The primary Maserati was launched in 1926. The primary Ferrari was launched in 1947. And the primary Porsche was launched in 1948. And my private favourite, the primary Land Rover, additionally was launched in 1948.
What do every of those legendary vehicles have in widespread?
Every predates the mainframe and COBOL, but nobody calls them outdated. Why? As a result of they’ve regularly advanced—embracing trendy engineering, cutting-edge expertise, and innovation whereas sustaining the effectivity, efficiency, reliability, and excellence they have been constructed on. The identical is true for the mainframe.
But, regardless of a long time of steady transformation, some critics nonetheless cling to the parable that mainframes are outdated, inefficient, and unable to combine with trendy IT programs. This couldn’t be farther from the reality. IBM’s z16, launched in 2023, was constructed for AI, and the z17, as a result of launch this yr, is poised to deal with new workloads with unparalleled safety, scalability, and effectivity. COBOL, the spine of essential purposes, is as simple to make use of as any trendy programming language when paired with the best instruments.
The issue isn’t the mainframe—it’s how we’ve managed and remodeled the purposes operating on it. As a substitute of strolling away from probably the most dependable, safe, and high-performing computing platform in historical past, we should always deal with how it’s evolving to assist new workloads, AI-driven insights, and hybrid cloud integration.
A Quickly Modernizing Area
The mainframe isn’t standing nonetheless. It’s taking over extra mission-critical workloads than ever, supporting every thing from AI-powered fraud detection to high-speed monetary transactions. In reality, a whopping 72 % of the world’s compute runs on mainframes whereas the platform makes up simply 8 % of IT prices.
Mainframe transformation includes two issues. First, growth groups must harness mainframes’ computing energy, scale, and knowledge storage capabilities. Second, they want these mainframe programs to align with the automation capabilities that their cousins within the cloud have adopted, making the mainframe software program growth life cycle extra environment friendly, eliminating handbook processes, and growing the standard and velocity of legacy purposes.
DevOps workflows alone received’t get us there, however instruments are bridging the hole.
In relation to instruments, retailers want mainframe code to be managed similar to cloud or distributed purposes, enabling steady integration/steady growth pipelines, automated testing, and model management whereas sustaining compatibility with legacy environments.
Tradition and the developer expertise additionally play an necessary position in mainframe transformation. If the developer expertise for engineers is subpar, a lift to effectivity is unlikely to emerge. Eradicating handbook bottlenecks, lowering or eliminating context switching, streamlining archaic growth processes, and adopting an agile tradition are all simple methods to enhance the developer expertise.
High-quality-Tuning the Mainframe for Authorities Effectivity
Clients I discuss to usually describe three very totally different—however equally legitimate—paths for fine-tuning their mainframe technique. Some authorities companies select a slow-and-steady strategy, increasing their mainframe footprint over time as wants evolve. “Our workloads are rising as our inhabitants grows,” one CIO informed me. “We’re not shifting off the mainframe—we’re rising with it.” For these companies, there’s a pure rhythm of progress that doesn’t require radical change, simply considerate funding as utilization expands.
Others are leaning into modernization by refactoring the code itself. With the assistance of Generative AI-powered code assistants, clients are telling me they’re lastly capable of sort out decades-old purposes with confidence. These instruments clarify unfamiliar code in plain language, doc it mechanically, and recommend greatest practices for making modifications. For presidency groups with restricted entry to senior mainframe builders, this new degree of code intelligence helps bridge the abilities hole and enabling sooner, safer transformation of core purposes.
After which there are the companies doubling down—reinvesting within the mainframe by upgrading to the newest zSystems and embracing DevOps practices throughout the board. “If we will do it on the distributed aspect, we should always be capable to do it on the mainframe,” one company chief informed me. By staying present, these organizations cut back technical debt, assist trendy growth instruments, and guarantee seamless integration into their enterprise-wide DevOps workflows.
Future-Proofing the Mainframe
The builders working with mainframes are additionally enthusiastic about their future. A 2024 Forrester Report discovered that “amongst international infrastructure {hardware} decision-makers, 61% stated that their agency makes use of a mainframe. Of those who use mainframes, 54% indicated that their group would improve its use of a mainframe over the following two years.”
There’s additionally a large ecosystem of distributors constructing instruments to modernize the mainframes.
That’s the reason you see increasingly more speak about synthetic intelligence, graphical scanning, and mapping instruments to parse, map, and refactor legacy code bases and monolithic code into extra manageable belongings. AI additionally provides organizations the power to rapidly onboard new assets and get them accustomed to their code base sooner to grow to be extra productive. Builders can pinpoint needed modifications sooner, lowering planning time and accelerating updates.
These developments are promising, and I don’t doubt that they might enable authorities providers to harness the mainframe’s knowledge storage and processing energy whereas additionally adopting the agility that has been the hallmark of Silicon Valley.
