
What happened
A 15-customer study by TheCUBE Research and Qualitate found all 15 recognized ServiceNow's workflow advantage, but 14 described runtime enforcement as external, custom or unproven, and 12 flagged a model-specific evidence gap.
Why it matters
Customers may treat Control Tower as an accountability and workflow layer rather than a complete technical governance plane, keeping specialist tools for model evidence and live enforcement.
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ServiceNow's pitch for AI Control Tower leans on an installed base built over years of ITSM, CMDB and risk workflows. The company says its platform supports roughly 100 billion workflows and nearly 8 trillion transactions, and Amit Zavery said more than 500 customers were live with AI Control Tower in the first six months. That context is the foundation of the right-to-win argument: ServiceNow already knows how work moves through an enterprise.
The field study tested whether that context translates into technical control. It found a sharp split. Governance workflow—inventory, ownership, approvals and audit history—is where ServiceNow draws consistent praise. But when it comes to model-specific evidence such as validation records, lineage and drift history, customers often generate those artifacts elsewhere, including IBM, hyperscaler-native tooling and specialists like Credo AI. Runtime enforcement is similarly fragmented, with Palo Alto Prisma AIRS and Cisco AI Defense cited as examples.
The commercial picture is mixed in a related way. ServiceNow replaced its legacy tiers with Foundation, Advanced and Prime, and management reports price uplifts of roughly 20% to 30% in the lower AI tiers and more than 30% in Prime. Customers see the value, but the study suggests unpredictability around total operating cost is a friction point that could shape how broadly Control Tower is deployed.
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