Enterprise AI Data Readiness: From Ambition to Execution
Enterprise AI data readiness is the state in which an organization’s systems, infrastructure, and governance policies are modern enough, connected enough, and controlled enough to support reliable, large-scale AI deployment across critical business workflows. For many enterprises, that state still feels distant. Decades of tightly coupled legacy applications, siloed databases, and inconsistent data definitions make it hard to move beyond small pilots. The result is a gap between board-level AI ambition and what production systems can safely support. Instead of focusing only on models, enterprises are starting to confront the foundations: legacy system modernization, enterprise data governance, and hybrid cloud AI deployment. Recent partnerships between IBM and ServiceNow on one side, and Unisys and Rafay Systems on the other, show how major vendors are targeting these structural barriers to unlock AI at scale.
IBM and ServiceNow: Legacy System Modernization as AI’s Launchpad
IBM and ServiceNow are framing legacy system modernization as a precondition for enterprise AI data readiness. Their multi-year collaboration aims to scan and refactor aging applications using tools such as IBM Bob, Enterprise Application Runtime for Java, and IBM watsonx.data, then connect those modernized systems to the ServiceNow AI Platform. Instead of forcing wholesale replacement, the intent is to evolve existing systems so enterprises can run AI on any model while keeping critical workflows intact. As John Aisien of ServiceNow notes, “Most enterprises have the ambition to deploy agentic AI, but lack the foundation to run it at scale.” By extending ServiceNow’s Workflow Data Fabric with IBM’s enterprise data capabilities, the partners want to turn legacy estates into an AI-ready foundation rather than a constraint.
AI Governance at Scale: Data Fabric Meets Workflow Control
The IBM–ServiceNow collaboration also focuses on AI governance at scale by pairing IBM watsonx.data with ServiceNow’s Workflow Data Fabric and Data Catalog. This joint approach targets data quality, observability, and master data management so that AI agents, copilots, and autonomous workflows operate on trusted information. Raj Datta of IBM states that “AI adoption at scale requires more than access to models. It requires rethinking the systems, data and governance that support them.” In practice, this means defining shared data semantics, enforcing consistent controls as data flows through workflows, and instrumenting quality checks across the lifecycle. For enterprises, especially those with complex compliance needs, the outcome is not only better models but also traceable decisions and auditable AI behavior embedded in IT operations, service management, and broader business processes.
Unisys and Rafay: Orchestrating Hybrid Cloud AI Deployment
While IBM and ServiceNow address application and data layers, Unisys and Rafay Systems focus on the infrastructure orchestration needed for hybrid cloud AI deployment. Their partnership combines Unisys’ AI expertise and managed cloud services with Rafay’s self-service platform to provide a unified AI software layer that spans agents, models, and modular AI infrastructure. This is aimed at enterprises where only 36% say they are ready to support large-scale AI workloads. The offering supports AI and GPU-intensive workloads across public, private, and hybrid environments, embedding governed, self-service capabilities directly into broader cloud and application operations. By simplifying deployment, lifecycle management, and governance, the collaboration is designed to move clients beyond proof-of-concept and into production, while maintaining consistency, security, and control across on-premises, edge, and cloud locations.
Why Regulated and Hybrid Environments Need Specialized AI Partnerships
Taken together, these partnerships signal a shift in enterprise AI strategy: from fixating on models to solving foundational data, governance, and infrastructure problems. Regulated industries and organizations with large hybrid estates face strict compliance rules, sensitive data, and fragmented infrastructure, making AI governance at scale and reliable orchestration non-negotiable. IBM and ServiceNow are positioning ServiceNow as an AI control tower and workflow layer, with IBM providing modernization and data engines underneath. Unisys and Rafay, meanwhile, are building a governed infrastructure fabric that allows AI workloads to move consistently across on-premises, edge, and cloud. These efforts address the same structural blockers—legacy system modernization and enterprise data governance—but from different layers of the stack. The emerging pattern is clear: scalable AI in the enterprise will depend less on a single model and more on coordinated, strategic partnerships.
