The U.S. Army Western Hemisphere Command, part of the U.S. Department of Defense, operates in an environment where data is not a support function but a strategic asset. In high-stakes public sector settings, decisions must be made with speed, precision, and accountability. That reality places a premium on trusted data, disciplined governance, and execution models that can withstand complexity at scale.

In this first part of a two-part interview series, Deepak Shah, Chief Data Officer at USAWHC, speaks with Adita Karkera, Chief Data Officer for Deloitte’s Government and Public Services, about what it takes to build enterprise data initiatives that last. The conversation focuses on how serious transformation begins: with a clear vision, a mission that turns aspiration into action, and goals that prove value over time.

Shah traces his path to the CDO role through more than three decades of work across global investment banking, asset management, and capital markets, where he leads data and technology initiatives spanning real-time trade processing, reporting systems, and enterprise data hubs. His work centers on bridging business and technology while building structures that standardize and streamline data across organizations.

Those experiences gradually expand into larger enterprise initiatives shaped by stakeholder alignment and governance. A key turning point came during Carnegie Mellon’s Chief Data Officer executive program, where an article he wrote on data governance drew the attention of leaders within the DoD and ultimately led to his appointment as CDO.

Why initiatives fail before they begin

For Shah, the most important stage of any major initiative is the beginning. He argues that organizations often undermine their own efforts before execution is even underway because they fail to establish a sufficiently clear vision, mission, and set of goals: “In my experience, this is where most initiatives either succeed or quietly fail before they even start.”

He describes this discipline not as a ceremonial planning step but as the operating logic behind transformation. Without it, execution fragments quickly. Different teams create disconnected solutions, each addressing their own local priorities, but without forming a coherent enterprise capability. Over time, that fragmentation leads to duplication, frustration, and a lack of alignment strong enough to carry the work forward.

Vision as the enterprise North Star

Shah frames vision as the organizing force behind transformation. In his view, it functions as the North Star, giving teams a common direction and helping ensure that individual efforts contribute to enterprise-wide outcomes rather than isolated projects.

Without the North Star, initiatives become disconnected projects instead of real enterprise transformation,” Shah explains.

He says that a strong vision does three things:

1. It establishes the enterprise frame: A governed foundation with federated domains that still have room to innovate. That balance, he argues, is essential. Too much centralization slows the organization down, while too little creates disorder.
2. The vision has to translate into disciplined execution: Shah warns that if an organization’s ambition stops at dashboards while manual processes remain unchanged, the effort has already fallen short. To him, automation and embedded controls must be part of the design from the start, or the vision remains aspirational rather than operational.
3. A clear vision aligns teams around a common direction: It helps them understand how their work connects to a broader strategy, which reduces duplication, improves collaboration across domains, and gives the organization something meaningful to rally around.

Mission turns aspiration into a flight plan

While vision defines direction, Shah sees mission as the mechanism that turns direction into motion. He describes it as the flight plan that prevents organizations from drifting toward shiny tools, fragmented priorities, or disconnected execution.

For a CDO, he says, that mission is straightforward: delivering trusted, decision-ready data so leaders can operate with speed, confidence, and accountability. In Shah’s formulation, mission is what keeps the organization focused on the outcomes it must deliver rather than the technologies it happens to be experimenting with at any given moment.

He argues that vision and mission alone are not enough. Goals are the element that makes progress visible and defends the value of the investment over time. Without them, he says, leaders inevitably begin asking what the organization has actually gained: “Without goals, progress is invisible.”

The distinction he draws is important: meaningful goals measure adoption, quality, and efficiency, not just activity. In other words, success is not defined by the number of tools launched or tasks completed, but by whether the organization becomes more effective, more trusted, and more efficient in how it operates. Shah sees this transparency as essential to sustaining momentum because it builds trust, and trust is what enables transformation to endure.

Why “solve and scale” requires discipline

As the conversation turns to the familiar transformation phrases “solve and scale” and “don’t boil the ocean,” Shah insists that the idea only works when leaders bring real rigor to it. For him, the phrase is often repeated casually, but its practical meaning is far more demanding: “Don’t boil the ocean doesn’t mean that the ocean is not important.”

What it means, in his view, is that trying to fix everything at once is neither realistic nor sustainable. He says he has seen organizations attempt exactly that, only to lose momentum and fail to scale anything meaningfully. Shah’s alternative is to begin with use cases, but not in an ad hoc way. He starts by establishing a standardized intake template with consistent parameters tailored to different domains, then publishes that framework to business leaders across the organization.

That structure, he says, creates discipline from the outset. It ensures that use cases are not simply interesting technical experiments but explicit problems tied to real business value.

Prioritizing enterprise value over experimentation

Once use cases are gathered, Shah prioritizes them through a transparent scoring model that evaluates factors such as business value, regulatory impact, risk reduction, and operational efficiency. The point of that model is to give leaders a common basis for comparison so they can prioritize objectively rather than by instinct or novelty: “Every use case is evaluated through the same structure.”

This is where the idea of “solve and scale” becomes real for Shah. Organizations must deliberately choose high-value, enterprise-relevant use cases that are also architecturally scalable. That way, they are not just solving one problem in isolation but building the reusable data foundation and architecture that can support broader adoption across the enterprise.

He repeatedly highlights the importance of trust here. Transformation, he argues, does not become sustainable because leaders announce it. It becomes sustainable when an organization solves one meaningful problem well, earns confidence through it, and then scales the capability outward in a repeatable way.

Trust, not novelty, is what makes AI succeed

In his closing remarks, Shah broadens the discussion from initiative design to the broader arc of AI adoption. He warns against treating AI success as a race to the next model, the next large language model, or the next platform. For him, the real determinant of success lies elsewhere.

“AI success is not about using the next model, the next LLM, or the latest AI platform. It is about building the data foundations, governance, and execution discipline that create trust and enable scale over time,” Shah concludes.

CDO magazine appreciates Deepak Shah for sharing his insights with our global community.