What Walmart and Target’s AI Bets Tell You About Modern Retail Marketing

Walmart and Target are spending heavily on AI, and neither company is doing it for the press release. Both retailers have embedded artificial intelligence across pricing, inventory, personalisation, and advertising in ways that are starting to show up in their commercial results. What they are building tells you something important about where retail marketing is heading, and what it means for any marketing team trying to compete with less resource.

The gap between what these two retailers are doing with AI and what most marketing teams are doing is not a technology gap. It is a strategy gap. And that distinction matters more than the tools themselves.

I have spent time working with retail clients across multiple categories, and the pattern I see repeatedly is marketing teams investing in tools before they have sorted out their data. That is the wrong order. Walmart and Target have not made that mistake. Their AI strategies are built on top of data foundations that took years to construct, and that sequencing is not accidental.

What Is Walmart Actually Doing With AI?

Walmart’s AI strategy is more commercially integrated than most people realise. The company has been building out its advertising business, Walmart Connect, and AI sits at the centre of how it monetises that platform. When a supplier pays to promote a product on Walmart’s digital properties, the placement decisions, bid optimisation, and audience targeting are increasingly AI-driven. That is not a marketing feature. That is a revenue line.

Beyond advertising, Walmart has applied AI to demand forecasting at a scale that most retailers cannot match. With thousands of stores and a supply chain that spans continents, even marginal improvements in forecasting accuracy translate into material cost savings. The company has also used AI to improve its search functionality, both on its website and in-store, making product discovery faster and reducing the friction that causes shoppers to abandon a purchase.

Walmart’s acquisition of Vizio in 2024 was widely reported as a hardware play, but the more interesting angle is the data angle. Vizio’s SmartCast platform gives Walmart access to viewership data from millions of connected televisions. That data, combined with Walmart’s purchase history data, creates a closed-loop attribution model that most advertisers would pay a significant premium to access. When a consumer sees an ad on their Vizio TV and then buys the product at Walmart, Walmart can see both events. That is a powerful position for an advertising platform to be in.

If you want to understand how AI is reshaping the broader marketing technology landscape, the AI Marketing hub at The Marketing Juice covers the tools, strategies, and commercial implications in depth.

What Is Target Doing Differently?

Target’s AI strategy has a different centre of gravity. While Walmart has been aggressive about building an advertising business on top of its data, Target has focused more on using AI to improve operational efficiency and the in-store experience. That is not a lesser ambition. It is a different commercial logic.

Target has invested heavily in AI-powered inventory management, using machine learning to predict which products will sell in which stores at which times. This sounds like a logistics problem, but it is also a marketing problem. A product that is out of stock cannot be sold. A store that consistently has the right products in the right place builds a reputation for reliability that no advertising campaign can manufacture.

Target has also been building out its own retail media network, Roundel, which uses its first-party data to help brands reach Target shoppers both on and off Target’s own properties. The off-site element is significant. Roundel can serve ads across the open web and on third-party platforms, using Target’s purchase data to inform the targeting. That is a meaningful capability, and it is one that requires a strong data infrastructure to operate credibly.

Where Target has been particularly interesting is in its use of AI for personalisation at the individual level. Its Circle loyalty programme generates a continuous stream of behavioural data, and Target uses that data to personalise offers, communications, and product recommendations in ways that go beyond simple segmentation. The ambition is to make every customer interaction feel relevant without feeling intrusive, which is harder than it sounds.

Where Both Retailers Are Competing on the Same Ground

Despite their different emphases, Walmart and Target are converging on the same strategic territory: the use of first-party data to build advertising businesses that generate revenue from brands, while simultaneously using that data to improve the shopping experience for consumers. This is not a coincidence. It is a response to the same commercial pressures.

Retail margins are thin. The economics of physical retail have been under pressure for years. Building a media business on top of a retail operation is one of the more elegant solutions to that problem, because the data that makes the media business valuable is generated automatically by the retail operation. You do not have to create the data. You just have to build the infrastructure to use it.

I saw a version of this dynamic play out when I was working on performance marketing for clients in the travel sector at lastminute.com. The moment you have purchase data, you have something that most advertisers want. The challenge is building the systems to monetise it without degrading the customer experience that generated the data in the first place. Both Walmart and Target are handling that tension in real time, at a scale that makes the stakes very high.

Generative AI is also entering both companies’ workflows, though more quietly than the headlines suggest. AI-assisted content creation, product description generation, and creative testing are all areas where both retailers are experimenting. Tools in this space have matured considerably, and resources like HubSpot’s breakdown of AI content tools give a useful picture of what is available to teams operating at a much smaller scale than Walmart or Target.

The Data Advantage That AI Cannot Replace

Here is the thing that gets lost in most coverage of retail AI: the AI is not the competitive advantage. The data is. Walmart and Target have decades of transaction history, billions of individual purchase events, and the physical infrastructure to generate new data continuously. The AI models they run are only as good as the data they are trained on, and their data is genuinely hard to replicate.

This matters for how you interpret their strategies. When Walmart announces a new AI-powered pricing tool, the announcement is not really about the tool. It is about the fact that Walmart has enough pricing and demand data to make that tool work in a way that a smaller retailer with thinner data cannot. The tool is the visible part. The data is the moat.

Early in my career, I was asked to improve the performance of a client’s email programme. The instinct in the room was to talk about subject line testing and send-time optimisation. But when I looked at the data they had, the bigger opportunity was in how they were segmenting their list. They had years of purchase history sitting in a CRM that nobody had properly interrogated. The AI tools available now would have made that analysis faster, but the fundamental insight was the same: the data you already have is almost always more valuable than the tools you are thinking about buying.

For teams looking to understand how AI tools can work with the data they already have, Semrush’s overview of AI optimisation tools is a reasonable starting point for understanding the landscape without getting lost in vendor claims.

What the Retail Media Arms Race Means for Brands

If you are a brand that sells through Walmart or Target, their AI investments have direct implications for your marketing budget. Both retailers are actively encouraging brands to spend more through their retail media networks, and the pitch is compelling: you can reach shoppers who are already in a purchase mindset, using targeting data that is based on actual purchase behaviour rather than inferred intent.

The challenge is that this spend often comes out of trade marketing budgets rather than digital marketing budgets, which creates internal friction for brands that have not updated their organisational structures to reflect how retail media actually works. The people who manage retailer relationships and the people who manage digital media are often different teams with different reporting lines, and the result is that neither team is optimising the full picture.

I have judged the Effie Awards and seen entries from brands that were doing genuinely sophisticated work across retail media and brand media simultaneously. The ones that worked were not the ones with the biggest budgets. They were the ones where the brand team and the trade team were actually talking to each other and using shared data to make decisions. That sounds basic, but it is rarer than it should be.

The measurement question is also unresolved. Both Walmart Connect and Roundel offer attribution reporting, but those reports are produced by the same platforms that are selling the media. That is a conflict of interest that deserves more scrutiny than it typically gets. Independent measurement matters, and brands that rely solely on retailer-provided attribution data are working with a perspective on reality, not reality itself.

What Smaller Marketing Teams Can Take From This

The Walmart and Target AI story is not just a story about big companies doing big things. There is a strategic logic embedded in what they are doing that applies at any scale, and it is worth extracting that logic rather than dismissing it as irrelevant to smaller operations.

The first principle is sequencing. Both retailers built their data infrastructure before they tried to monetise it. If your team is looking at AI tools before you have a clear picture of what data you own and how it is structured, you are doing it in the wrong order. Start with a data audit. Understand what you have, where it lives, and how clean it is. The tools can wait.

The second principle is integration. Neither Walmart nor Target is using AI in a single department. Pricing, inventory, media, personalisation, and supply chain are all connected. The value of AI compounds when the outputs of one system feed into another. Most marketing teams use AI tools in isolation, which limits what they can do. Connecting your SEO workflow to your content workflow to your email workflow is a smaller version of the same idea. Resources like Moz’s guide to AI tools for SEO and Semrush’s breakdown of AI email assistants can help teams start building those connections at a practical level.

The third principle is commercial clarity. Every AI investment Walmart and Target have made can be traced back to a commercial outcome: lower costs, higher conversion, more advertising revenue, better margin. If you cannot draw that line for a tool you are considering, that is a signal worth paying attention to.

When I was growing an agency from 20 to 100 people, one of the disciplines I tried to maintain was asking what a new capability was actually going to change commercially. Not what it was going to enable in theory, but what would be different in the P&L in twelve months. That question kills a lot of shiny object investments before they happen, and it is a useful filter for AI tools as much as anything else.

The Risks Neither Company Talks About Publicly

There are real risks in the strategies both retailers are pursuing, and they tend to get less airtime than the success stories.

The first is consumer trust. Personalisation at the level both companies are pursuing requires collecting and processing a significant amount of behavioural data. Consumers are increasingly aware of this, and the line between helpful personalisation and surveillance-adjacent behaviour is one that both retailers need to manage carefully. A single high-profile data incident could damage the trust that makes their data assets valuable in the first place.

The second is supplier dependency. As Walmart and Target become more powerful advertising platforms, the brands that sell through them become more dependent on those platforms to reach their own customers. That is a structural shift in the balance of power in the retail ecosystem, and brands that do not think carefully about their media mix are at risk of becoming entirely dependent on retailers for consumer access.

The third is model decay. AI models trained on historical data reflect historical patterns. When consumer behaviour shifts, models that were performing well can degrade quickly. Both retailers invest heavily in model maintenance and retraining, but this is a continuous cost that is easy to underestimate. For smaller teams adopting AI tools, the same risk applies at a smaller scale: a model or tool that works today needs to be monitored, not just deployed.

For a broader view of how AI is reshaping marketing strategy and where the real opportunities sit, the AI Marketing section of The Marketing Juice covers these themes across industries and team sizes.

About the Author

Keith Lacy is a marketing strategist and former agency CEO with 20+ years of experience across agency leadership, performance marketing, and commercial strategy. He writes The Marketing Juice to cut through the noise and share what works.

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