ServiceNow and IBM have announced an expanded multi-year collaboration aimed at helping enterprises unlock their data and modernize legacy systems to support AI at scale. The partnership focuses on removing longstanding barriers that have slowed AI adoption across large organizations, particularly around fragmented data and outdated infrastructure.
Through the expanded agreement, the two companies will integrate IBM’s watsonx.data, application modernization tools, and automation capabilities with ServiceNow’s AI Platform. The goal is to improve data quality, observability, and governance while enabling more autonomous, agentic AI workflows.
A key part of the collaboration involves extending ServiceNow’s Workflow Data Fabric with IBM watsonx.data. This is designed to give organizations better control over enterprise data, ensuring it is clean, governed, and ready for AI use cases. The companies are also working together on autonomous infrastructure operations, combining tools from Red Hat, IBM, and HashiCorp to help IT teams detect and resolve issues more proactively.
The expanded capabilities are expected to become available in the second half of 2026. The move reflects a growing industry focus on data readiness as a foundational requirement for scaling AI beyond pilots, particularly as organizations look to deploy more autonomous agentic systems.
“Most enterprises have the ambition to deploy agentic AI, but lack the foundation to run it at scale,"
“IBM brings the tooling to modernize the systems and extend ServiceNow’s data capabilities. ServiceNow provides the platform to put that data to work across every workflow in the business. Together, we’re helping enterprises move from AI ambition to real, scalable outcomes.”
John Aisien, senior vice president and general manager, central product management, security & risk at ServiceNow
“AI adoption at scale requires more than access to models. It requires rethinking the systems, data and workflows that support them,”
“Together with ServiceNow, we’re building an open, flexible foundation for AI that can scale across operations and deliver real business value.”
Raj Datta, vice president of ISV and AI partnerships at IBM
Conditions Driving the Change
Legacy systems and fragmented data remain one of the biggest obstacles preventing large enterprises from scaling AI beyond isolated pilots and proofs of concept.
Many organizations continue to struggle with poor data quality, lack of observability, and inconsistent governance, which directly limits their ability to deploy reliable agentic AI systems.
Enterprises are under increasing pressure to modernize aging applications and infrastructure while simultaneously trying to adopt more autonomous AI technologies that require clean, structured, and governed data.
The shift toward agentic AI has raised the bar for data readiness, as autonomous agents need access to accurate, real-time data across multiple systems to operate effectively and safely.
Traditional IT operations are becoming too slow and reactive to support the speed and complexity of AI-driven environments, creating demand for more autonomous infrastructure management and self-healing capabilities.
Organizations are recognizing that without stronger data governance and observability layers, AI initiatives risk producing unreliable outputs, compliance issues, and limited business value at scale.
The growing volume and complexity of enterprise data, combined with the need to integrate legacy systems with modern AI platforms, has made manual data management approaches unsustainable for most large enterprises.
As companies move more workloads toward agentic systems, they are facing heightened requirements around data lineage, quality monitoring, and policy enforcement that current fragmented environments cannot easily support.
Competitive pressure is increasing for enterprises to accelerate their AI transformation, but many are being held back by the technical debt and data silos created by years of incremental modernization efforts.
The convergence of AI platform capabilities and the need for production-grade data infrastructure is pushing vendors to form deeper partnerships that combine workflow automation, data intelligence, and governance in a more unified way.
What AI Governance Looked Like Before
Before recent advancements in integrated data and workflow platforms, AI governance in most enterprises was largely disconnected from the actual systems where data lived and where decisions were executed. Data governance was typically handled through separate tools and teams, often focused on compliance and reporting rather than enabling AI use cases. As a result, organizations struggled to make their data consistently AI-ready at scale.
Legacy systems remained a major bottleneck. Many enterprises had spent years attempting to modernize applications and data infrastructure, but these efforts were usually slow, fragmented, and poorly aligned with AI initiatives. Data quality issues, lack of real-time observability, and inconsistent governance policies meant that even when AI models were developed, they often could not be deployed reliably into production environments. This created a persistent gap between AI ambition and operational reality.
Governance itself was mostly reactive. Organizations relied heavily on manual processes, periodic reviews, and policy documentation to manage data and AI risk. While frameworks existed on paper, they rarely translated into automated controls or real-time visibility across the data estate. This made it difficult to scale agentic AI systems, which require clean, governed, and continuously monitored data to operate autonomously without creating unacceptable risk.
As a result, many AI projects remained stuck in pilot phases. Enterprises could demonstrate value in controlled environments but faced significant hurdles when trying to expand these initiatives across the business due to underlying data and governance limitations.
What AI Governance Looks Like Now
The expanded collaboration between ServiceNow and IBM reflects a clear shift toward more integrated and operationally focused AI governance. Rather than treating data governance as a separate compliance function, organizations are increasingly embedding governance capabilities directly into the platforms that manage workflows and data.
This evolution emphasizes making data AI-ready as a core requirement for scaling AI. By combining workflow automation with advanced data intelligence and observability, enterprises can now apply governance controls earlier in the data lifecycle and maintain them more consistently as AI systems operate. This includes improved data quality monitoring, lineage tracking, and policy enforcement across both modern and legacy environments.
There is also a growing focus on aligning governance with the realities of agentic AI. Instead of relying solely on pre-deployment reviews and static policies, organizations are building governance into the operational layer. This allows for more dynamic oversight of how data is used by autonomous systems, with better visibility into data flows and decision paths.
Infrastructure operations are also becoming more autonomous. By integrating automation and observability tools across IT environments, companies can reduce the manual effort required to maintain reliable, governed systems that support AI workloads. This represents a move away from fragmented governance toward more unified, platform-driven approaches that connect data readiness directly to AI execution.
Our Take
AI Governance Take
This announcement highlights an important shift in how enterprises should approach AI governance. Data readiness and governance are no longer secondary concerns — they have become foundational requirements for scaling AI, especially agentic systems. Organizations that continue to treat data governance as a separate compliance exercise will continue to struggle moving AI beyond pilots.
The collaboration between ServiceNow and IBM reflects a growing recognition that effective AI governance requires tighter integration between workflow platforms, data intelligence, and modernization capabilities. Rather than relying on fragmented tools and manual processes, enterprises need platforms that can enforce data quality, observability, and governance controls in a more unified and automated way.
For governance leaders, the priority should be assessing how well current data infrastructure supports AI use cases. This means evaluating whether existing tools can deliver the level of data quality, lineage, and real-time visibility required for autonomous systems. Companies that have kept data governance siloed from their AI and automation initiatives are likely to face increasing friction as they attempt to scale.
The market is clearly moving toward platforms that combine workflow orchestration with embedded data governance. Organizations should view partnerships like this as a signal to re-evaluate whether their current stack can support governed, production-grade AI at scale — or whether they need to consolidate toward more integrated solutions. Those that make this shift earlier will be better positioned to deploy agentic AI responsibly and effectively.
