Enterprise Field Notes · Issue #7

What Walmart Understood That the Other 95% Didn’t

Walmart built the blueprint for enterprise AI. Here is the part you can actually copy, without their budget.

By Ben Pickett · June 4, 2026

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By Ben Pickett

I sell AI for a living. So it should bother you that the best blueprint for enterprise AI I have read this year did not come from an AI company. It came from a retailer. And almost nobody outside retail has read it.

Last week I wrote Andrew a letter about why his AI isn’t working. Andrew is the operator I keep meeting in different bodies. Smart, funded, two years into an AI program, and quietly furious that the productivity he was promised never arrived. His workforce says nothing changed. His board deck says everything changed. Somewhere between those two sentences is the truth, and it is costing him a fortune.

Andrew is not unusual. He is the rule. Last year MIT’s NANDA initiative studied 300 enterprise AI deployments for a report it called The GenAI Divide. Ninety-five percent of those deployments had produced no measurable impact on the P&L. Not small impact. None. Only about 5% were pulling real revenue out of the work.

So after I sent that letter, I went looking for the 5%. Not someone with a thesis. Someone with a system in production and the numbers to prove it. I found them. The strange part is that they published the entire blueprint. It was Walmart.

And the best part is the piece that matters is copyable. Not the two years and the five thousand engineers. The shape. That is what the rest of this is about.

What Walmart actually built

Start with the shape, because the shape is the lesson.

Walmart did not build one giant assistant and hope it would do everything. They built four. Each one is purpose-built for a single audience, trained on that audience’s data, and accountable for that audience’s outcomes. Sparky serves shoppers. My Assistant serves the associates who run the stores. Marty serves the sellers, suppliers, and advertisers. WIBEY serves the developers building on top of all of it. Four super agents, four constituencies, no pretending that a customer and a warehouse associate need the same thing.

And here is what almost everyone gets backward. Walmart’s move was not to add agents. It was to tame them. Before the four super agents, Walmart had agent sprawl, a pile of bots multiplying faster than anyone could govern them. The super-agent structure was subtraction, not addition. They imposed order on a mess. Most enterprises are still in the addition phase, racing to stand up one more bot, when the lesson from the company that made it work is the opposite. The win was not more agents. It was control.

Underneath those four sit roughly 200 task-specific agents. Narrow, boring, reliable. And the piece most people skip past is WIBEY, because WIBEY is not really a chatbot for engineers. It is an orchestration layer. It interprets intent, finds the right agent, and coordinates the handoffs across the whole system. Walmart runs it as a federated model, where individual teams own their own agents while the orchestration layer makes sure those agents can find each other, talk to each other, and be trusted in production. All of it sits on Element, Walmart’s own machine-learning platform.

Suresh Kumar, Walmart’s global chief technology and development officer, put their stance in one sentence. Walmart is all in on agents. The architecture is the proof that he meant it structurally, not as a press line.

I want to be careful here, because the easy version of this story is wrong. Walmart did not invent agentic architecture. Plenty of people sketched this diagram on a whiteboard before Walmart did. What Walmart did is harder and rarer. They shipped it at scale, to millions of users, and they have the business results to show it works. The whiteboard is free. The proof is what costs two years.

The proof

This is where most AI stories go quiet, so let me stay loud.

Start with how serious they were. Walmart’s 2025 proxy shows it paid Daniel Danker, the executive vice president running its AI push, 44.1 million dollars. That is more than the 29.2 million it paid CEO Doug McMillon. A retailer decided the person building its agent architecture was worth more than the person running the whole company. Sit with that.

On its recent earnings calls, Walmart told investors that shoppers who use Sparky spend about 35% more per order than the average shopper. Units purchased through Sparky more than quadrupled in a single quarter. By the end of the fiscal year, half of all Walmart app users had already tried it. And this is happening inside a company that just did 713 billion dollars in revenue, so it is not a rounding error in a sandbox. It is moving the biggest number there is.

These are not engagement metrics. Nobody is celebrating time on page. These are dollars per order and units sold, the two numbers a CFO actually trusts. That is the difference between an AI program and an AI announcement. One of them changes the P&L.

So we have a working reference architecture, in production, with the receipts. The natural next thought for every operator reading this is the dangerous one. Great, let’s go do what Walmart did. Do not do what Walmart did.

Why most of us cannot copy it directly

Here is the part the architecture diagrams leave out. Walmart spent something like two years and on the order of five thousand engineers building this. They wrote their own orchestration. They built their own ML platform. They trained domain-specific models on proprietary data most companies will never have. They had the balance sheet to fund a multi-year bet that produced nothing visible for a long time, and the institutional patience not to kill it in month nine.

You probably have none of that. Andrew certainly doesn’t. He has a real budget, a small team, a board that wants results this year, and a competitor moving just as fast.

And the data says the build-it-yourself instinct is exactly the trap. That same MIT report found that when companies buy AI capability from specialized vendors and partner for it, the projects succeed about 67% of the time. When they build it internally, they succeed roughly a third as often. McKinsey’s State of AI 2025 saw the other half of the problem: 62% of organizations are now experimenting with AI agents, but only 23% have managed to scale them across the enterprise. Everyone can run a pilot. Almost nobody can run the system.

If the lesson from Walmart were to spend two years and five thousand engineers, the lesson would be useless to the 99% of companies that need it most. But that is the wrong lesson. The resourcing is not the lesson. The resourcing is just what it cost Walmart to discover the lesson on our behalf. The lesson is the shape.

The portable architecture

Strip Walmart out of the Walmart story and look at what remains. Take away the proprietary models, the custom platform, the five thousand engineers. What is left is a pattern, and the pattern is portable.

Group your agents by the people they serve, not by the technology. Customer, employee, partner, developer. Those four constituencies exist in almost every enterprise, whether you sell sneakers or audits or insurance. Picture a mid-size accounting firm. Its customers are clients. Its employees are the staff doing the work. Its partners are the referral sources and the software it files through. Its builders are the small internal team wiring the tools together. That is the same four-quadrant shape Walmart drew, minus the 713 billion dollars. Give each quadrant a purpose-built agent that knows its world.

Under each of those, specialize. Build or buy narrow task agents that do one job reliably, instead of one general assistant that is mediocre at everything and trusted with nothing.

Above all of it, run an orchestration plane. This is the part operators underestimate, and it is the part the market is finally catching up to. The analysts tracking this space now say orchestration and governance are eclipsing raw model capability as the main reason enterprises pick one platform over another. Read that again. The model is no longer the differentiator. The layer that routes, coordinates, governs, and keeps the system accountable is the differentiator. Walmart’s real moat is not Sparky. It is the orchestration above Sparky.

And then give the workforce access. Not a pilot for twelve people. The architecture only compounds when everyone is inside it.

Here is the honest part. For Walmart, that orchestration plane and the multi-model access underneath it were a two-year build. For the rest of us, the data is clear that they should be bought, not built. That is the part of this story I am closest to, because the orchestration and multi-model layer is exactly what we build at Swa. I am not going to turn this into a pitch. I will just tell you the most useful thing I learned from studying Walmart, which is that the layer everyone treats as plumbing is actually the product. The model is not the moat. The structure is.

I want to be honest about the part the diagrams do not show. That orchestration layer is the answer, and it is also where all the new risk concentrates. Every agent you add is another door someone can walk through. Govern them centrally or you have just built a faster way to leak. And the human question is the one even Walmart will not fake. Their own executive said out loud, we expect jobs to evolve and we do not know what that looks like yet. I believed them more for saying it, not less. Anyone selling you certainty on that is selling you something.

The close

For two years the enterprise conversation has been a fight about which model to pick. Open or closed. This vendor or that one. Benchmarks traded like baseball cards. Walmart skipped the entire argument. They did not win by picking the best model. They won by designing the best structure and letting the models be interchangeable parts inside it.

The companies that win the next decade are not the ones with the cleverest model. They are the ones with the clearest architecture. Walmart just showed all of us what one looks like in production, with the earnings to back it. You do not have their two years or their five thousand engineers. You do not need them. You need their shape.

So here is the one thing worth doing this week, and it does not require a budget or a single engineer. Get a whiteboard. Draw four boxes. Customer, employee, partner, builder. In each box, write the single agent that quadrant needs most. That is it. You will not have built anything yet, but you will have done more real architecture than 95% of the companies in that MIT study.

Stop arguing about models. Start designing the structure. Start there, then ship yours.

I’m Ben Pickett. I write Enterprise Field Notes. If your team is wrestling with the orchestration question, my inbox is open. What does your super-agent structure look like?

References

1. The GenAI Divide: State of AI in Business 2025. MIT NANDA initiative, reported by Fortune, August 2025. Across 300 enterprise AI deployments, 95% delivered no measurable P&L impact; tools bought from specialized vendors succeeded about 67% of the time, versus roughly a third as often for internal builds.
2. The State of AI in 2025. McKinsey, November 2025. Only about 6% of organizations qualify as high performers capturing significant value, and while 62% are experimenting with AI agents, just 23% have scaled them. McKinsey’s core finding is that the advantage is organizational, not technological.
3. From models to agents: a new era of intelligent systems at Walmart. Walmart Global Tech, August 2025. Walmart’s own account of WIBEY as an invocation and orchestration layer across its agentic ecosystem, built on the proprietary Element ML platform, alongside the launch of four super agents.
4. Walmart rolls out four AI ‘super agents’. Digital Commerce 360, July 2025. Reports CTO Suresh Kumar consolidating Walmart’s AI into four super agents for customers, associates, sellers and suppliers, and developers.
5. Walmart credits Sparky AI agent with lifting AOV, unit sales growth. Digital Commerce 360, May 2026. On Walmart’s May 2026 earnings call, Walmart U.S. CEO David Guggina said Sparky shoppers have about 35% higher average order value than non-users, and units purchased through Sparky more than quadrupled from the prior quarter.
6. Walmart’s Sparky AI agent increases order value. Constellation Research, 2026. Independent analysis of the average-order-value lift and of Sparky adoption, with roughly half of Walmart app users having tried it.
7. Walmart’s AI Chief Earned More Than Its CEO in 2025 With a $44.1M Payday. WWD, 2026. Reports that Walmart paid Daniel Danker, its EVP of AI, 44.1 million dollars in 2025, more than CEO Doug McMillon’s 29.2 million.

About the author

I'm Ben. I write Enterprise Field Notes, and by day I'm COO at Swa, after years running reliability, data protection, and database operations at Nike. The lesson that keeps proving itself: anything you cannot run without, and cannot walk away from, is a risk you have not priced yet. What is yours?

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Read more of Ben's Enterprise Field Notes at benpickett.com.

© 2026 Ben Pickett · Enterprise Field Notes