- What makes ServiceNow AI Control Tower different from other AI governance tools
- How it works alongside Agent Fabric, Orchestrator, Studio, and Data Fabric
- Why enterprises can’t scale AI agents safely without oversight
- Which roles inside the company interact with it (and who approves)
Aviation depends on control towers because the margin of error is slim. Tracking flight paths, spotting risks, and coordinating hundreds of moving parts requires both precision and trust. Passengers may never see it, but their safety depends on it.
ServiceNow applies the same logic here with the AI Control Tower.
Just like in aviation, enterprises adopting AI agents need a controlled environment where they can watch, approve, and guide these autonomous systems. That’s exactly the role ServiceNow AI Control Tower is built to play.
What Is ServiceNow AI Control Tower?
ServiceNow AI Control Tower acts as the governance hub for enterprise AI. It:
- Tracks the AI inventory: every system, dataset, and model in use, including third-party ones.
- Manages the lifecycle: from idea submission to risk assessment, deployment, and monitoring.
- Assesses risks: bias, privacy, reputational damage, and regulatory noncompliance.
- Provides visibility: a single view of the AI landscape for IT, legal, compliance, and business stakeholders.
- Enables remediation: when issues arise—such as model drift or compliance gaps—automated workflows trigger corrective actions.
AI should never operate as a black box. Control Tower makes it observable, auditable, and measurable.
How Does It Fit with ServiceNow’s Other AI Components?
In May 2025, when our team covered Knowledge 2025 in Las Vegas, we saw ServiceNow announce an entire set of products designed to manage the full lifecycle of AI agents. Among them was the AI Control Tower, which works together with other tools that provide context, access to knowledge, and define how agents interact with employees, other agents, and even external systems.
Let’s quickly review them:
- AI Agent Fabric: the shared environment where agents operate.
- AI Agent Orchestrator: coordinates tasks across multiple agents and systems.
- AI Studio: lets teams create new agents using low-code and natural language.
- Workflow Data Fabric: provides contextual, real-time data without duplication.
Why is it different from Azure, AWS, or Google?
If you’ve worked with cloud AI platforms, you already know that Azure, AWS, and Google each have strong governance tools. Azure AI Studio, AWS Bedrock, and Google Vertex AI let you register models, monitor their performance, detect drift, and apply compliance rules. And you can also use third party models (OpenAI, Anthropic, Cohere, Mistral, and others inside those ecosystems).
The challenge is that governance stays locked inside each cloud. If your company runs models in Azure, Vertex, and Bedrock at the same time (which is increasingly common), you end up juggling multiple dashboards and policies. Each provider gives you visibility, but only for the workloads in its own environment.
ServiceNow takes a different approach here. AI Control Tower doesn’t try to be another model marketplace. Instead, it acts as an agnostic hub at the workflow level. It doesn’t matter if the underlying model runs in Azure, Google, or AWS—what Control Tower governs is how those AI agents operate inside your business processes: IT requests, HR cases, finance approvals, supply chain tasks, or customer service interactions.
That difference matters. Because in a real enterprise stack, you rarely have “one model to rule them all.” You’ll have different models solving different problems, spread across providers. The value of Control Tower is that it gives you one place to track them all, and more importantly, to connect governance with outcomes.
Increasing control slows innovation… or quite the opposite?
It’s easy to assume that adding more control could discourage innovation. Teams or individual contributors might think twice before suggesting ideas if they expect heavy compliance checks. But ServiceNow found another formula: keeping governance in place without destroying the culture of innovation or the willingness of employees to experiment. In practice, it created a balance where different teams can propose ideas—like automating process documentation or handling typical HR requests—while making sure those initiatives are safe and won’t create problems later.
Once submitted, the proposal is added to the AI inventory and reviewed by experts, often within an AI Center of Excellence. Their task is not to block initiatives but to check whether they are safe and viable. The review involves answering questions such as:
- Does the use case involve personal or sensitive data?
- Could the model introduce bias or unfair outcomes?
- How transparent are the system’s decisions?
- What regulations or internal policies would apply?
These questions help categorize the level of risk and define the safeguards required. Rather than shutting down experimentation, this process makes it possible for innovation to continue—while keeping it under control and ready to be corrected if issues appear later.
What challenges does it address?
In a webinar from late May, the ServiceNow team emphasized something we also see every day at Inclusion Cloud: AI adoption is moving faster than AI governance. And while governance frameworks like Control Tower are one way to tackle the issue, the underlying challenges are broader and very familiar to mid-sized companies.
From our work with clients, three stand out:
- Data readiness: Many teams want to adopt AI, but their data is scattered in spreadsheets, legacy systems, or siloed apps. Before an agent can deliver value, someone has to transform those Excel files or databases into structured, reliable inputs.
- Choosing and coordinating models: It’s not just about picking the “best” LLM. Often, companies end up running multiple models for different tasks, which raises the question of how to coordinate them and prevent overlap.
- Access and security: One of the biggest risks we see is models pulling in sensitive or confidential information that shouldn’t be exposed. Controlling access to training data and defining clear guardrails is critical.
These are exactly the kinds of topics we explore in our YouTube series Getting Started with AI, aimed at mid-sized companies beginning their AI journey. In those conversations, we’ve highlighted how organizations can start small—testing a use case, validating the data, and setting clear access rules—without opening the door to compliance or reputational risks.
Governance tools like ServiceNow’s Control Tower matter because they bring these elements together in one place. But at the core, the real challenge is preparing the data, coordinating the models, and ensuring that experimentation doesn’t compromise the organization’s trust or security.
Do You Need a Chief AI Officer or a Center of Excellence?
One of the first questions business leaders often ask is: who actually owns AI Control Tower inside the company?
It’s not a trivial question. Different organizations are taking different routes. Some put the responsibility on the CIO or CTO, since they already oversee core systems and IT governance. Others extend it to the Chief Digital Officer or Chief Data Officer, especially in companies where AI is seen as part of the broader digital and data strategy. And in some cases, entirely new roles are emerging: the Chief AI Officer (CAIO), dedicated to setting AI strategy, balancing risk, and reporting directly to the board. Reports suggest this role is gaining traction, especially in highly regulated industries where AI oversight needs executive weight.
Another common alternative is to form an AI Center of Excellence (CoE). Instead of a single executive, this model brings together leaders from IT, risk, compliance, legal, and business units. The CoE acts as a governance committee: reviewing new proposals, running impact assessments, and deciding what moves forward. For mid-sized companies, this often proves more practical than appointing a CAIO outright, as it distributes responsibility and brings different perspectives into the conversation.
So which is the right answer? In our view, a CoE is usually the best starting point. It creates a structured space where different leaders can bring issues to the table, encourage their teams to experiment, and collectively review the risks and opportunities. The presence of IT in this forum is critical—not just to ensure integration, but to prevent the rise of “shadow AI”, the next evolution of shadow IT, where employees start running unsanctioned models without oversight.
From there, some organizations may later evolve toward a Chief AI Officer who consolidates responsibilities at the executive level.
Final Word
Bill McDermott said it best during the Knowledge 2025 keynote: “There is no artificial intelligence without human intelligence.” That’s exactly the point of the AI Control Tower.
Many analysts agree we’re entering the so-called “disillusionment phase” of AI. But that’s not a bad thing. In fact, it signals two important shifts:
- The technology is reaching more stability and maturity.
- By knowing its limits, we can use it more safely and avoid unnecessary risks.
That’s what makes the AI Control Tower so compelling right now. Yes, it’s still early, but more companies are starting to apply AI to automate specific tasks while also giving business teams the space to propose ideas for everyday problems that could be solved faster and better with AI.
The difference now is that these experiments don’t run wild: they’re reviewed, tested, and monitored. Governance ensures sensitive data is protected, compliance is covered, and outcomes are reliable.
At Inclusion Cloud, we’re proud to be a ServiceNow Partner. If the AI Control Tower is on your roadmap, we can help you accelerate the journey with certified professionals who know how to make these tools work for your business.
Q&A: What Business Leaders Want to Know
Q: Is ServiceNow AI Control Tower only for technical teams?
No. While IT and AI experts use it daily, the real value is cross-functional. Legal, compliance, and risk teams use the same dashboards as developers and product managers. This ensures everyone works with the same source of truth.
Q: How does it help with compliance?
Control Tower maps AI systems to frameworks like GDPR, HIPAA, or the EU AI Act. It also runs impact assessments that automatically surface risks such as bias, privacy issues, or reputational damage—making it easier to prove compliance.
Q: Can it monitor third-party AI systems, not just ServiceNow?
Yes. One of its strongest features is that it can track AI systems and agents built in-house, hosted on other clouds, or purchased from third-party vendors. This gives leaders a single view across the entire AI landscape.
Q: Isn’t this just another dashboard?
Not really. Unlike monitoring tools, AI Control Tower connects governance directly with workflows. That means when an issue appears—say, a model drifting—automatic workflows can trigger remediation steps instead of just reporting the problem.
Q: What’s the business case?
Leaders gain visibility into ROI. You can see whether AI systems are actually saving costs, improving productivity, or creating risks. That shifts AI from hype to measurable business impact.
Q: Who owns AI Control Tower in the organization?
Typically, it’s managed by an AI Center of Excellence (CoE), which includes IT leaders, risk officers, compliance, and sometimes a Chief AI Officer. But business units also interact with it when proposing or reviewing AI use cases.
