Walmart's AI Pivot: Assessing the Infrastructure of Agentic Commerce

Walmart is attempting a high-risk, high-reward transformation, betting its massive scale can build the foundational AI infrastructure for the next retail paradigm. The core thesis is clear: the company is pivoting from a discount retailer to a tech-powered enterprise, with its central platforms becoming the shared operating system for a new era of agentic commerce.

This structural pivot is now being formalized. Effective February 1, 2026, Walmart is centralizing its enterprise platforms to accelerate shared capabilities, freeing up its operating segments to be more focused on and closer to customers. This reorganization, led by a newly appointed Chief Growth Officer for Walmart Inc., is a deliberate move to build an internal tech layer that can scale across all its businesses. The goal is to create a unified foundation where innovations in digital advertising, marketplace operations, and data ventures can be leveraged company-wide.

At the heart of this shift is a specific AI strategy. Walmart is not chasing generic large language models. Instead, it is deploying what it calls "purpose-built agentic AI", specialized tools trained on its decades of proprietary retail data. This approach, described as "surgical," focuses on highly specific tasks that can be stitched together to orchestrate complex workflows. The company's proprietary MLOps platform, Element, is the in-house "factory" designed to avoid vendor lock-in and optimize compute power, giving it speed and flexibility competitors lack.

The market is being signaled this ambition. The US$905 billion retailer's transfer to Nasdaq was more than a symbolic gesture; it was a bold claim that Walmart is no longer a traditional discount chain but a tech-powered enterprise. This move frames the company's future around its ability to use AI to fundamentally rewire its operations and, potentially, to sell that infrastructure to others. The transformation is now underway, with the structural changes and the AI strategy converging to build the rails for a new technological S-curve.

Execution Drivers: The S-Curve of AI Adoption

The strategic pivot is now being measured against a clear adoption metric. In the first quarter of 2025, generative AI search drove a 22% global e-commerce growth for Walmart. This is the exponential signal the company is chasing. It shows the technology is moving beyond novelty to become a direct engine for scaling core revenue, transforming the online shopping experience from a scroll-driven chore to a goal-driven journey. This growth rate is the kind of inflection point that defines a technological S-curve.

To ride this curve, Walmart is building the infrastructure for deep integration. The company is constructing a suite of four super agents designed to handle complex workflows across shopper, employee, and partner experiences. This isn't about isolated chatbots; it's about creating a layered system where AI agents can orchestrate tasks like automated grocery replenishment or personalized styling suggestions. The vision is for these agents to understand intent and serve it up with minimal friction, a shift that could dramatically increase customer engagement and loyalty.

The most telling indicator of true adoption, however, is internal. Walmart is using its own AI to bootstrap its development. The internal tool Wibey uses AI to bootstrap its own development, creating a self-improving loop. This is a critical milestone. It means the company is not just applying AI to its retail problems but is embedding it into its own engineering and product cycles. This internal feedback loop accelerates innovation speed and reduces the time to market for new features, giving Walmart a compounding advantage in building its AI infrastructure layer.

The bottom line is that Walmart is moving from the "tinkering" phase to one of measurable, exponential adoption. The 22% e-commerce growth proves the technology works at scale. The build-out of super agents provides the architecture for deeper integration. And the self-bootstrapping of internal tools signals that AI is becoming the core operating system, not just an add-on. This is the setup for a sustained growth trajectory on the next retail paradigm.

Financial Impact and Valuation Implications

The strategic bets are translating to real financial performance, but the path to a new valuation paradigm is fraught with execution risk. The primary impact is not a sudden shift to high-margin software, but a relentless drive to convert AI-driven efficiency into sustainable margin expansion within the razor-thin economics of retail. This is the core challenge: converting operational gains into bottom-line power.

The evidence shows the engine is running. In the first quarter of 2025, generative AI search drove a 22% global e-commerce growth. That's the exponential adoption curve in action, directly boosting top-line revenue. More importantly, Walmart's internal tools are delivering massive cost savings. Its AI-powered data operations improved over 850 million product catalogue points, a task that would have required 100 times the headcount manually. Supply chain AI eliminated 30 million unnecessary delivery miles. These are not theoretical efficiencies; they are hard numbers that improve the operating leverage of a massive, capital-intensive business.

Yet, the retail S-curve is steep. Walmart's own CTO frames the AI strategy as "surgical", focused on specific tasks to solve complex workflows. This precision is necessary because the company is not escaping its core business-it is using AI to make it more profitable. The goal is to use these efficiency gains to protect and expand operating margins, which remain under pressure from the fundamental economics of discount retail. The risk is that these gains are consumed by competitive pricing or reinvested into further AI development, leaving the margin expansion story uncertain.

This creates the central valuation tension. Investors must price in the high risk of execution failure against the potential for a new, higher-margin infrastructure revenue stream. The company is already commercializing its supply chain optimization technology as a SaaS product. If its proprietary MLOps platform, Element, and its suite of purpose-built agents can be scaled beyond internal use, they could become a new profit center. But that future is speculative. For now, the stock's value hinges on whether Walmart can successfully navigate this dual challenge: using AI to squeeze more profit from its existing, low-margin retail operations while simultaneously building a new, higher-margin tech layer. The transfer to Nasdaq signals the ambition, but the financial markets will demand proof that the surgical AI tools can stitch together a more profitable future.

Catalysts and Risks: The Path to Agentic Commerce

The transformation from ambitious announcements to tangible results now hinges on a few near-term milestones. The key catalyst is the commercial launch of agentic commerce features, moving beyond pilot programs to everyday use. Walmart is already laying the groundwork, with a new partnership that will soon allow customers to shop through ChatGPT using Instant Checkout. More broadly, executives envision a shift where AI handles routine tasks like automated grocery replenishment. When these features become standard, they will provide the first clear signal that the company's "four super agents" are stitching together complex workflows to solve real customer problems, validating the core infrastructure build.

Yet the major risk is the persistent gap between grand AI visions and demonstrable improvements in customer lifetime value. Walmart's CTO frames the approach as "surgical," focused on specific tasks. This precision is necessary, but it also risks creating a collection of point solutions that don't yet coalesce into a compelling, sticky customer experience. The market will judge success not by internal efficiency gains, but by whether these AI tools make shoppers more loyal and spend more. If the technology remains a convenient add-on rather than a transformative habit, the valuation premium for a tech-powered enterprise will be hard to justify.

The leadership signal will be critical in the coming months. The new CEO, John Furner, is set to take the helm in February 2026. His first major strategic decisions will reveal the pace and depth of the AI transformation. Evidence shows the company is preparing for this shift, with a new Chief Growth Officer appointed to oversee the enterprise platforms that will house the AI infrastructure. Furner's choices on resource allocation, partnership priorities, and the balance between internal AI development and external collaborations like the OpenAI deal will be the clearest indicator of whether Walmart is committed to a rapid, execution-focused build-out or a slower, more cautious integration. The path to agentic commerce is now set, but its success depends on the leadership's ability to turn the power tools into a profitable, customer-obsessed reality.