Most organizations approach operational AI as a tooling decision: select a model, connect some systems, launch a pilot.
TCWGlobal is approaching it differently.
As an employer of record (EOR), TCWGlobal operates in a PII-heavy environment where trust, governance, and accuracy are not optional. For them, operationalizing AI isn’t just about capability—it’s about readiness. And readiness starts with mindset.
Leadership sets the guardrails
TCWGlobal treated AI adoption as a leadership and change management effort first—not a tool rollout. Before expanding AI into operational workflows, they focused on making AI visible, building fluency, and addressing concerns directly rather than letting uncertainty fill the gap. They measured employee sentiment, used that feedback to shape training and communication, and set clear expectations for what it means to be an AI-capable employee in 2026.
As CEO Samer Khouli puts it:
"AI is going to redefine what great service looks like in our industry—and we're not in a moment where you can afford to wait and see. The organizations that lead in five years are the ones building AI fluency into their culture right now, not as a feature but as a foundation. That means investing in your people and in the right technology infrastructure to support them—because neither works without the other. That's how you build the kind of organization that can use AI thoughtfully, at scale, without losing what makes you trusted. That's the bet we're making at TCW."
That philosophy took shape as AIoRAMA—a monthlong internal campaign built around three pillars: mindset, creativity, and impact. Rather than a one-time training event, it combined structured sessions on the human side of AI adoption with hands-on challenges designed to push employees further into real application. Sessions tackled questions employees actually had: What makes human contributions irreplaceable as AI grows? What hesitancies are worth taking seriously? How do you embed AI into your work without losing your own judgment and touch? The challenges moved from foundational (building a GPT, creating a workflow automation) to more ambitious (building agents outside of ChatGPT, developing an app or website with AI assistance). The goal was to make AI concrete, personal, and actionable—not abstract or threatening.
Operational AI: From guardrails to the technical backbone
Leadership may set the tone—but operational reality shapes the design.
TCWGlobal's Apollo team (automation engineering and data analytics) is where abstract AI strategy meets day-to-day friction. Halle Davis and Jill Arldt are the ones who feel it first—when business teams can't answer a customer question from existing reports, it lands with them. They're the translators: taking a business question, identifying which systems hold the relevant data, and building the path to an answer.
TCWGlobal values proactive service—giving customers resources and insight before they have to ask. That standard requires fast, accurate access to data across a payroll system, a CRM, and an accounting system that have historically operated in silos. Answering even a simple client question often means pulling context from multiple places, and despite a library of 1,000+ reports, new edge cases keep surfacing. Every time they do, the request routes back to Apollo.
As Halle Davis, who leads data analytics at TCWGlobal, describes it:
"We don't lack data. What we're building is a faster, more direct path between the business question and the answer—without creating new risk in a PII-heavy environment."
That's exactly the problem CData Connect AI is built to solve. TCWGlobal has selected it as the foundation for a governed, natural-language access layer that lets internal teams ask questions more directly—without bypassing security or exposing sensitive EOR data. The architecture prioritizes real-time access across operational systems, no unnecessary data replication, role-based visibility, and guardrails for how AI interacts with live data. The goal isn't just faster answers. It's a data foundation that can support AI at scale—without introducing new compliance risk in an environment where that risk is never theoretical.
What this signals
AI-forward organizations aren't defined by how quickly they deploy a chatbot. They're defined by how intentionally they prepare their people, structure their governance, and connect their systems.
TCWGlobal is building that foundation deliberately—and that's what operationalizing AI actually looks like.
Stay tuned: we’ll share more on TCWGlobal’s specific use case in the coming months, once their solution goes live.
