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 frequent?
Every predates the mainframe and COBOL, but nobody calls them outdated. Why? As a result of they’ve regularly developed—embracing fashionable engineering, cutting-edge expertise, and innovation whereas sustaining the effectivity, efficiency, reliability, and excellence they had been constructed on. The identical is true for the mainframe.
But, regardless of many years of steady transformation, some critics nonetheless cling to the parable that mainframes are outdated, inefficient, and unable to combine with fashionable IT programs. This couldn’t be farther from the reality. IBM’s z16, launched in 2023, was constructed for AI, and the z17, attributable to launch this yr, is poised to deal with new workloads with unparalleled safety, scalability, and effectivity. COBOL, the spine of vital functions, is as straightforward to make use of as any fashionable programming language when paired with the proper instruments.
The issue isn’t the mainframe—it’s how we’ve managed and reworked the functions operating on it. As a substitute of strolling away from essentially the most dependable, safe, and high-performing computing platform in historical past, we must always concentrate on how it’s evolving to assist new workloads, AI-driven insights, and hybrid cloud integration.
A Quickly Modernizing House
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 truth, a whopping 72 % of the world’s compute runs on mainframes whereas the platform makes up simply 8 % of IT prices.
Mainframe transformation entails two issues. First, growth groups have to harness mainframes’ computing energy, scale, and information 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 guide processes, and growing the standard and velocity of legacy functions.
DevOps workflows alone gained’t get us there, however instruments are bridging the hole.
On the subject of instruments, outlets want mainframe code to be managed similar to cloud or distributed functions, 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 essential function in mainframe transformation. If the developer expertise for engineers is subpar, a lift to effectivity is unlikely to emerge. Eradicating guide bottlenecks, decreasing or eliminating context switching, streamlining archaic growth processes, and adopting an agile tradition are all straightforward methods to enhance the developer expertise.
High quality-Tuning the Mainframe for Authorities Effectivity
Clients I speak to usually describe three very totally different—however equally legitimate—paths for fine-tuning their mainframe technique. Some authorities businesses 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 advised me. “We’re not transferring off the mainframe—we’re rising with it.” For these businesses, 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, prospects are telling me they’re lastly capable of sort out decades-old functions with confidence. These instruments clarify unfamiliar code in plain language, doc it routinely, and counsel 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 functions.
After which there are the businesses doubling down—reinvesting within the mainframe by upgrading to the most recent zSystems and embracing DevOps practices throughout the board. “If we are able to do it on the distributed aspect, we must always be capable of do it on the mainframe,” one company chief advised me. By staying present, these organizations cut back technical debt, assist fashionable 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% mentioned that their agency makes use of a mainframe. Of people who use mainframes, 54% indicated that their group would improve its use of a mainframe over the subsequent 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 shortly onboard new assets and get them aware of their code base sooner to develop into extra productive. Builders can pinpoint obligatory modifications sooner, decreasing planning time and accelerating updates.
These tendencies are promising, and I don’t doubt that they’d permit authorities companies to harness the mainframe’s information storage and processing energy whereas additionally adopting the agility that has been the hallmark of Silicon Valley.
