Mainframe modernization is a process enterprises undertake to update or transform their legacy mainframe applications or systems to take advantage of the agility, developer productivity, cost optimization and competitive advantages enabled by new or emerging technologies.
Approaches to modernization can vary widely in scale and scope, with some taking on a total overhaul of an existing system’s functionality and others taking a more focused approach. But the the most common mainframe modernization approach is mainframe application modernization, which focuses on updating one or more mainframe applications. In fact, the terms mainframe modernization and mainframe application modernization are often used interchangeably.
It’s worth noting that as a method of modernization, mere code translation often fails to address the complexity of the problem modernization sets out to solve. Successful mainframe modernization projects concentrate not only on code, but on system-level engineering concerns such as data architecture, runtime replacement and transaction processes.
Ultimately the right mainframe modernization solution for an enterprise is one that keeps an enterprise’s mission-critical systems competitive in a rapidly changing IT landscape, while continuing to leverage the transactional, security, resilience and performance advantages of the mainframe computing platform. According to a recent report, organizations see substantial and increasing return on investment (ROI) from their mainframe modernization projects, ranging from 288% for app modernization on a mainframe to 362% for projects that move workloads from a mainframe onto other platforms.1
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For many organizations, mainframe applications are the beating heart of their enterprise IT. They drive core business processes—such as e-commerce, transaction processing, automated decision-making, fraud detection and highly regulated workloads—that rely on the mainframe platform’s superior transaction processing, security and resiliency.
Not surprisingly, today enterprises see less opportunity in remaking the code base of these mission-critical applications, or migrating them to another computing platform. They see more opportunity—and far less risk—in focused modernization initiatives that evolve these applications to strategically expose APIs, integrate with hybrid cloud services, or embed AI or AI-driven capabilities.
A mainframe modernization strategy can include the following elements:
Hybrid cloud integration—modernizing an on-premises legacy mainframe application to interact seamlessly with hybrid cloud architectures, services and resources. This enables workload optimization—for example, moving moving some workloads to cloud infrastructure to take advantage of cloud scalability or elastic compute capacity, while keeping others on the mainframe to help ensure security or meet service level agreement (SLA) specifications.
DevOps integration—refactoring application code to leverage DevOps practices and cloud-native development architectures. DevOps integration increases business agility and pace of innovation by enabling development teams to quickly and frequently deliver and deploy new features and capabilities in response to customer or market demands, in a way that monolithic legacy application code does not.
AI integration—automating and enhancing an application’s predictive and decision-making capabilities by integrating artificial intelligence (AI) and machine learning capabilities into the application, its business logic or its data platform.
API modernization—using APIs to streamline integration with applications running on other platforms, including newer, more technologically advanced systems. This makes it easier to integrate an application with other applications and ecosystems, to expand its capabilities without coding new features, or to leverage new mainframe technology without upgrading or replacing the existing mainframe.
Infrastructure optimization—when most advantageous, re-platforming or rehosting certain applications on cloud or other more cost-effective infrastructure while maintaining or improving performance.
Organizations can mix these and other methods to achieve different goals. Suppose an organization wants more flexibility to operationalize an application’s transaction data—to make some of the data available to partner-facing applications, or to train AI models. By implementing data virtualization, the application can deliver data to other applications in real-time without providing access to the system of record. And through hybrid cloud integration, the data can be replicated securely to the cloud, for scalability and elastic compute capacity model training requires.
Here are five benefits organizations typically realize with a strong mainframe modernization approach.
Legacy application modernization helps enterprises keep pace with new technologies and helps ensure competitors won’t gain a technological edge. The introduction of AI tools into mainframe modernization projects, for example, accelerates critical areas of transformation like code refactoring, DevOps design, knowledge preservation, and quality-of-service improvements.
Mainframe modernization is an opportunity to add skills and capabilities to your employees, making them more competitive and able to contribute better to your critical operations. For example, if your applications were coded more than a decade ago, chances are your coders could use a refresh as much as their code. AI tools can also address skills gaps as developers trained on the original languages the apps were written in—like COBOL—retire.
Upgrading IT systems is a great way for businesses to take advantage of the many automation capabilities that have been introduced in recent years. Automating mundane tasks like scheduling, maintenance and data entry helps employees refocus their time and energy on more value adding activities.
Mainframe data—about customers, workflows, maintenance issues and more—offers significant potential value for use in other systems. Mainframe modernization solutions that utilize new technologies like AI and machine learning can help leverage your company’s historical data in new ways, to uncover new strategic insights.
While there are different approaches and strategies to mainframe modernization, at a fundamental level, they all involve the upgrading of existing IT systems and applications. Scope, scalability and other requirements can vary depending on business need, so it’s important for stakeholders to be in alignment on their expectations for a project, as well as its potential risks.
Any modernization initiative—regardless of scope—comes with certain risks. The complexity of upgrading technology that was installed 10, 20, even 30 years ago is simply too great to eliminate risk entirely. Here are some of the challenges a mainframe modernization project typically faces.
While these risks can’t be eliminated altogether, these widely used approaches to mainframe modernization can lower them considerably while still allowing organizations to reach their modernization goals.
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1 Kyndryl’s 2025 State of Mainframe Modernization Survey Report, Kyndryl 2025