IBM and ServiceNow on June 11 announced a multi-year collaboration to help enterprises modernize legacy applications, improve AI-ready data governance, and bring autonomous operations into IT workflows. The partnership targets two barriers IBM and ServiceNow say continue to block enterprise AI at scale: the AI-ready data problem and the legacy application layer.
The planned joint solutions will combine IBM’s AI, data, and automation capabilities with the ServiceNow AI Platform. IBM and ServiceNow said the work will focus on modernizing aging systems, extending ServiceNow Workflow Data Fabric with IBM enterprise data capabilities, and enabling autonomous IT operations.
The solutions are expected to become available in the second half of 2026.
Legacy Modernization into the AI Foundation
IBM and ServiceNow are framing legacy modernization as a precondition for enterprise AI.
Decades of interconnected legacy systems remain a major barrier to moving quickly on AI. Rather than positioning the collaboration around wholesale replacement, IBM and ServiceNow said the goal is to help organizations evolve existing systems, run AI on the models they choose, and unlock more of their enterprise data.
“Most enterprises have the ambition to deploy agentic AI, but lack the foundation to run it at scale,” said John Aisien, General Manager and Senior Vice President, Central Product Management, at ServiceNow. “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.”
The application modernization component will use IBM Bob, Enterprise Application Runtime for Java, and IBM watsonx.data to scan and refactor legacy systems. The goal is to bring aging applications into the AI era without requiring enterprises to start from scratch.
Data Governance and the Workflow Layer
The collaboration also extends ServiceNow Workflow Data Fabric with IBM watsonx.data.
That part of the partnership is designed to support data quality, observability, and master data management through ServiceNow Data Catalog. IBM and ServiceNow said the aim is to help mutual customers keep data AI-ready as it moves into enterprise workflows.
“AI adoption at scale requires more than access to models. It requires rethinking the systems, data, and governance that support them,” said Raj Datta, General Manager of ISV and AI Partnerships at IBM.
The data governance component is significant because enterprise AI depends on more than data access. AI agents and copilots need trusted business context, governed data definitions, observable data quality, and controls over how information flows into workflow decisions.
For ServiceNow, the partnership strengthens its positioning as an orchestration layer for work, data, and AI agents. For IBM, it extends watsonx.data and related data capabilities into the ServiceNow workflow environment.
Autonomous IT Operations: Execution Layer
The third part of the collaboration focuses on autonomous infrastructure operations.
IBM and ServiceNow plan to integrate Red Hat Ansible, IBM Bob, Instana, HashiCorp Terraform, and HashiCorp Vault into ServiceNow IT workflows. The goal is to detect, remediate, and resolve issues before they affect the business.
That approach connects several operational layers like infrastructure automation, observability, provisioning, secrets management, and workflow orchestration. The broader aim is to move AI-enabled operations beyond alerting and into coordinated remediation.
That is where AI scale becomes practical. Modernized applications and AI-ready data can surface the right signals, but workflow integration determines whether those signals turn into action across IT operations, business services, and enterprise processes.
The announcement builds on an existing IBM-ServiceNow relationship around AI and workflow automation. The new collaboration moves the focus deeper into the foundation layer: legacy systems, governed enterprise data, and IT operations automation.
What This Means for ERP Insiders
Legacy modernization sits inside the enterprise AI agenda. IBM and ServiceNow are not presenting AI at scale as a model-selection problem; they are tying it to aging applications, Java modernization, and the ability to evolve existing systems without starting over. AI readiness depends on the condition of the core application estate as much as the AI platform layered above it.
Data governance becomes a workflow requirement. Extending ServiceNow Workflow Data Fabric with IBM watsonx.data connects data quality, observability, master data management, and cataloging to the workflows where AI outputs are used. Governed data must sit close to execution if AI agents are expected to act inside business processes.
Autonomous IT needs an integrated operations stack. The planned integration of Ansible, Instana, Terraform, Vault, IBM Bob, and ServiceNow workflows shows how AI-enabled operations require automation, observability, infrastructure provisioning, and security controls to work together. AI-enabled business operations will depend on whether IT can detect and resolve infrastructure issues before they affect critical enterprise workflows.
