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IBM and ServiceNow Collaborate on Enterprise Data for AI

IBM and ServiceNow announced an expanded collaboration to address two of the biggest barriers blocking enterprise AI at scale: the AI-ready data problem and the legacy application layer. 

The collaboration aims to combine IBM’s AI, data and automation capabilities with the ServiceNow AI Platform to help enterprises break through outdated systems and put their data to work for AI. IBM and ServiceNow plan to deliver joint solutions that modernize aging systems, extend ServiceNow Workflow Data Fabric with IBM’s enterprise data capabilities, and enable autonomous IT operations so the world’s largest enterprises can unlock the transformative value of agentic AI.

“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. Together, we’re helping enterprises move from AI ambition to real, scalable outcomes.”

“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, IBM. “Together with ServiceNow, we’re building an open, flexible foundation for AI that helps enterprises move faster while maintaining control and trust.”

The collaboration integrates IBM’s software solutions with the ServiceNow AI Platform and aims to create new solutions for customers across three key areas:

  • Application modernization: Scans and refactors legacy systems using tools like IBM Bob, Enterprise Application runtime (Java) and IBM watsonx.data so enterprises will be able to bring aging applications into the AI era without starting from scratch.
  • Enterprise data governance: Extends ServiceNow Workflow Data Fabric with IBM watsonx.data to unlock key capabilities like Data Quality, Observability, Master Data Management – leveraging ServiceNow Data Catalog so that mutual customers can keep their data AI-ready.
  • Autonomous infrastructure operations: Integrates Red Hat Ansible, IBM Bob, Instana, Hashicorp Terraform, and Hashicorp Vault into ServiceNow IT workflows to detect, remediate, and resolve issues before they affect the business.

These joint solutions are expected to be available in the second half of 2026.

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IBM and ServiceNow Collaborate on Enterprise Data for AI

IBM and ServiceNow announced an expanded collaboration to address two of the biggest barriers blocking enterprise AI at scale: the AI-ready data problem and the legacy application layer. 

The collaboration aims to combine IBM’s AI, data and automation capabilities with the ServiceNow AI Platform to help enterprises break through outdated systems and put their data to work for AI. IBM and ServiceNow plan to deliver joint solutions that modernize aging systems, extend ServiceNow Workflow Data Fabric with IBM’s enterprise data capabilities, and enable autonomous IT operations so the world’s largest enterprises can unlock the transformative value of agentic AI.

“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. Together, we’re helping enterprises move from AI ambition to real, scalable outcomes.”

“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, IBM. “Together with ServiceNow, we’re building an open, flexible foundation for AI that helps enterprises move faster while maintaining control and trust.”

The collaboration integrates IBM’s software solutions with the ServiceNow AI Platform and aims to create new solutions for customers across three key areas:

  • Application modernization: Scans and refactors legacy systems using tools like IBM Bob, Enterprise Application runtime (Java) and IBM watsonx.data so enterprises will be able to bring aging applications into the AI era without starting from scratch.
  • Enterprise data governance: Extends ServiceNow Workflow Data Fabric with IBM watsonx.data to unlock key capabilities like Data Quality, Observability, Master Data Management – leveraging ServiceNow Data Catalog so that mutual customers can keep their data AI-ready.
  • Autonomous infrastructure operations: Integrates Red Hat Ansible, IBM Bob, Instana, Hashicorp Terraform, and Hashicorp Vault into ServiceNow IT workflows to detect, remediate, and resolve issues before they affect the business.

These joint solutions are expected to be available in the second half of 2026.

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For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...