IBM and ServiceNow said that they are expanding their collaboration to tackle one of the biggest problems slowing enterprise AI: data locked inside legacy systems and hard-to-use application layers. The companies said the goal is to help large organizations modernize older systems, connect enterprise data to AI, and support autonomous IT operations at scale.
The two companies said they want to combine IBM’s AI, data and automation tools with the ServiceNow AI Platform to help enterprises bring data into AI workflows without replacing every legacy system first.
The extended collaboration is designed to address two barriers they see as central to enterprise AI: the “AI-ready data problem” and the legacy application layer. Their position is that many companies want agentic AI, but cannot scale it because their systems are fragmented and their data is not prepared for AI use.
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Data sits across old applications, departmental tools, cloud systems and manual workflows, which makes it hard for AI models to use it cleanly. IBM and ServiceNow said they plan to help customers modernize aging systems, extend ServiceNow Workflow Data Fabric with IBM enterprise data capabilities, and enable autonomous IT operations that can detect and resolve problems before they spread.
The companies want to let enterprises run AI on the model of their choice while keeping control and trust in place. Many CIOs now want AI adoption without opening up new governance or security gaps.
IBM’s Raj Datta said AI at scale requires more than model access; it requires rethinking the systems, data and governance around them.
John Aisien, general manager and senior vice president, central product management, at ServiceNow, said most enterprises want agentic AI, but do not yet have the foundation to run it at scale.
The three areas the partnership is focused on
IBM and ServiceNow said the collaboration will initially focus on three areas. The first is application modernization, where tools such as IBM Bob, Enterprise Application Runtime for Java and IBM watsonx.data will be used to scan and refactor older systems so they can support AI-era workloads.
The second is enterprise data governance. Here, the companies plan to extend ServiceNow Workflow Data Fabric with IBM watsonx.data and related capabilities such as data quality, observability and master data management, with ServiceNow Data Catalog helping keep data AI-ready.
The third is autonomous infrastructure operations. IBM said the joint work will connect Red Hat Ansible, IBM Bob, Instana, HashiCorp Terraform and HashiCorp Vault into ServiceNow IT workflows so issues can be detected and remediated before they affect the business.
One useful data point from the announcement is ServiceNow’s own platform scale: the company said more than 85 billion workflows run on its platform each year. That matters because it shows the collaboration is being built around a system already used for large-scale enterprise operations, not a niche pilot environment.
IBM’s products and consulting reach clients in more than 175 countries, which gives the partnership a broad enterprise base if the joint solutions gain traction.
The partnership is meant to reduce the amount of manual work needed to make AI useful inside large organizations. If it works, companies should be able to move from scattered data and disconnected systems to AI-ready workflows that are easier to govern and run.
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The partnership also lines up with IBM’s recent enterprise AI messaging. In the same newsroom, IBM recently said CIOs and CTOs face a growing AI control gap as deployment scales, which reinforces the idea that control and governance are becoming central issues as AI expands across the enterprise.
