IBM and ServiceNow are teaming up for new services they say will help enterprise customers bring aging legacy environments into an AI-ready infrastructure. The collaboration will combine IBM's AI, data, and automation capabilities with ServiceNow's AI platform for a variety of offerings that will modernize aging systems, enable autonomous IT operations, and help organizations evolve existing systems rather than replace them, the companies stated.

The Challenge of Legacy Systems in the AI Era

Decades of deeply interconnected legacy systems are the biggest barrier to moving fast on AI, the companies stated. Many enterprises have accumulated vast estates of mainframe computers, custom-coded applications, and complex middleware that have been patched and extended over years. These systems often contain critical business logic and data, but their rigid architectures and lack of modern interfaces make them difficult to integrate with contemporary AI platforms. The partnership aims to address this by providing a structured approach to modernizing these systems incrementally, without the risk and cost of full replacement.

According to John Aisien, senior vice president and general manager of central product management, security, and risk at ServiceNow, "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." This statement underscores the complementary nature of the two vendors: IBM's deep expertise in large-scale systems and mainframe environments, and ServiceNow's ability to orchestrate workflows and manage processes across disparate technology stacks.

Three Core Services for the Second Half of 2026

The vendors will focus on three core services that will be available in the second half of 2026. Each service targets a specific aspect of the legacy system modernization challenge:

Application Modernization

The application modernization service scans and refactors legacy systems using tools like IBM Bob, Enterprise Application runtime (Java), and IBM watsonx.data to help enterprises bring existing applications into the AI era without starting from scratch. IBM Bob is a code analysis and transformation tool that can automatically understand and refactor COBOL, PL/I, and other mainframe languages, converting them into modern Java or cloud-native microservices. By combining this with watsonx.data, which provides a unified data platform for AI workloads, enterprises can gradually modernize their most critical applications while preserving business continuity.

This service recognizes that many enterprises have decades' worth of custom business logic embedded in legacy applications. Replacing these systems entirely would be prohibitively expensive and risky. Instead, IBM and ServiceNow offer a path to incremental modernization, where the most valuable components are refactored and connected to ServiceNow's AI-powered workflows. For example, a legacy insurance claims processing system could have its core rules extracted into a microservice that triggers automated actions in ServiceNow, reducing manual effort and speeding up response times.

Autonomous Infrastructure Operations

The autonomous infrastructure operations service 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. This creates a closed-loop system where infrastructure changes are automatically validated, security policies are enforced, and operational incidents are handled without human intervention.

For instance, when a server experiences a performance anomaly, Instana's observability platform can detect the issue and automatically trigger a ServiceNow incident. The workflow then uses Terraform to spin up a replacement instance, Ansible to configure it with the correct settings, and Vault to inject secrets securely. IBM Bob can even analyze the application code to identify the root cause of the performance issue and suggest a fix. This level of automation is critical for enterprises running hybrid cloud environments with thousands of interconnected legacy and modern systems.

Data Governance

The data governance service extends ServiceNow Workflow Data Fabric with IBM watsonx.data to unlock key capabilities like Data Quality, Observability, and Master Data Management – employing the ServiceNow Data Catalog so that mutual customers can keep track of their AI-ready data. Data governance is often the weakest link in AI initiatives, as poor data quality leads to inaccurate models and unreliable outcomes. By integrating watsonx.data with ServiceNow's workflow layer, enterprises can automatically enforce data lineage, validate data quality at each step of a process, and maintain a comprehensive catalog of data assets.

This service addresses a common pain point: legacy systems often store data in inconsistent formats, with duplicate records and missing metadata. The combined solution can scan data sources, identify anomalies, and automatically create data quality rules that are enforced in ServiceNow workflows. For example, a customer master data management process could be automated to merge duplicate records from multiple legacy databases, with each change tracked and audited through the ServiceNow platform.

Historical Context: A Long-Standing Partnership

IBM and ServiceNow have a long-standing relationship, having worked together to help large enterprise customers implement everything from cloud computing, automation, and security to IT service management and observability technologies. This history of collaboration means that the two vendors have already integrated many of their products, and the new services build on proven foundations. For example, Red Hat Ansible Automation Platform has been integrated with ServiceNow for years, allowing enterprises to automate IT operations directly from the ServiceNow console.

IBM's mainframe expertise is particularly valuable. Despite predictions of its demise, the mainframe still handles the majority of the world's transactional data, including banking, insurance, and government systems. Many organizations are reluctant to rip out these systems because they are extremely reliable and handle enormous workloads. IBM has been investing heavily in making mainframes more open and easier to integrate with modern technologies, including the introduction of APIs, containerization support, and AI accelerators on the latest z16 and z17 models. The partnership with ServiceNow extends this strategy by providing a workflow and automation layer that can orchestrate actions across mainframes, distributed systems, and cloud environments.

Industry Implications and Competitive Landscape

The announcement comes at a time when enterprises are under immense pressure to adopt AI but are struggling with the practical challenges of data silos, technical debt, and a shortage of skilled talent. Competitors like Microsoft with Azure and Azure AI, Google with Vertex AI, and Amazon with AWS AI services are all vying for a piece of this market. However, IBM and ServiceNow are differentiating themselves by focusing on the specific needs of large enterprises with substantial legacy investments. While hyperscalers offer general-purpose AI tools, they often lack the deep understanding of mainframe environments and the workflow integration that ServiceNow provides.

Another key differentiator is the emphasis on agentic AI – AI systems that can autonomously perform complex tasks across multiple systems. ServiceNow's AI Platform includes capabilities for building and managing AI agents that can execute workflows, resolve incidents, and make decisions with minimal human oversight. IBM's watsonx platform, on the other hand, provides the foundation for training, deploying, and managing AI models at scale. Together, they enable enterprises to create intelligent agents that can interact with legacy systems, extract data, and trigger actions in ServiceNow, effectively bridging the gap between old and new technology.

Real-world use cases could include financial services firms that need to process loan applications using data from multiple legacy systems, healthcare providers that want to automate patient record updates across disjointed databases, and manufacturers that need to modernize their supply chain management without disrupting production. In each case, the combination of application modernization, autonomous operations, and data governance provides a holistic approach to digital transformation.

IBM has also been active in open-source communities, contributing to projects like Kubernetes, Apache Hadoop, and Jupyter. ServiceNow has similarly invested in its own platform ecosystem, with a marketplace of third-party integrations and a low-code development environment. This openness allows enterprises to customize the services to their specific needs and integrate additional tools as required.

The long-term vision of the partnership is to create a continuous improvement cycle: legacy applications are modernized into microservices that run on modern infrastructure, which is then managed autonomously, with data quality maintained at every step. As more systems are modernized, the AI agents become smarter and can handle more complex scenarios, further reducing the need for manual intervention. This incremental approach is likely to resonate with CIOs who have been burned by failed big-bang transformation projects in the past.

Overall, the IBM and ServiceNow collaboration represents a pragmatic evolution of enterprise IT, one that recognizes the reality of existing infrastructure while offering a clear path to an AI-driven future. By focusing on the three pillars of application modernization, autonomous operations, and data governance, they are addressing the core barriers that have prevented many organizations from moving forward with AI. The availability of these services in the second half of 2026 gives enterprises time to plan their migration strategies and to start small proof-of-concept projects that can demonstrate value quickly.

Source: Network World News