AI in Retail: What Walmart and Amazon Reveal About Scale

A few years ago, most retailers talked about AI carefully. Companies spoke carefully about AI. They tested recommendation engines, ran small warehouse automation pilots, and waited to see if the technology would truly improve their operations. 

That hesitation is disappearing fast. Now, the conversation has changed. AI in retail is now closely linked to inventory planning, fulfillment, pricing, warehouse coordination, logistics forecasting, and customer engagement. Retailers no longer see artificial intelligence as a side project. In many cases, it is becoming a core part of their operations. 

The amount of investment shows this change. Industry forecasts indicate the global AI in retail market could exceed $138 billion by 2035. But the real story is not just about market size. 

It is the pressure driving adoption. Retailers now work in an environment where supply chains are unstable, customer expectations keep rising, and profit margins are tight. Delays spread quickly across global commerce. Inventory mistakes are noticed right away. Customers lose patience fast. 

AI in retail: What is the current scenario?

AI in retail is now used for more than just chatbots and personalization. Retailers are using AI to improve supply chain coordination, warehouse automation, forecasting, customer engagement, and fulfillment speed. Walmart and Amazon show that long-term success depends more on strong infrastructure, integration, and scalable execution than on using isolated AI tools. 

Traditional systems were not designed for that level of operational speed. 

This situation is why companies like Walmart and Amazon are investing heavily in AI infrastructure, automation, forecasting systems, and operational intelligence. They are not just adding new tools. They are changing how large-scale retail operations work. 

KPMG retail leader Mustafa Surka, Partner, Forensic Services, Risk Advisory Consumer Markets & Retail Leader, said in a 2026 interview that “demand forecasting, it’s all thanks to AI,” and added that production planning has moved “from weeks to minutes or even days to minutes,” showing how AI is speeding retail decision-making.

Walmart CTO Vinod Bidarkoppa, Global executive (CIO, CTO, CDO) said, “At this scale, the only way to move faster is to move smarter,” and described “from self-healing inventory to agentic AI, we’re creating systems that turn real-time signals into real-time action, freeing up associates and delivering for customers.”

And that creates an important question for CTOs. 

As AI becomes more deeply integrated into retail infrastructure, what actually separates companies that see measurable gains from those still stuck in endless experimentation? 

AI in retail is becoming an operational priority

Retail companies used to separate AI initiatives from core operations. That distinction no longer feels realistic. 

Today, AI systems help make decisions across ecommerce platforms, warehouse networks, logistics, customer service, and inventory operations simultaneously. The technology is spreading because retailers want to solve problems that older software cannot handle well. 

And there are plenty of those problems. 

Demand now changes faster than before. Shipping disruptions move quickly through supply chains. Customer expectations keep rising in both physical and online stores. At the same time, retailers handle huge amounts of data every day from suppliers, fulfillment centers, stores, delivery systems, and online platforms. 

The complexity keeps increasing. 

This is partly why AI adoption accelerated so aggressively over the last 18 months. Many retail leaders are no longer asking if AI is important. They are now trying to determine where it offers the greatest operational advantage. 

People often talk about Walmart’s AI investments as automation. But there is something more practical behind it. 

Operational responsiveness. 

Large retailers can no longer afford slow fulfillment systems or uncoordinated logistics. Even small inefficiencies become costly at scale. 

This pressure is why Walmart expanded its use of warehouse robots in regional distribution centers. The company’s Symbotic systems move inventory faster and adapt to changing warehouse layouts better than older automation systems.cs alone is not really the point. 

The bigger change is that retailers now want systems that can respond to changing conditions rather than rely on fixed workflows. Here, AI starts becoming infrastructure. 

Walmart is also investing in retail-specific AI systems

Generic AI models can help with general productivity tasks. But retail environments are more complex than that. 

Retailers work on pricing, inventory forecasting, customer engagement, fulfillment, supplier management, and logistics planning simultaneously. Large organizations now want AI systems that are trained for these specific needs. 

Walmart’s Wallaby initiative reflects that direction

The company has also reportedly introduced internal AI coordination systems capable of managing large numbers of AI agents operating across different functions simultaneously. 

This could become one of the toughest enterprise AI problems in the next few years. 

Not deploying AI. 

Managing AI complexity once it spreads across operations. 

Amazon approached AI in retail like long-term infrastructure

Amazon’s current AI position did not emerge overnight. The company spent years building cloud infrastructure, machine learning systems, fulfillment automation, and centralized data systems before generative AI became a mainstream business strategy, and then people sometimes realized. 

Today, AI influences almost every layer of Amazon’s retail ecosystem: 

• recommendations 

• search 

• warehouse operations 

• logistics planning 

• customer support 

• advertising systems 

• fulfillment coordination 

Its recommendation systems reportedly drive a major portion of e-commerce sales. But recommendation engines are only part of the story now. 

Much of Amazon’s AI work focuses on making operations more efficient at scale. Operational improvements matter enormously 

This is where the scale changes the economics. Delivery costs or warehouse inefficiencies may appear insignificant in isolation. Across Amazon’s network, those tiny gains compound rapidly. 

That is why the company continues to invest heavily in logistics optimization, robotics systems, forecasting models, and supply chain automation. 

Executives have repeatedly emphasized how small operational efficiencies eventually translate into billions of dollars at scale. This point highlights an important aspect of AI in retail. The most valuable AI systems are often not the most visible ones. 

These are the systems that quietly reduce operational friction behind the scenes. 

AWS also changed how retailers access AI infrastructure

Amazon has influenced the broader AI landscape through AWS just as much as through its retail business. It is a tier for companies trying to adopt machine learning infrastructure without building everything internally from scratch. 

That accessibility significantly accelerated adoption across retail.  But easier access to AI tooling created another challenge. 

Many companies found that it is much easier to deploy AI tools than to integrate them into their existing, often fragmented, systems.  And that gap still persists across much of retail. 

AI in supply chain management became impossible to ignore. Supply chains pushed AI adoption forward faster than many executives initially expected. 

Retailers spent the last several years dealing with shipping delays, supplier instability, changing demand patterns, inventory shortages, and regional disruptions happening almost simultaneously. 

Traditional forecasting systems often reacted too slowly. 

AI systems are increasingly being used because retailers want faster awareness of inventory movement, logistics, supplier performance, and demand planning. Especially in retail environments, where timing mistakes can quickly become expensive. 

One retailer reportedly avoided major financial losses by combining optimization systems with language models that translate operational data into practical recommendations for managers. 

The most interesting part was not just the accuracy of the predictions. It was the decision speed, increasingly important across enterprise AI adoption. 

Conversational AI in retail is changing customer behavior

Customer service expectations changed surprisingly quickly once AI-powered systems became more common. 

Consumers now expect immediate responses, personalized recommendations, and faster support across digital channels.  Retailers noticed that shift almost immediately. Modern conversational AI systems now support: 

• order tracking 

• returns handling 

• product recommendations 

• troubleshooting 

• personalized shopping assistance 

Some companiSome companies are seeing customers make purchasing decisions faster when conversational systems make the buying process smoother. There is another side to this trend, too. 

As more retailers use similar AI systems, it becomes harder to stand out with customer experience. Consumers quickly get used to new levels of convenience. 

What seems impressive today often becomes the norm tomorrow. 

Generative AI in retail is expanding beyond marketing teams

Early generative AI adoption in retail focused heavily on marketing workflows.

Product descriptions. Advertising copy. Campaign assets. That phase moved quickly. 

Retailers are now experimenting with generative AI in search, merchandising, e-commerce, customer engagement, and many other support systems, which has caught attention fast. 

Traffic from AI-driven discovery systems has risen sharply in recent retail cycles. Many retailers are now trying to understand how AI-generated shopping journeys might permanently change e-commerce behavior. 

Still, there is growing skepticism beneath all the excitement. Because eventually, most retailers will gain access to similar foundational AI technologies. 

At that point, how well companies execute their operations becomes more important again. Execution is where retail leaders separate themselves. 

AI in retail still depends heavily on infrastructure quality

This may be the most important lesson from Walmart and Amazon. 

AI systems perform much better when they are connected to a strong operational infrastructure. 

Many retailers still operate fragmented environments in which inventory systems, logistics platforms, customer databases, supplier tools, and analytics systems remain disconnected. 

In these situations, even advanced AI systems have trouble. 

That is why companies leading enterprise AI adoption spent years investing in centralized operational infrastructure before aggressively scaling AI. 

The infrastructure came first. The AI value expanded later. 

That order is much more important than many organizations realized during the early days of AI excitement. 

What CTOs should pay attention to now?

Retail companies are now entering a tougher phase of AI adoption. The age was relatively straightforward. Companies could deploy isolated AI tools without changing large parts of the organization itself. 

Scaling AI across operations is a different challenge. 

That requires: 

• integrated data environments 

• scalable infrastructure 

• operational coordination 

• governance systems 

• continuous model monitoring 

• cross-functional execution 

More and more, retailers are finding that having mature infrastructure is just as important as having strong AI capabilities. 

In the next few years, the most successful companies probably will not be those using the most AI tools. 

Instead, they will be the ones who integrate AI into their operations more effectively than their competitors. 

In brief

AI in retail is moving from isolated experiments to long-term operational strategy. Walmart and Amazon show that successful AI adoption depends less on flashy features and more on strong infrastructure, supply chain coordination, operational visibility, and scalable execution. For CTOs, the main challenge is not whether to adopt AI, but whether the organization is ready to scale it effectively.