processing and delivery data presents formidable challenges to AI implementation. Incomplete, biased, or inaccurate data leads to flawed models and unreliable predictions (Upcore Technologies, April 12, 2024).
Challenge 2: Talent and Expertise Gaps
41% of retailers report lack of in-house AI/ML expertise as a significant barrier (Ecommerce News UK, March 13, 2024). 72% of IT leaders mention AI skills as one of the crucial gaps that need to be addressed urgently, and 60% of public sector IT professionals consider AI skills shortages to be the top challenge to implementing AI (Itransition, 2025). The rapid pace of AI advancement demands consistent learning and adaptation. Recruiting ML talent is difficult: recruiting difficulty varies by role, with specialized positions like AI research scientists particularly hard to fill (Itransition citing McKinsey data, 2025).
Challenge 3: Integration with Legacy Systems
Many retailers operate on outdated infrastructures that do not seamlessly support ML integration, leading to downtime and workflow disruptions (Devfi, February 18, 2025). Integrating AI systems with existing infrastructure poses significant challenges, particularly for large enterprises with complex and diverse data sources. Effective data management strategies, robust integration frameworks, and continuous data quality checks are essential (ResearchGate, December 25, 2023).
Challenge 4: High Implementation Costs
While cloud platforms reduce some costs, building ML infrastructure still requires significant investment. Small to mid-sized retailers or those lacking technical resources face disadvantages (Medium/AppVin Technologies, November 14, 2024). Deep learning models require substantial processing power, increasing hardware and cloud costs (Devfi, February 18, 2025).
Challenge 5: Privacy and Security Concerns
Privacy and data governance risks like data leaks are the leading AI concerns across the globe, selected by 42% of North American organizations and 56% of European ones (Itransition, 2025). Cybersecurity and data privacy are currently US executives' top concerns when implementing generative AI, at 81% and 78% respectively (Itransition, 2025). Retailers work with large amounts of sensitive customer and transaction data that cannot be used directly in testing due to privacy regulations (RTTS, November 5, 2024).
Challenge 6: Lack of Executive Buy-In
35% of retailers cited lack of executive backing as a crucial challenge to AI adoption (Ecommerce News UK, March 13, 2024). However, this factor is expected to evolve as AI success stories proliferate and confidence in AI technologies solidifies. Moving from pilot to production requires sustained executive commitment and resource allocation.
Challenge 7: Model Interpretability ("Black Box" Problem)
Deep learning models involve complex algorithms that are difficult to interpret and explain. This "black box" nature raises concerns about transparency and accountability, particularly in high-stakes decisions (Upcore Technologies, April 12, 2024). While explainable AI (XAI) techniques are emerging, they add complexity and are not yet standard practice across the industry (IABAC, June 6, 2025).
Challenge 8: Scaling and Productionization
15% of machine learning professionals cite ML monitoring and observability as the biggest challenge in productionizing ML models, making it the most common obstacle (Itransition, 2025). Access to relevant training data is the second most common challenge (cited by 13%). Moving from a successful pilot to enterprise-wide deployment at scale requires robust MLOps practices, automated monitoring, and continuous integration/deployment pipelines.
The Future of Machine Learning in Retail (2025–2030)
Several trends will shape the next phase of ML in retail:
1. Agentic AI and Autonomous Systems
The second half of 2024 saw growing interest in agentic AI models capable of independent action. Tools like Salesforce's Agentforce and Walmart's agentic AI framework with agents like Sparky and Marty represent the future (TechTarget, 2024; AInvest, August 30, 2025). By 2025, AI agents will handle routine tasks like scheduling, data analysis, and even some decision-making with minimal human intervention—though oversight remains critical.
Walmart is positioned as a leader in the projected $1.7 trillion agentic commerce market (AInvest, August 30, 2025). Expect more retailers to deploy purpose-built agents for specific workflows (customer service, inventory management, supplier relations, marketing campaigns) over the next five years.
2. Generative AI Expanding Beyond Content
Generative AI use more than doubled from 33% of organizations in 2023 to 71% in 2024 (BayTech Consulting, 2025). In retail, GenAI is projected to grow at a 27.6% CAGR to 2030, the fastest among AI technologies (Mordor Intelligence, July 3, 2025). Applications will expand beyond text and images to include:
Product design and prototyping
Dynamic pricing strategies generated on the fly
Personalized shopping experiences at individual customer level
Synthetic data generation to overcome training data scarcity
3. Edge AI and Real-Time Processing
Edge AI enables data processing close to its source rather than relying solely on cloud computing. By 2025-2030, this will allow faster processing and real-time decision-making on devices like smartphones, IoT sensors, and in-store cameras (HD Web Soft, November 8, 2024). Retailers will deploy computer vision at the edge for automated checkout, shelf monitoring, and customer analytics with sub-second latency while staying compliant with data privacy laws. Edge-hybrid architectures are advancing at a 24.7% CAGR through 2030 (Mordor Intelligence, July 3, 2025).
4. Multimodal AI
Multimodal AI integrates and processes information from multiple data sources—text, images, audio, video—simultaneously. By 2025-2030, customer support chatbots will handle both text and images (a customer uploads a picture of a damaged product, and the chatbot responds immediately). Visual search will become more sophisticated, and AI will analyze in-store video, voice commands, and purchase history together to provide hyper-personalized recommendations (HD Web Soft, November 8, 2024).
5. Small Language Models (SLMs)
A striking trend is the rise of Small Language Models that pack impressive performance into much smaller parameter counts. In 2022, achieving a 60% score on the MMLU benchmark required Google's PaLM with 540 billion parameters. By 2024, Microsoft's Phi-3-mini achieved the same threshold with just 3.8 billion parameters—a 142-fold reduction in model size in just over two years (BayTech Consulting, 2025). This enables advanced AI capabilities to run on edge devices like smartphones and tablets, significantly reducing costs and improving privacy (HD Web Soft, November 8, 2024). Expect retailers to deploy SLMs for in-store applications, reducing reliance on cloud connectivity.
6. Quantum Computing (Long-Term)
While still in early stages, quantum computing could allow machines to process data much faster, making machine learning models more powerful and efficient. This remains a horizon technology unlikely to impact mainstream retail before 2030, but research investments continue (IABAC, June 6, 2025).
7. Federated Learning
Federated learning allows ML models to learn from decentralized data sources without actually moving the data, improving privacy while still enabling learning from valuable data (IABAC, June 6, 2025). This addresses privacy concerns and could enable collaborative learning across retail chains without sharing proprietary data.
8. Increased Regulatory Scrutiny
The EU's AI Act passed in 2024 sets new compliance standards. The U.S. is likely to follow with sector-specific regulations for high-stakes industries (TechTarget, 2024). Regulators may endorse or require sector-specific large language models for retail, setting new standards for AI compliance and data security. Retailers will need robust AI governance frameworks emphasizing transparency, fairness, and accountability (Walmart Corporate, November 20, 2024).
9. Sustainability and Green AI
As environmental concerns grow, retailers will focus on reducing the carbon footprint of AI systems. Training large ML models consumes significant energy. Expect emphasis on more efficient models, renewable energy for data centers, and optimization techniques that reduce computational costs without sacrificing performance.
10. Omnichannel Integration Deepens
AI will increasingly map full customer journeys and adjust in real time across mobile, web, and physical store touchpoints. Target's Store Companion and FairPrice Group's Store of Tomorrow (built on Google Cloud, unifying cart data, in-store sensors, and e-commerce profiles) illustrate how generative models serve both staff and customers while binding physical and digital channels (Mordor Intelligence, July 3, 2025).
FAQ
Q1: What is machine learning in retail, and how does it differ from traditional analytics?
Machine learning uses algorithms that improve performance through experience without being explicitly programmed for every scenario. Traditional analytics follows fixed rules and reports on historical data. ML adapts in real time: when Walmart's system detected Hurricane Ian, it automatically rerouted shipments and adjusted inventory based on patterns learned from past disruptions, not because engineers programmed hurricane responses (CIO Dive, December 13, 2022).
Q2: How much does it cost to implement machine learning in a retail business?
Costs vary widely. Large retailers like Walmart invested $500 million in AI and automation (AInvest, August 30, 2025). However, smaller retailers can start with cloud-based platforms like AWS, Azure, or Google Cloud on a pay-as-you-go basis, reducing upfront infrastructure costs (IABAC, June 6, 2025). A pilot project focused on one use case (e.g., demand forecasting for a product category) might cost $50,000-$200,000, depending on data readiness and external consulting needs. ROI determines whether to scale.
Q3: Which retailers are successfully using machine learning today?
Walmart and Target lead adoption, with documented results including 8% profit growth for AI users, 68% success rates in automated negotiations, and improved inventory turnover (IHL Group, December 2023; Bloomberg, April 26, 2023; DigitalDefynd, March 17, 2025). Amazon, Alibaba, Tesco, and FairPrice Group also deploy ML at scale (Straits Research, 2025; Mordor Intelligence, July 3, 2025).
Q4: How long does it take to see ROI from machine learning investments?
Walmart's Pactum AI pilot showed positive ROI within three months (Talking Logistics, May 1, 2023). Target developed Store Companion in six months and rolled it out chainwide by August 2024 (CIO Dive, June 21, 2024). However, building foundational data infrastructure can take 1-2 years. Quick wins (e.g., personalized email campaigns) can deliver ROI in weeks, while complex supply chain optimization may take 12-24 months.
Q5: Do retailers need a data science team to use machine learning?
Not necessarily. Cloud platforms offer pre-built ML models and AutoML tools that reduce technical barriers (HD Web Soft, November 8, 2024). However, interpreting results, integrating models with business processes, and maintaining systems require expertise. Many mid-sized retailers partner with vendors like Blue Yonder, Manhattan Associates, or Relex Solutions to access AI capabilities without building large in-house teams (Virtasant, 2024). Larger retailers like Walmart and Target invest in internal data science teams for competitive advantage.
Q6: Can machine learning predict customer behavior accurately?
Yes, but with caveats. ML models analyze patterns in historical data to predict future behavior, but accuracy varies. Netflix reports that over 80% of content watched is based on AI-driven recommendations (Mosaikx Marketing, July 7, 2024). Target's personalization engine saw noticeable increases in conversion rates from targeted campaigns (DigitalDefynd, March 17, 2025). However, models require continuous retraining as consumer preferences shift, and unexpected events (pandemics, economic shocks) can disrupt predictions.
Q7: What are the biggest challenges in implementing machine learning?
The top challenges are: (1) Data quality issues—incomplete or inconsistent data leads to poor predictions; (2) Talent shortages—41% of retailers lack AI/ML expertise (Ecommerce News UK, March 13, 2024); (3) Integration complexity with legacy systems; (4) Privacy and security concerns—42%-56% of organizations cite data governance risks (Itransition, 2025); (5) High upfront costs; (6) Lack of executive buy-in (35% cite this as a barrier) (Ecommerce News UK, March 13, 2024).
Q8: How does machine learning improve inventory management?
ML analyzes sales history, weather, local events, social trends, and supply chain data to forecast demand more accurately than traditional methods. Walmart's AI reduced stockouts significantly and increased inventory turnover rates (CDO Times, June 7, 2024; Big News Network, 2024). Target reduced overstock and understock instances, improving inventory turnover ratios and reducing clearance sales (DigitalDefynd, March 17, 2025). Models adjust in real time: if a storm alert is issued, AI anticipates spikes in emergency supplies and triggers replenishment.
Q9: Is machine learning only for large retailers like Walmart and Target?
No. While large retailers have more resources, mid-sized businesses can use cloud-based ML platforms (AWS, Azure, Google Cloud) and specialized retail AI vendors like Blue Yonder, Manhattan Associates, and Relex Solutions without Walmart-level budgets (Virtasant, 2024; IABAC, June 6, 2025). The key is starting with a focused pilot project (e.g., personalizing email campaigns or forecasting demand for top-selling products) to prove ROI before scaling.
Q10: Will machine learning replace retail jobs?
No. ML automates repetitive tasks, freeing employees for higher-value work. Target's Store Companion empowers team members to serve guests with more care (Target Corporate, June 20, 2024). Walmart's Parvez Musani said "jobs are becoming more fulfilling and high-skilled" as automation eliminates mundane tasks (PYMNTS, July 3, 2024). Both retailers emphasize human oversight and continue hiring AI talent. However, job roles will shift—demand will grow for data analysts, ML engineers, and employees skilled in working with AI tools.
Q11: How do retailers ensure machine learning models are fair and unbiased?
Retailers must implement bias testing frameworks, use fairness metrics to measure whether models treat different groups equally, and leverage debiasing techniques during training (Upcore Technologies, April 12, 2024). Walmart's AI engines include mechanisms to "forget" anomalies that should not influence future predictions (Walmart Global Tech, October 25, 2023). Explainable AI (XAI) methods enhance transparency and interpretability (IABAC, June 6, 2025). Continuous monitoring and audits are essential.
Q12: What role does cloud computing play in retail machine learning?
Cloud platforms (AWS, Azure, Google Cloud) provide the computing power and storage needed for ML without requiring retailers to build expensive on-premise infrastructure. Walmart's Element platform operates across multiple clouds to avoid vendor lock-in (Walmart Global Tech, March 14, 2024). Cloud platforms offer pre-built ML tools, auto-scaling, and pay-as-you-go pricing, making AI accessible to retailers of all sizes (IABAC, June 6, 2025). However, some applications (like in-store computer vision) benefit from edge computing to reduce latency and protect privacy.
Q13: Can machine learning help with supplier relationships and negotiations?
Yes. Walmart's Pactum AI chatbot negotiates commercial terms with suppliers, achieving a 68% success rate, 3% average cost savings, and 4x ROI (Bloomberg, April 26, 2023; Sourcing Journal, April 27, 2021). The AI analyzes supplier demands, compares with trends, commodity values, and competitors' costs, then strikes deals within days rather than weeks or months. 75% of suppliers prefer negotiating with the AI over humans, and 83% find it easy to use (Bloomberg, April 26, 2023; PYMNTS, January 11, 2023).
Q14: What is agentic AI, and how does it apply to retail?
Agentic AI refers to AI models capable of independent action and decision-making with minimal human intervention. Walmart's agentic AI framework includes agents like Sparky (customer solutions) and Marty (supplier negotiations) that autonomously handle tasks, manage workflows, and take care of routine actions like scheduling and data analysis (AInvest, August 30, 2025; TechTarget, 2024). These agents are transforming retail operations but still require human oversight and have narrowly defined scopes.
Q15: How do privacy regulations like GDPR affect retail machine learning?
GDPR (Europe), CCPA (California), and the EU AI Act impose strict requirements on how retailers collect, store, and use customer data. Retailers must obtain consent, ensure data security, provide transparency about AI usage, and allow customers to access or delete their data (Upcore Technologies, April 12, 2024). 42% of North American organizations and 56% of European ones cite privacy and data governance risks as leading AI concerns (Itransition, 2025). Techniques like federated learning and differential privacy help retailers use ML while protecting individual privacy (IABAC, June 6, 2025).
Q16: What are the best machine learning use cases for small retailers?
Start with high-impact, low-complexity applications: (1) Personalized email marketing using customer purchase history; (2) Demand forecasting for top-selling products to reduce stockouts; (3) Customer segmentation to target promotions effectively; (4) Chatbots for common customer service questions; (5) Dynamic pricing for clearance items. Cloud platforms offer pre-built tools for these use cases with minimal technical expertise required. Prove ROI in one area before expanding.
Q17: How does machine learning handle seasonal demand variations?
ML models incorporate seasonality as a feature in training data. Walmart's holiday forecasting uses historical sales from past holiday seasons, adjusting for factors like weather patterns, macroeconomic trends, and promotional calendars (Walmart Global Tech, October 25, 2023). Target's AI analyzes seasonal trends along with past sales patterns and real-time data (DigitalDefynd, March 17, 2025). Models continuously learn: if a product consistently spikes in December, the algorithm adjusts future December forecasts accordingly. Ensemble methods combine multiple models that excel at different seasonal patterns.
Q18: What is the difference between machine learning and generative AI?
Machine learning is a broad field where algorithms learn patterns from data to make predictions or decisions. Generative AI is a subset of ML (specifically deep learning) that creates new content—text, images, code—rather than just analyzing existing data. Target's Store Companion chatbot uses generative AI to create natural language responses to employee questions (Target Corporate, June 20, 2024). Walmart uses traditional ML for demand forecasting and generative AI for personalized recommendations and content creation (Walmart Corporate, November 20, 2024). GenAI is the fastest-growing segment in retail AI, projected to grow at 27.6% CAGR to 2030 (Mordor Intelligence, July 3, 2025).
Q19: How do retailers measure the success of machine learning projects?
Key performance indicators vary by use case. For demand forecasting: forecast accuracy (percentage deviation from actual sales), stockout rates (percentage of time products are unavailable), inventory turnover (how quickly inventory sells). For personalization: conversion rates (percentage of visitors who purchase), average order value, customer lifetime value. For supplier negotiations: agreement success rate (Walmart targets 20%, achieved 68%), cost savings percentage (Walmart averaged 3%), negotiation turnaround time (Walmart averaged 11 days). ROI should be measured as (financial gains - implementation costs) / implementation costs.
Q20: What is the future of machine learning in retail beyond 2025?
Expect: (1) Agentic AI systems handling complex workflows autonomously; (2) Multimodal AI integrating text, images, video, and voice for hyper-personalized experiences; (3) Edge AI enabling real-time processing in stores with sub-second latency; (4) Small Language Models running advanced AI on smartphones and edge devices; (5) Federated learning protecting privacy while enabling collaborative insights; (6) Quantum computing (long-term) dramatically accelerating ML capabilities; (7) Stricter regulatory frameworks requiring transparency and fairness; (8) Deeper omnichannel integration mapping full customer journeys across all touchpoints. The global AI in retail market is projected to grow from $11.61-$14.24 billion in 2024 to $40.74-$96.13 billion by 2030 (Grand View Research, 2024; Mordor Intelligence, July 3, 2025).
Key Takeaways
Machine learning delivers measurable ROI in retail: Walmart achieved 26.18% YoY EPS growth and 30% logistics cost savings, while retailers using AI saw 8% annual profit growth in 2023-2024, outpacing non-adopters (AInvest, August 30, 2025; IHL Group, December 14, 2023).
Demand forecasting and inventory optimization are the #1 use case: Accounting for 28.3% of the AI in retail market, these applications reduce stockouts, improve inventory turnover, and free up working capital (Mordor Intelligence, July 3, 2025).
Walmart's Pactum AI automated supplier negotiations achieved 68% success rates, 3% average cost savings, and 4x ROI, with 75% of suppliers preferring to negotiate with AI over humans (Bloomberg, April 26, 2023; Sourcing Journal, April 27, 2021).
Target's Store Companion GenAI chatbot deployed to nearly 2,000 stores in 2024, empowering hundreds of thousands of team members to answer process questions instantly and coach new employees (Target Corporate, June 20, 2024).
The global AI in retail market is projected to grow from $11.61 billion in 2024 to $40.74–$96.13 billion by 2030, with machine learning holding the largest technology share at 35-40% (Grand View Research, 2024; Mordor Intelligence, July 3, 2025).
Top challenges include data quality issues, talent shortages (41% of retailers lack AI/ML expertise), privacy concerns (42%-56% cite data governance risks), and integration complexity with legacy systems (Ecommerce News UK, March 13, 2024; Itransition, 2025).
Machine learning does not replace retail workers—it empowers them: Both Walmart and Target emphasize that AI automates mundane tasks, allowing employees to focus on higher-value work and customer service (Target Corporate, June 20, 2024; PYMNTS, July 3, 2024).
Success requires a multi-year journey: Retailers must establish data infrastructure, start with pilot projects, integrate with existing systems, continuously monitor and retrain models, and scale gradually based on proven ROI.
North America leads adoption (33.4%-37.4% market share in 2024), but Asia-Pacific is the fastest-growing region (18.9% CAGR through 2030), driven by mobile-first consumers and government AI programs (Grand View Research, 2024; Mordor Intelligence, July 3, 2025).
Future trends include agentic AI, generative AI expansion, edge computing, multimodal AI, small language models, federated learning, and stricter regulatory frameworks, with the market projected to continue rapid growth through 2030.
Actionable Next Steps
Assess Current State: Conduct a data quality audit. Inventory what data you collect (sales, customer behavior, inventory levels, weather, supplier lead times) and evaluate completeness, accuracy, and accessibility. Identify gaps that ML systems will require.
Define a High-Impact Use Case: Choose one specific problem where ML can deliver measurable ROI. Options include demand forecasting for top products, personalized email campaigns, or inventory optimization for high-turnover categories. Set clear success metrics (e.g., "reduce stockouts by 15% within six months").
Start with a Pilot Project: Follow Walmart's playbook—pilot with a small scope (e.g., one product category, one supplier segment, or one store cluster) before scaling. Set a target ROI threshold that justifies expansion.
Leverage Cloud Platforms: Explore pre-built ML tools on AWS, Azure, or Google Cloud to reduce upfront costs. Many offer free tiers or trials. Alternatively, evaluate retail-specific vendors like Blue Yonder, Manhattan Associates, or Relex Solutions for turnkey solutions.
Build or Partner for Expertise: If lacking in-house AI talent, consider hiring one or two ML specialists or partnering with consultants who understand retail. Walmart invested in extensive training programs, growing AI training participants fivefold (CDO Times, June 7, 2024). Target developed Store Companion with in-house teams in six months (CIO Dive, June 21, 2024).
Establish Data Governance and Privacy Frameworks: Implement data quality standards, encryption, compliance with GDPR/CCPA, and transparent customer communication about AI usage. Address privacy concerns proactively to avoid regulatory penalties and customer backlash.
Integrate with Existing Systems: Engage IT teams early to plan integration with ERP, point-of-sale, and inventory management platforms. Use APIs and microservices to connect ML models seamlessly (Devfi, February 18, 2025).
Implement Continuous Monitoring: Deploy dashboards to track key metrics in real time (forecast accuracy, stockout rates, conversion rates). Establish a schedule for model retraining (e.g., quarterly) to adapt to changing conditions.
Prioritize Change Management: Communicate benefits to frontline employees, provide training on new tools, and incorporate feedback from users. Walmart emphasizes that "associates' feedback is a critical part of the tuning and training process" (Walmart Global Tech, October 25, 2023).
Scale Gradually Based on Proven ROI: Once a pilot succeeds, expand to adjacent use cases or additional locations. Walmart started with tail-end supplier negotiations and expanded to mid-tier suppliers and transportation rates (Talking Logistics, May 1, 2023). Target piloted Store Companion in 400 stores before chainwide rollout (CIO Dive, June 21, 2024).
Glossary
Agentic AI: AI models capable of independent action and decision-making with minimal human intervention, handling tasks like scheduling, data analysis, and routine workflows (TechTarget, 2024).
Artificial Intelligence (AI): The broad field of computer science focused on creating systems that can perform tasks requiring human intelligence, such as learning, reasoning, and problem-solving.
Automated Checkout: Systems using computer vision and machine learning to identify products without manual scanning, enabling cashier-less transactions.
AutoML (Automated Machine Learning): Tools that automate the process of building and optimizing ML models, reducing technical barriers and development time (Medium, November 25, 2024).
Bias (in ML): Systematic errors in ML models that lead to unfair outcomes for certain groups, often caused by biased training data.
Big Data: Extremely large datasets that traditional data processing software cannot handle, requiring specialized tools and techniques.
Chatbot: A software application using natural language processing to simulate conversation with human users, such as Target's Store Companion or Walmart's Pactum AI for supplier negotiations.
Cloud Computing: Delivery of computing services (servers, storage, databases, networking, software) over the internet, enabling scalable ML deployment without on-premise infrastructure.
Computer Vision: AI technology that enables computers to interpret and understand visual information from images and videos, used in retail for automated checkout and shelf monitoring.
Deep Learning: A subset of machine learning using neural networks with multiple layers to process complex data, particularly effective for image and speech recognition.
Demand Forecasting: Using historical data and external factors to predict future customer demand for products, enabling better inventory planning.
Edge AI: Processing AI models on local devices (smartphones, IoT sensors, in-store cameras) rather than in the cloud, reducing latency and improving privacy.
Ensemble Methods: Combining predictions from multiple ML models to improve accuracy, as Walmart does for demand forecasting (CIO Dive, December 13, 2022).
ERP (Enterprise Resource Planning): Software systems that manage core business processes like inventory, procurement, accounting, and supply chain operations.
Explainable AI (XAI): Techniques that make ML model decisions transparent and interpretable, addressing the "black box" problem.
Feature Engineering: The process of selecting, transforming, and creating relevant variables (features) from raw data to improve ML model performance.
Federated Learning: A method allowing ML models to learn from decentralized data sources without moving the data, enhancing privacy (IABAC, June 6, 2025).
Generative AI: AI systems that create new content (text, images, code) rather than just analyzing existing data, like Target's Store Companion chatbot.
Hyperparameter Tuning: The process of optimizing the settings (hyperparameters) that control how an ML model learns, such as learning rate and model architecture.
Inventory Turnover: A metric measuring how quickly inventory sells, calculated as cost of goods sold divided by average inventory value. Higher turnover indicates more efficient inventory management.
Machine Learning (ML): A subset of AI where algorithms improve performance on tasks through experience, learning patterns from data without being explicitly programmed for every scenario.
MLOps (Machine Learning Operations): Practices and tools for deploying, monitoring, and maintaining ML models in production environments.
Multimodal AI: Systems that integrate and process information from multiple data types (text, images, audio, video) simultaneously (HD Web Soft, November 8, 2024).
Natural Language Processing (NLP): AI technology enabling computers to understand, interpret, and generate human language, used in chatbots and sentiment analysis.
Neural Network: A machine learning model inspired by the human brain, consisting of interconnected nodes (neurons) that process information in layers.
Omnichannel: A retail strategy providing seamless customer experiences across all channels (online, mobile, in-store), unified through integrated data and AI.
Overfitting: When an ML model learns training data too well, including noise and anomalies, leading to poor performance on new data.
Personalization Engine: An ML system that analyzes individual customer data to deliver tailored product recommendations, promotions, and experiences.
Predictive Analytics: Using historical data, statistical algorithms, and ML to forecast future outcomes, such as demand, customer behavior, or equipment failures.
Recommendation Engine: An ML system that suggests products to customers based on their behavior, preferences, and patterns learned from similar users.
Reinforcement Learning: A type of ML where an agent learns by interacting with an environment and receiving rewards or penalties for actions.
Route Optimization: Using ML to determine the most efficient delivery or transportation routes, considering factors like traffic, distance, and delivery windows. Walmart saved 30 million unnecessary driving miles through ML-powered route optimization (Virtasant, 2024).
Small Language Model (SLM): Highly capable AI models with significantly fewer parameters than large language models, enabling deployment on edge devices (BayTech Consulting, 2025).
Stockout: A situation where a product is unavailable for purchase due to zero inventory, resulting in lost sales and customer dissatisfaction.
Supervised Learning: ML where models train on labeled data (input-output pairs) to predict outcomes for new, unseen data.
Supply Chain: The network of organizations, people, activities, and resources involved in moving products from suppliers to customers.
Unsupervised Learning: ML that finds patterns in unlabeled data without predefined categories, used for customer segmentation and market basket analysis.
Sources & References
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