Walmart's SVP Just Retired the Biggest Myth in Enterprise AI
What the world's largest retailer learned by actually doing the work
At the 2026 Autonomous Summit, Dave Glick, SVP of Enterprise Business Services at Walmart, sat down with Philippe De Ridder, CEO of Board of Innovation, for a conversation that should be required listening for every enterprise leader.
Not because Dave shared a polished corporate narrative. Because he shared what actually happens when the world's largest retailer stops planning and starts building.
The takeaway that caught everyone off guard: "It's not overhyped. It's underhyped."
The Myth Worth Retiring
When Philippe asked Dave to name one myth about enterprise AI he'd like to retire, the answer was immediate: "The myth is that it's hard."
That's a bold claim from someone operating at Walmart's scale. But here's what backs it up:
People who have never written a line of code in their lives are now building agents at Walmart. Thousands of business users have been trained on command-line interfaces. About half of them keep coming back. They're not just using AI. They're becoming engineers.
Dave's philosophy is deceptively simple: "If you move the customer and the engineer closer together, good things will happen. When the customer and the engineer are the same person, they're going to get what they know they wanted."
The "Nano Agent" Strategy
Walmart has a prioritization problem every enterprise faces. Individual teams want tools built for their specific needs. Engineering can't prioritize a solution that only helps one team when enterprise-wide projects are on the table.
The old answer: "No, that's not big enough."
The new answer: "Let me show you how to do it yourself."
This is the Nano Agent philosophy. Rather than centralizing all AI development, Walmart empowered business users to build their own solutions. Not with complex platforms. With direct access to AI coding tools.
The result? Thousands of what Dave calls "citizen engineers." Business people who understand their own problems better than any requirements document could capture. People who now have the tools to solve those problems themselves.
This isn't about replacing engineers. It's about eliminating the bottleneck between knowing what you need and getting it built.
Governance That Doesn't Stop Progress
Here's where most enterprises get stuck. They see the speed at which AI is moving and immediately reach for the brakes. New policies. New committees. New approval chains.
Walmart took a different approach.
Dave's team worked with their Chief Compliance Officer and came to a realization: existing data governance and privacy policies are sufficient. Rather than inventing new frameworks for AI, they applied the policies they already had and moved forward.
Dave shared a story that crystallized the approach. The Chief Auditor, the person responsible for internal oversight, told Dave he had downloaded AI tools and written some Python over the weekend. But he couldn't get an API key to access production systems.
Dave's response: "Good. The system is working as it should."
The philosophy: let people experiment freely. But maintain a high bar for production access. Speed and safety aren't opposites. They're complementary when you design for both.
The Vibe Coding Session
Perhaps the most telling moment came when Dave described a spontaneous session with his engineers. They were in town for a week. Dinner plans were on the table.
Instead, Dave proposed an alternative: "How about we do vibe coding instead?"
From 6pm to midnight, the team built an agent builder. The goal was audacious: create a tool where you could input a standard operating procedure and have it automatically generate an agent to execute that process.
They got a working UI. They built their first agent. In one evening.
One of Dave's team members called him afterward with a realization that stopped him in his tracks: "This is not overhyped. This is underhyped. I haven't written code in 10 years, and I built a SaaS product in one weekend."
What This Means for Enterprise Leaders
Walmart's approach inverts the conventional playbook. Most enterprises spend two years building guardrails before anyone builds anything. Walmart spent two years thinking about risks, then decided to start doing.
The difference shows up in momentum. When you actually build, you hit real barriers. Working with compliance, legal, InfoSec. Running through those barriers rather than theorizing about them.
Three principles emerge from Walmart's approach:
First, democratize the capability. When business users can build their own solutions, engineering becomes a force multiplier rather than a bottleneck. The people closest to problems become the people solving them.
Second, use existing governance. The instinct to create new AI-specific policies often creates more friction than protection. Start with what you have. Find edge cases as they emerge. Don't pre-solve problems you haven't encountered.
Third, just start. Every theoretical planning cycle is a cycle not spent learning from actual implementation. The insights Dave's team gained came from doing, not planning to do.
The Speed of Change
When Philippe asked how Dave stays current in a space evolving this fast, the answer was refreshingly practical: podcasts, conversations with practitioners, and most importantly, constant building.
"Every morning we wake up and there's a new model or a new tool," Dave said. "So we have to continue to iterate and continue to dive deep."
This isn't a space where you can delegate learning. Leaders who want to drive AI transformation need to get their hands dirty. Build agents. Try the tools. Discover firsthand what's possible.
The Bottom Line
Three years after ChatGPT's launch, most enterprises are still in planning mode. Walmart has thousands of citizen engineers building solutions, a governance model that enables rather than constrains, and an executive team that writes code alongside their teams.
The myth that AI is hard persists because most organizations haven't tried. They've researched. They've strategized. They've formed committees.
Walmart started doing.
The gap between those approaches grows wider every day. And as Dave put it: the world is our oyster. We're only limited by what we can think of.
This article is based on a fireside conversation at the Autonomous Summit, hosted by Board of Innovation. HauerX Holdings backs AI-native growth companies building the future of enterprise performance.
About the Author: Jason Hauer is CEO of HauerX Holdings, a portfolio company backing and building AI-native growth companies for enterprise transformation. Board of Innovation is a HauerX portfolio company partnership.
