Why mainframes remain central to enterprise transformation

Mainframes continue to underpin a vast share of the global economy, handling roughly 70% of the world’s core transactions.

The idea that AI-powered code translation will enable enterprises to modernize the platform or even move away from these systems seemed an innovative approach—but it’s fundamentally flawed.

Translating code is not the same as modernizing a business-critical platform and conflating the two risks underestimating their complexity and destabilizing the systems businesses still depend on most.

Rather than rendering the mainframe obsolete, AI, cyber resilience, and hybrid cloud are fundamentally changing how it fits into the enterprise.

The digital backbone quietly powering critical industries

These systems sit at the heart of the world’s most critical industries – from banking and insurance to government and transportation – where performance, security, and resilience are non-negotiable.

Far from being relics of a past era, they function as foundational IT infrastructure: deeply embedded, continuously operating, and shaped by decades of accumulated business logic that cannot simply be rewritten without risk of disruption.

This is why, according to IBM’s Institute for Business Value, 75% of organizations still expect mainframe applications to remain central to their digital transformation, while 60% see them as essential to enabling AI.

Therefore, it’s about evolving what already works at global scale.

What is often overlooked is how significantly the platform has already changed. Today’s mainframe is no longer an isolated environment, but an integrated part of modern enterprise architecture – one that connects seamlessly with cloud platforms, AI and supports developers working across systems with consistent tools and practices.

Ultimately, driving innovation into the transactional world.

Where transformation is really happening

Modernization is not a question of programming languages, but of resilience, efficiency, and future readiness. As such, it has moved beyond the CIO agenda and into the wider C-suite conversation.

At the center of this shift is a fundamental rethink of how organizations work with their most valuable asset: data.

Rather than extracting data from the mainframe, many enterprises are bringing applications and innovation closer to where that data already resides. This “data proximity” model allows teams to work directly with accurate, real-time information, without duplicating datasets or introducing additional risk, cost, and complexity.

At the same time, modern integration approaches ensure that sensitive data remains tightly governed and controlled – accessible where needed, but never indiscriminately exposed across environments. In an era of increasing regulatory scrutiny, this balance between accessibility and control is becoming a decisive advantage.

AI isn’t replacing core systems – it’s extending and embracing them

AI is becoming a powerful accelerator of this transformation, helping organizations unlock more value from their core systems. It enables faster decisions, real-time insights, and new digital services built on trusted enterprise data.

A good example comes from the banking sector, where a leading player’s modernization efforts are helping support advanced fraud detection capabilities by analysing 100% of transactions in real time. AI is integrated into the core platform, improving performance and decision-making where the data lives, while providing a robust foundation to scale workloads, productivity, and innovation.

It is also reshaping the developer and engineering experience. Tasks that once required deep, specialized knowledge – such as working within COBOL-based applications – are becoming more accessible. This enables a new generation of developers to work with mainframe systems as naturally as they do with other enterprise platforms.

Crucially, organizations are combining the core strengths of the mainframe with a diverse set of AI models including smaller, more efficient models trained on their own enterprise data. The ability to choose the right model for each use case delivers a more secure, scalable, and effective experience.

Security moves to the boardroom

As cyber threats, operational risks, and regulatory pressures intensify, resilience is no longer just an IT concern, it is a board-level priority.

Against that backdrop, mainframes remain central to enterprise security strategies. They underpin encryption, secure transaction processing, ransomware resilience, and regulatory compliance, providing a trusted foundation for critical operations.

Increasingly, they are also being used to prepare for emerging threats: For example, incorporating digital certificates to better handle access in an agentic world, anticipating possible fraud in instant payments, and getting ready for a post-quantum security landscape, where sensitive data intercepted today could be decrypted in the future as computing capabilities advance.

Hybrid cloud reshapes the role of the mainframe

The mainframe no longer operates in isolation, but as part of a hybrid cloud architecture.

Through APIs and modern integration, organizations are enabling mainframes to participate fully in enterprise-wide digital ecosystems – combining the flexibility of cloud environments with the reliability, performance, and governance required for mission-critical workloads.

This is not about migration for its own sake, but about interoperability and architectural balance. For example, one of Europe’s largest financial technology providers is using hybrid cloud capabilities within their own data centers to support hundreds of financial institutions, strengthening resilience, digital sovereignty, and scalability at national scale.

This reflects how critical systems are being designed so that connectivity and control are no longer opposing goals.

Modernization with intent

The mainframe continues to evolve alongside new environments and operational demands, extending its role beyond the traditional back office and allowing broader workload consolidation.

In the transport sector, for example, infrastructure modernization has improved a logistics hub’s operational performance while reducing CO₂ emissions by hundreds of metric tons, demonstrating how established systems can support both efficiency and sustainability goals.

Taken together, these developments challenge the idea that modernization means replacement. In practice, it is about keeping critical systems relevant as business needs change.

That matters because modernization is not a one-off program. Instead, it is an ongoing discipline shaped by operational priorities and supported by technology evolution.

The organizations doing this best are not focused on exit strategies. They focus on evolution – preserving resilience at the core while enabling innovation around it. Because when the digital core is trusted and stable, everything built on top of it can move faster.

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