Today, most enterprises are no longer debating whether to adopt AI. Generative tools sit inside service desks. Automation drives compliance workflows. Autonomous agents are embedded in business processes. The discussion has shifted from adoption to oversight — and to who is actually steering it.
“AI has become a CEO-level priority,” says Cathy Mauzaize, President – EMEA at ServiceNow. “Nearly half of CEOs are saying that if they don’t embrace AI, they risk being left behind. It’s transformative. But if you want to use AI at scale, governance must sit at leadership level.”
Compliance vs governance
The shift from execution to accountability demands a clearer definition of governance. For Mauzaize, governance must be clearly distinguished from compliance.
“Compliance is whether you respect rules — reporting to regulators, adhering to requirements,” she explains. “Governance is the framework that gives people space to innovate within boundaries. If you confuse the two, you risk slowing — or even killing — innovation.”
In 2026, that distinction is practical. The EU AI Act is operational, with enforcement underway, requiring enterprises to classify AI systems by risk, document datasets and maintain audit trails. At the same time, Gulf digital authorities have embedded expectations around transparency, traceability and data oversight into national digital strategies. Boards are no longer asking whether AI is deployed; they are asking how it is controlled.
“Governance gives you visibility and traceability,” Mauzaize says. “You need transparency across your stack. That’s why this is a leadership issue.”
Accountability does not sit with a single executive. CIOs typically own architecture, while CISOs have become central as risk and security concerns grow alongside AI’s operational footprint.
“In practice, CIOs are uniquely positioned,” she says. “And CISOs have to be in the room, because risk and security are inseparable from AI.”
This partnership has become more important as enterprises confront the real-world implications of autonomous systems. AI no longer just analyses data, it takes action. Decisions must therefore be accountable, traceable and aligned with policy.
“AI is not just a tool,” Mauzaize says. “It’s a framework. If you treat AI as a tool, you fail. If you treat it as a framework to connect data, employees and AI agents to put work into action, then it becomes both a business and technology agenda.”
One of the most visible stress points remains shadow AI — employees using unapproved tools because enterprise systems are fragmented or outdated. The productivity upside is real; so are the risks of data leakage, compliance gaps and inconsistent decision-making.
“If you don’t provide employees with modern, connected platforms, shadow AI becomes inevitable,” she says. “You reduce it by design. Provide modern tools. Embed security and regulatory controls directly into workflows. Governance must be built into the work itself.”
The AI Control Tower
This need for operational oversight is where the concept of an AI Control Tower enters the conversation. ServiceNow describes its AI Control Tower as a centralised governance layer built into its platform, connecting strategy, oversight, management and performance across enterprise AI initiatives.
Rather than monitoring isolated projects, the Control Tower provides a single environment to discover, manage and govern AI assets — including models, agents and workflows — whether native to the platform or sourced externally. It acts as a unified command centre, aligning visibility, lifecycle management and risk controls to business context.
“The world of tomorrow is made of huge amounts of data, huge amounts of AI agents, and still legacy systems,” Mauzaize says. “You need to make sure you can connect all of these and still orchestrate and operate at scale.”
The strategic value lies in bringing governance inside the workflow fabric rather than layering it on afterwards. Fragmented data weakens reliability. Unmonitored agents increase risk. Outputs that cannot be traced undermine accountability. The AI Control Tower is designed to create a single source of truth — a catalogue of AI systems, datasets and decision paths — allowing leaders to see what is deployed, how it behaves and where it intersects with core business services.
“If you don’t have the right data, there’s no AI,” Mauzaize says. “If you cannot control all the agents connecting, you’re exposed to risk. You need to transform this into action through workflows.”
In practical terms, governance is not an overhead. It is the connective tissue between policy and execution — ensuring AI delivers value without creating unmanaged exposure.
Mauzaize positions the AI Control Tower less as a feature and more as a reflection of how ServiceNow views its role in the enterprise stack. The company runs approximately 80 billion workflows annually — a scale that places AI inside structured business processes rather than on top of them.
“We’ve seen what works — and what doesn’t — at scale,” she says.
The next phase of enterprise AI, she argues, will not be defined by adding more models. It will be defined by rationalising what is already deployed.
“We’re strengthening our stack, particularly in risk and security,” Mauzaize says. “Scaling AI requires connecting data, AI, workflows and security together. That’s the foundation.”
For leadership teams, the priority is structural. Organisations that embed oversight into enterprise architecture — rather than layering it on as compliance — are better positioned to scale responsibly.
“Visibility, traceability, execution — that’s what allows you to innovate with confidence,” Mauzaize says.
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