Phani Chegondi
AI was initially confined to innovation labs, experimental pilots, and narrow departmental use cases. But in just a few years, it’s evolved into a core enterprise capability, powering everything from IT service delivery to customer engagement. That scale brings opportunity—but also complexity.
And now, across industries, enterprise leaders are realizing something:
Without governance, AI can’t scale responsibly.
Enterprises don’t just need AI that performs. They need AI they can trust—and that starts with control.
The Hidden Challenge of Enterprise AI: Fragmentation
As organizations build out AI capabilities, many find themselves facing the same core issue: no central view of what's running, where, and how it’s performing. Models are deployed by different teams, often without consistent oversight, lifecycle tracking, or defined ownership.
This creates several challenges:
- No unified inventory of AI assets across business units
- Compliance teams operating without visibility into how models make decisions
- Difficulty tracing model performance back to business outcomes
- Delayed response to model drift, bias, or failure
These risks aren’t theoretical. According to IBM’s Global AI Adoption Index 2023, 74% of companies lack a formal governance model, despite accelerating AI investments. It’s a growing gap between adoption and accountability—and that’s where platforms like ServiceNow AI Control Tower step in.
Introducing ServiceNow AI Control Tower
ServiceNow AI Control Tower is designed to help enterprises govern AI like they govern any other business-critical capability: with structure, visibility, and cross-functional accountability.
What makes it different is its integration into the Now Platform—connecting governance directly to workflows, data sources, and operations already in place.
Key capabilities include:
- Unified visibility: Track AI models, performance metrics, and ownership in a single console
- Embedded compliance: Apply policies, controls, and audit frameworks without disrupting innovation
- Lifecycle governance: Manage models from creation through retirement with versioning and documentation
- Performance monitoring: Link model activity to outcomes, identify drift, and flag risks in real-time
- Cross-team orchestration: Align data, risk, and business stakeholders with a shared operational view
This isn’t a bolt-on compliance tool—it’s a governance layer that scales with the business.
Making Governance Practical with Cprime|INRY
AI governance isn't just a technology challenge. It’s an organizational one. Successful implementation depends on more than enabling a platform—it requires a partner that understands the nuances of enterprise transformation and change management.
At Cprime|INRY, we approach ServiceNow AI Control Tower implementations with a focus on adoption, agility, and alignment.
We help organizations:
- Assess existing AI initiatives and identify governance gaps
- Design realistic operating models, tailored to their industry and risk landscape
- Deploy iteratively, using our PASS (Process Area Specific Sprints) methodology to generate quick wins
- Enable cross-functional collaboration between IT, risk, compliance, and business users
- Support continuous improvement through post-go-live optimization and managed services
Our goal is to make AI governance stick—not just as a platform feature, but as a capability embedded into the way teams work.
What the Implementation Journey Looks Like
While every organization’s path to governance is unique, the journey typically follows four foundational phases:
- Discovery and Assessment
Map all existing AI assets, use cases, and oversight mechanisms. Clarify the current state and define what “governed AI” should look like in your organization. - Governance Design
Configure the Control Tower with appropriate policies, controls, and role-based access. Establish escalation paths and risk thresholds. - Deployment and Enablement
Roll out capabilities iteratively, starting with high-impact models. Train stakeholders, build accountability, and embed governance into existing workflows. - Optimization and Scale
Use insights from the Control Tower to refine policies, improve model performance, and expand governance across new AI use cases.
Each phase is tailored to the organization’s maturity level—whether they’re early in their AI journey or managing dozens of models across business units.
Why Now?
AI regulation is no longer a distant threat. It’s already here—and expanding. From the EU AI Act to evolving data privacy laws and sector-specific guidelines, organizations are under increasing pressure to ensure their AI is explainable, auditable, and fair.
But regulation isn’t the only driver. Boards want accountability. Customers expect transparency. Internal teams need clarity when models impact critical workflows. The Control Tower isn’t just a response to external pressure—it’s a way to build confidence, internally and externally, in the way AI is used.
Our Role in Building Sustainable AI Governance
Our clients aren’t looking for tools—they’re looking for answers to real challenges. They want to know:
- Where is AI operating in my business today?
- Who is accountable when something goes wrong?
- How do we scale AI safely without slowing down innovation?
We help answer those questions, then build the infrastructure to support the answers. With a proven record in ServiceNow delivery, cross-industry experience, and a practical approach to governance, we help enterprises build responsible, scalable AI systems—faster.
The Bottom Line
Enterprise AI isn’t just a collection of models—it’s an ecosystem. And like any ecosystem, it needs structure. ServiceNow AI Control Tower brings that structure. Cprime|INRY helps make it operational.
In a world where AI is reshaping how work gets done, governance is no longer optional—it’s strategic. The organizations that move first to embed it into their operations will be the ones that lead with confidence.