Editor’s Note: Mainframes are often spoken of in extremes, either hailed as indestructible workhorses powering critical infrastructure or dismissed as outdated relics of a bygone era. But for Seshadri Nathan Sundaram (Sesha), Senior Partner at McLaren Strategic Solutions, the story is far more nuanced. He began his career working directly with COBOL and has grown alongside every major tech shift. His perspective is grounded in hands-on experience and shaped by continuous transformation. This article captures his view on where mainframe modernization stands today, and how AI is turning long-standing obstacles into solvable challenges.
What started as room-sized machines powering Cold War-era defence and payroll now quietly supports modern banking, healthcare, and government. The history of Mainframes has been evolving, but so have future expectations. Today, they’re expected to be as agile and integrable as cloud-native platforms, demanding a smarter approach to modernization.
“In my 40 years in software engineering and enterprise technology, I’ve seen this evolution firsthand. I began my career in the early 1980s, writing COBOL and managing mainframe-based systems. I’ve since moved through nearly every wave of enterprise technology—from client-server architecture and object-oriented programming to today’s cloud-native and AI-driven platforms.”
“The mainframe has remained an essential foundation that businesses continue to depend on, even as modernization becomes increasingly urgent.”
“Mainframes are engineered for reliability, handling tens of thousands of transactions per second with 99.999% uptime. These systems were designed when failure wasn’t an option—a principle that still holds today.”
That makes them ideal for:
But in an era that demands agility, seamless integration, and cloud interoperability, mainframes can also become bottlenecks if not evolved.
“When I started in technology, I worked hands-on with COBOL and network databases like IDMS. At the time, these systems were the gold standard. I didn’t realize then that I was stepping into a computing tradition that would remain deeply embedded in global business for decades to come.”
“Over time, I transitioned into web development, Java, and C#, then into leadership roles overseeing teams across architectures and industries. As I moved forward, mainframes never disappeared; they remained essential to the organizations I supported.”
“Each year brought new strategies to retire mainframes, but little changed. Why? Because modernization is hard. And for most, it’s safer to defer the risk.
“It wasn’t until the past few years, with the emergence of AI-powered tools, that modernization started to feel truly achievable. Not just at the code level, but at the business logic level where it matters.”
Mainframes often contain millions of lines of COBOL code layered with decades of business logic, data dependencies, and undocumented behaviours. Replacing them isn’t just a technical challenge, but an operational risk. Downtime can compromise customer trust, compliance, and revenue.
“A poorly executed transition can miss critical business rules buried deep within legacy code. That’s why many organizations continue to delay modernization or take a cautious, incremental approach.”
“One of the most common pitfalls is assuming that mainframe modernization means translating COBOL into Java line by line. This ‘lift-and-shift’ strategy simply moves the complexity without solving the problem. It results in modern syntax with legacy structure, still disconnected from today’s architectural standards.”
True modernization requires a deep understanding of what the system does, how it supports business operations, and how its logic can be restructured into modular, flexible, and testable components.
Artificial Intelligence is transforming the modernization landscape. AI tools can scan millions of lines of code, identify functional groupings, and semantically understand business rules and data flows.
“At McLaren Strategic Solutions, we use AI to inspect legacy systems, identify dead code, capture runtime behaviour, and generate clean, maintainable code that reflects real-world use. AI doesn’t just automate translation. It enables transformation by understanding intent, not just syntax.”
To bring consistency and accuracy to modernization efforts, McLaren uses the IMAGE Framework, a five-phase methodology that leverages AI agents:
“Unlike conventional approaches, IMAGE ensures that legacy business logic is preserved, tested, and improved—not just rewritten.”
“Throughout my career, I’ve worked on both mainframe and modern systems. I began with COBOL and network databases, then moved to C++, Java, Python, and cloud platforms. I’ve led teams through every stage of software delivery, from architecture to development to testing to production support.”
“This dual perspective has shown me the value in bridging generations of technology. The future isn’t about abandoning mainframes. It’s about evolving- slow incremental migration while maintaining all the strengths of the mainframe.”
Mainframes aren’t just systems; they’re institutional knowledge, operational backbones, and decades of business logic in code. But transformation doesn’t have to mean disruption. As Sesha shows, modernization is most effective when it builds on what’s already strong, preserving the core while enabling agility.
The future for businesses handling intricate legacy infrastructures is gradual development rather than hurried innovation. AI, structured frameworks like IMAGE, and the wisdom of lived experience make mainframe modernisation not simply feasible but sustainable.
About the Author: Seshadri Nathan Sundaram, Senior Partner at McLaren Strategic Solutions, is a technology leader with 40+ years of global BFSI experience, specializing in legacy and modern platforms (COBOL, Java, .NET, data engineering). He is an expert in application architecture, product engineering, testing, automation, and emerging technologies (AI/ML, LLMs), with a strong focus on capability building, innovation, tech governance, and executive mentoring.
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