IBM and ServiceNow have expanded their collaboration to tackle one of the most challenging aspects of enterprise artificial intelligence: transforming legacy data and systems into a usable foundation for agents, automation, and autonomous operations. The multi-year partnership will combine IBM’s AI, data, and automation capabilities with ServiceNow’s AI platform to modernize outdated applications, improve data governance, and automate infrastructure operations.
This announcement comes at a time when many large companies are no longer asking whether to use AI, but rather why integrating it into core processes is so difficult. The answer often lies less in the model itself and more in architecture: fragmented data, outdated applications, manual workflows, system dependencies, and layers of technology not designed for AI agents.
Enterprise AI clashing with decades of legacy systems
IBM and ServiceNow focus on two main barriers: the challenge of preparing data for AI and the layer of legacy applications. Companies have been building interconnected systems for decades—often critical for billing, support, operations, HR, procurement, customer service, or infrastructure management. Replacing them all at once is costly, risky, and often unfeasible.
Their collaboration aims to offer an interim approach: evolving existing systems rather than fully replacing them. This aligns with the reality of large corporations and public organizations. AI cannot be deployed at scale only on clean demos or perfectly ordered data. It must work with mainframes, old Java applications, heterogeneous databases, incomplete catalogs, ITSM processes, and already-existing automation tools.
| Business Barrier | Actual Problem | IBM and ServiceNow Approach |
|---|---|---|
| Legacy applications | Critical systems difficult to replace | Gradual modernization and refactoring |
| Unprepared data | Insufficient quality, observability, and governance | Expanding Workflow Data Fabric with watsonx.data |
| Fragmented IT operations | Siloed incidents, changes, and remediation | Integrating automation and IT workflows |
| Isolated AI models | Lack of connection with real processes | AI applied within enterprise workflows |
| Loss of control risk | Governance and trust concerns | Open, flexible base supporting multiple models |
John Aisien, an executive at ServiceNow, summarizes the challenge by noting that many companies want to deploy agentic AI, but lack the foundational infrastructure to do so at scale. Raj Datta, Head of ISV and AI Partnerships at IBM, adds an important insight: adopting AI is not just about gaining access to models—it also requires rethinking the systems, data, and governance that support them.
Three key areas: modernization, data governance, and autonomous operations
The collaboration is structured around three pillars. The first is application modernization. IBM and ServiceNow plan to use tools like IBM Bob, Enterprise Application Runtime for Java, and watsonx.data to scan, refactor, and help migrate aging applications to a more AI-ready environment.
The second pillar focuses on enterprise data governance. The goal is to extend ServiceNow’s Workflow Data Fabric with IBM’s watsonx.data capabilities—including data quality, observability, and Master Data Management—while leveraging ServiceNow Data Catalog. The aim is for joint clients to keep their data AI-ready, a task often more complex than commercial presentations suggest.
The third pillar addresses autonomous infrastructure operations. IBM will integrate tools such as Red Hat Ansible, Instana, HashiCorp Terraform, and HashiCorp Vault into ServiceNow workflows to detect, remediate, and resolve issues before they impact the business.
| Collaboration Area | Tools Mentioned | Objective |
| Application modernization | IBM Bob, Enterprise Application Runtime for Java, watsonx.data | Scan and refactor legacy systems |
| Data governance | ServiceNow Workflow Data Fabric, watsonx.data, Data Catalog | Maintain AI-ready data |
| Data quality & observability | IBM’s data capabilities | Enhance data reliability for AI |
| Master Data Management | IBM watsonx.data and related governance | Unify critical business entities |
| Autonomous operations | Ansible, Instana, Terraform, Vault, IBM Bob | Detect, remediate, resolve incidents |
| IT workflows | ServiceNow AI Platform | Link intelligence with operational execution |
Joint solutions are expected in the second half of 2026, though both companies caution that future-oriented statements may change or be withdrawn without notice.
Why ServiceNow is a strategic piece
ServiceNow increasingly positions itself as a “control tower” for enterprise AI. Its platform not only manages tickets or IT processes but also connects departmental tools, cloud applications, legacy systems, and agents. The company claims its platform executes over 85 billion workflows annually, illustrating why it aims to become the hub where AI shifts from recommendation to action.
This partnership with IBM fits into that strategy. ServiceNow is embedded in many large companies’ operational processes. IBM brings expertise in modernization, hybrid cloud, Red Hat, automation, enterprise data, and consulting. The goal is to solve a practical question: how to bring AI into real processes without dismantling the existing infrastructure entirely.
| ServiceNow asset | Role in the alliance |
| AI Platform | Orchestrate intelligence and execution within workflows |
| Workflow Data Fabric | Connect data across systems |
| Data Catalog | Provide visibility and governance over data assets |
| IT workflows | Enable automation, remediation, and tracking |
| Enterprise ecosystem | Reach core processes of large organizations |
For many companies, value won’t just come from another chatbot but from an agent that can detect anomalies, access reliable data, trigger automation, open or close incidents, execute controlled changes, and ensure traceability. That requires AI to be connected to real operational systems, not only conversational interfaces.
IBM aims for AI to integrate into legacy systems without dismantling everything
IBM has long positioned itself around hybrid cloud, Red Hat, automation, and enterprise AI. Its role in this alliance is to provide tools to work with complex systems that cannot be moved all at once. For large organizations, legacy is not just “old code” but often where core business resides.
Modernizing an old application can involve understanding dependencies, modularizing, transforming runtimes, improving observability, reorganizing data, and creating interfaces for agents to operate safely. Done poorly, AI may increase risks; done well, it can speed tasks that previously relied on manual processes or expert knowledge across teams.
| Legacy problem | Risks if ignored | Value of modernization |
| Difficult-to-understand code | Slower, costly changes | Increased adaptability |
| Data trapped in old systems | AI lacks context | Accessible data for workflows |
| Lack of observability | Late incident detection | Faster detection and remediation |
| Manual processes | High operational costs | Guided automation |
| Undocumented dependencies | Migration risks | Gradual, controlled modernization |
| Weak governance | Unreliable AI | Better traceability and trust |
The mention of IBM Bob is particularly interesting because it points to an AI strategy focused on development and modernization. IBM aims to bring code analysis and transformation capabilities to environments where system change costs are high. In this context, ServiceNow can function as an orchestration and operation layer.
Preparing data for AI requires deliberate effort
The second part of the partnership addresses a less visible but critical issue: AI-ready data. Many companies have sufficient data, but not necessarily usable data for agents or models. Data may be duplicated, outdated, misclassified, lacking clear lineage, or distributed across departments.
An enterprise agent working with inaccurate data does not scale productivity; it scales errors. That’s why data governance is once again central. Quality, observability, catalogs, and Master Data Management are not new pieces, but AI makes them more urgent. If a model must make decisions or trigger workflows, companies need to know where the data originates, whether it is current, and who can access it.
| Data capability | Why it matters for AI |
| Data quality | Reduces incorrect responses and decisions |
| Observability | Detects pipeline and source issues |
| Catalog | Enables asset discovery and contextual understanding |
| Master Data Management | Unifies key entities like customer, product, or supplier |
| Governance | Defines permissions, traceability, and authorized use |
| Data within workflows | Connects knowledge with operational actions |
Here, ServiceNow and IBM seek to bridge two often separated worlds: workflow platforms and data platforms. AI agents require both. Without data, agents lack context; without workflows, agents cannot act in a controlled manner.
Autonomous operations: fewer tickets, more remediation
The third pillar targets IT operations. Integrating Red Hat Ansible, Instana, Terraform, and Vault into ServiceNow workflows aims to enable organizations to detect, remediate, and resolve issues proactively. This evolution moves towards more autonomous operations.
Instana contributes observability. Ansible executes automation. Terraform manages infrastructure as code. Vault handles secrets. ServiceNow coordinates flows, logs actions, manages approvals, and provides context. When integrated effectively, many incidents could be resolved with less manual intervention.
| Tool | Role in Autonomous Operations |
| Instana | Monitoring and problem detection |
| Red Hat Ansible | Task automation and remediation |
| Terraform | Infrastructure as code management |
| HashiCorp Vault | Secrets and credential management |
| IBM Bob | System modernization and support |
| ServiceNow IT workflows | Orchestration, control, approvals, audit trails |
The challenge lies in balancing autonomy and control. Not all incidents should resolve automatically—some require human oversight or strict policies. True autonomous operations depend on having limits, logs, rollbacks, and governance in place.
A partnership designed for large enterprises, not demos
The IBM and ServiceNow collaboration does not seem aimed at users seeking a standalone AI assistant. Instead, it targets large organizations with critical processes, legacy systems, vast data repositories, and strict control needs. AI often encounters resistance in these contexts but also offers substantial value when integrated properly.
A key takeaway is that the future of enterprise AI will not depend solely on model quality; it will also depend on how effectively models are connected to real systems, reliable data, auditable processes, and secure automation. IBM and ServiceNow aim to occupy that vital intermediate layer between models and operations.
The expected rollout in the second half of 2026 provides time to see how these joint solutions materialize. For now, the announcement points clearly in one direction: modernize without disruption, govern data before automating, and turn workflows into the real workspace for AI-driven work.
FAQs
What have IBM and ServiceNow announced?
They have expanded a multi-year collaboration to create joint solutions that modernize legacy applications, prepare enterprise data for AI, and automate infrastructure operations.
When will these solutions be available?
IBM and ServiceNow expect joint solutions to be available in the second half of 2026.
What IBM tools will integrate with ServiceNow?
The announcement mentions IBM Bob, IBM watsonx.data, Enterprise Application Runtime for Java, Red Hat Ansible, Instana, HashiCorp Terraform, and HashiCorp Vault.
Why is this important for agentic AI?
Because enterprise agents need reliable data, modernized systems, and workflows where actions can be executed with control. Without this foundation, AI remains limited to isolated pilots.