Daimler Truck North America operates in a high-stakes industrial environment where manufacturing, freight movement, and supplier coordination depend on disciplined execution at scale. As one of the leading commercial vehicle manufacturers, the company works across complex operational systems in which data, process consistency, and human expertise all play a central role. In that setting, AI is not simply a technology conversation. It becomes a leadership, culture, and operating-model question.

In this third and final part of a three-part interview series, Edgar Gallo, Chief Data Officer at Daimler Truck North America, in conversation with Susan Wilson of Alation, turns from AI foundations and use cases to the broader implications of adoption. The conversation focuses on how leaders build trust inside teams, how organizations scale AI responsibly without losing control of knowledge, and how the next phase of enterprise AI may depend on ownership, interoperability, and a more adaptive kind of leadership.

In Part 1, Gallo discussed how AI agents and metadata are reshaping manufacturing, with a focus on trust, culture, and change management. In Part 2, he turned to real-world applications, showing how AI agents can reduce repetitive work in supply chain and aftermarket operations.

Trust begins with taking fear seriously

For Gallo, scaling AI inside an enterprise starts with a simple reality: change triggers fear. Employees do not react only to the technology itself but also to what they think it may mean for their stability, relevance, and livelihoods. Rather than dismissing that reaction, he treats it as a necessary part of change management.

“I write that fear on a post-it and keep it, and as things evolve, go back and bring that fear back and just check in, ‘Are you still concerned about this? Is this still a worry for you?’”

Gallo does not describe transformation as something that can be pushed through by telling people to simply trust the process. Instead, he emphasizes working collaboratively so that the change feels visible and understandable rather than abrupt or imposed.

Curiosity makes the shift durable

What begins as caution can, in Gallo’s telling, turn into something far more productive. Once people start engaging with new systems, they often discover paths of learning they would not have found otherwise. He points to the way once-specialized concepts no longer feel foreign to teams that have been brought into the work early and meaningfully: “They all know about it. They all have their own curiosity unleashed, and they’re all learning about these things, and how to implement them or find out where it’s being used across the company, and how they can leverage it for their job.”

That matters because it turns AI adoption into more than a tooling exercise. It becomes a mechanism for professional growth. Gallo suggests that when people feel their role is being expanded rather than threatened, they begin to engage differently. They become more invested, more exploratory, and ultimately more capable.

Ownership has to be designed into the process

A central theme in Gallo’s thinking is that transformation only lasts when the business owns it. He traces that mindset back to earlier work with data science teams, where he insisted that every engagement include a business participant from the very beginning.

“We start in the driver’s seat, but from day one, I need you to tell me a name because this person is going to be in the car with us, maybe in the back seat watching things happen. Eventually, they move into the passenger seat to get up close and ready, and eventually, we’re going to switch. And they’ll be driving, and I’ll be co-piloting.”

In that metaphor, AI adoption is not something a central team delivers and then walks away from: “Because this is how knowledge transfer happens. This is how the ownership happens, and this is how you can maintain accountability by having clear expectations so you have predictable results.”

Responsible AI starts with protecting what the company knows

When asked how organizations should scale AI responsibly, Gallo frames the question through the lens of business realities. Responsible use, he suggests, will vary by sector, but the starting point is clear: companies need boundaries around knowledge, exposure, and intellectual property.

“Your people are sources of knowledge. When you augment them with LLMs, they become even brighter.”

That amplification is valuable, but it also creates a management challenge. As employees become more capable with AI, they generate more insight, more efficiency, and more internal leverage. Organizations, in his view, have to think carefully about how to keep that value inside the enterprise and channel it through safe environments rather than uncontrolled tools.

Gallo is equally direct that warning people is not enough. Companies have to provide secure alternatives that make the right behavior practical: “It’s not enough to say ‘don’t do that.’ You need to follow up with the sentences, ‘Do it over here; we have this for you. We have this environment for you.’ This is how we keep it safe.”

Scale requires pragmatism, not attachment

Gallo also argues that responsible AI adoption demands speed in experimentation and discipline in deciding what deserves to continue. According to him, organizations should not cling to projects simply because they are fashionable or widely discussed.

The standard, he says, should be business fit. If a tool or use case does not align with how the company creates value, it should not be indulged indefinitely.

Gallo’s view of what comes next extends beyond internal enablement. He argues that companies cannot think about AI as a closed box. As partners, suppliers, and other stakeholders develop their own systems and standards, value will increasingly depend on how well those systems can interact.

He describes a future in which outputs from one company’s agent may need to be formatted so another company’s agent can consume them effectively. In that sense, collaboration becomes both technical and organizational. Enterprises will need to think not just about internal workflows but also about the “safe door in and out” for AI-enabled transactions across ecosystems.

Adoption matters more than novelty

When Gallo talks about metrics, he does not begin with model sophistication or technical performance. He begins with adoption. But even that measure is not about whether a system exists or runs. It is about whether it reaches the people closest to the work and starts producing value in execution.

He then describes what that looked like in practice for him, recalling the moment a catalog began receiving questions from the factory floor: “When this product got close to them, I realized we’re now adding value. It’s not just on the thinking side; it’s on the execution side.”

The biggest misconception about AI, he says, is “that it can do everything.”

Metadata’s next role is enabling reuse

Looking ahead, Gallo sees metadata becoming even more important, not only as a trust layer but also as a connective layer inside the company.

He says the next step is turning metadata into a marketplace that helps teams discover and reuse what already exists across the business instead of rebuilding the same assets from scratch.

In his view, metadata creates the connectivity that allows one domain to adapt another’s dashboards, ETL logic, or products with only small changes, enabling what he calls “positive proliferation” rather than duplication. As he puts it, “Those same dashboards are already there, with a tweak.”

The modern CDO has to keep reinventing the role

At the close of the conversation, Gallo reflects on the CDO role itself, suggesting that one reason it has historically seen so much turnover is that its objectives can feel impossibly large.

He argues that the CDO role lasts only when leaders stay adaptable and tie their work to enduring business priorities. He suggests that while technologies keep changing, the core goals do not, which is why data leaders must keep reinventing themselves in ways that help the enterprise deliver value.

As he puts it, “Be flexible and be like a Renaissance mind. Reinvent yourself as you adopt technology because the business imperatives remain: be profitable, reduce cost, and make the most out of your human capital.”

CDO Magazine appreciates Edgar Gallo for sharing his insights with our global community.