IBM and ServiceNow have announced a strategic collaboration designed to help enterprises bring legacy IT environments into the age of artificial intelligence. The partnership combines IBM's deep expertise in large-scale enterprise systems—particularly mainframes and legacy applications—with ServiceNow's AI-powered workflow and agent management platform. The goal is to enable organizations to modernize their decades-old interconnected systems without a complete overhaul, a move that could accelerate AI adoption across industries.
Legacy systems have long been cited as the single biggest barrier to implementing AI at scale. Many enterprises run on infrastructure built over 20 or 30 years, with custom code, proprietary databases, and tightly coupled dependencies. Replacing these systems entirely is often prohibitively expensive and risky. The IBM-ServiceNow alliance offers a middle path: instead of rip-and-replace, they provide tools to refactor, automate, and govern existing environments, making them AI-ready.
Three Core Services for AI Modernization
The companies have outlined three initial service offerings, all scheduled for availability in the second half of 2026. Each addresses a critical pain point in the journey toward AI-enabled operations.
Application Modernization
The first service focuses on scanning and refactoring legacy applications. Using IBM's proprietary tools such as IBM Bob—a code analysis and transformation engine—along with Enterprise Application runtime for Java, and the watsonx.data data management platform, enterprises can modernize applications without rewriting them from the ground up. The service identifies code that can be optimized, migrated to modern runtime environments, or containerized. This preserves business logic while enabling access to AI features like natural language processing or predictive analytics.
Autonomous Infrastructure Operations
The second offering integrates multiple automation and observability products into ServiceNow's IT workflows. Specifically, Red Hat Ansible, IBM Bob, Instana (IBM's observability platform), HashiCorp Terraform, and HashiCorp Vault are combined to create an autonomous operations layer. This system can detect anomalies, automatically remediate issues, and even resolve potential outages before they impact business services. By embedding these capabilities into ServiceNow's incident, change, and problem management workflows, IT teams can reduce downtime and free up staff for higher-value work.
Data Governance
The third service extends ServiceNow's Workflow Data Fabric with IBM's watsonx.data. It enables capabilities like data quality tracking, observability, and master data management. The integrated data catalog allows mutual customers to govern AI-ready data consistently across the enterprise. This is critical because AI models are only as good as the data they are trained on—and many legacy systems have siloed, inconsistent data. With a unified governance framework, companies can ensure their AI initiatives are built on a trustworthy foundation.
Why Legacy Systems Matter
Enterprises across sectors like banking, insurance, healthcare, and government still rely heavily on mainframes and legacy applications. These systems handle mission-critical transactions, customer records, and supply chain operations. Replacing them is not only expensive but also risky due to the intricate dependencies built up over decades. The IBM-ServiceNow approach acknowledges that reality. Instead of forcing a full migration, they offer a modernization path that leverages existing investments while adding AI capabilities."
Dr. John Aisien, senior vice president and general manager for central product management, security, and risk at ServiceNow, emphasized the challenge: "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."
Long-Standing Partnership
IBM and ServiceNow have collaborated for years on large-scale enterprise projects spanning cloud computing, automation, security, IT service management, and observability. This new initiative deepens that relationship by directly targeting the modernization of legacy environments. Both companies have extensive sales and support organizations that can help customers navigate the complexities of integrating new AI tools with old systems.
The timing is also significant. As generative AI and agentic AI become more mainstream, enterprises are looking for ways to apply these technologies to their most valuable—but often hardest-to-change—systems. The ability to inject AI into legacy workflows without requiring a full rewrite could be a game-changer for industries with heavy regulatory and compliance burdens.
Beyond the Three Services
While the three announced services form the initial offering, the companies hinted at future expansions. Possible areas include AI-driven security operations, where legacy systems often have weak security postures, and AI-assisted code generation for legacy languages like COBOL. IBM's mainframe division has been investing in AI for years, including tools that help mainframes communicate with modern cloud environments. ServiceNow, meanwhile, has been expanding its AI capabilities through acquisitions and internal development. The partnership could eventually extend to industry-specific solutions, such as for financial services or healthcare.
Another important aspect is the role of data. Legacy systems generate massive amounts of data that often goes underutilized. By using IBM's watsonx.data as an intelligent data foundation, enterprises can not only clean and tag that data but also feed it into ServiceNow's workflows for real-time decision-making. This could lead to improvements in areas like customer service, predictive maintenance, and fraud detection.
The autonomous infrastructure operations service deserves special attention. By combining IBM Bob's code analysis with Red Hat Ansible's configuration management and Instana's observability, the service promises self-healing infrastructure. For example, if a legacy database starts experiencing performance degradation, the system could automatically identify the bottleneck, apply a temporary fix, and notify administrators. This reduces reliance on manual intervention and speeds up incident resolution.
Challenges Ahead
Despite the promise, modernizing legacy systems is never straightforward. Enterprise IT teams may be skeptical of vendor promises, especially in heavily regulated industries. Governance and compliance requirements can slow down adoption of AI tools. Additionally, training staff to use new platforms requires time and investment. The IBM-ServiceNow collaboration will need to demonstrate real-world success stories and provide robust support to overcome these hurdles.
The 2026 availability date gives both companies time to refine their integration and conduct pilot programs with early adopters. Those pilots will likely focus on specific use cases like mainframe application modernisation in a bank or autonomous operations in a manufacturing environment. If successful, the partnership could set a template for how other vendors approach legacy modernization in the AI era.
Ultimately, the collaboration highlights a broader trend: that AI is not just for startups or greenfield deployments. The biggest impact may come from infusing intelligence into the existing systems that still run the global economy. IBM and ServiceNow are betting that their combined expertise can make that vision a reality.
Source: Network World News