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Those failures are happening in part because customer demands are changing. “In the past, people didn’t expect access to systems 24/7, and not all systems were connected,” Subramanian says. “But today, organizations don't operate on an island - they operate in an ecosystem. In that ecosystem, you have to exchange data, and it’s become increasingly apparent that the interchange points where those exchanges happen can become points of weakness.

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Competitive realities are also forcing many firms to reinvent, says Shodhan Sheth, Global Head of Enterprise Modernization, ������. “Businesses have been differentiating with technology for some time; now there are businesses that are differentiating with faster adoption of newer technology paradigms,” he explains. “If you can’t move fast and adopt new technology, your competition will adopt it faster and do something different, whether that’s a more interesting customer experience or better operational efficiency.”����

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These forces mean modernization has become a focus not only for CIOs, but also the rest of the C-suite, with and technology upgrades leading the list of priorities for both CEOs and CFOs this year.������

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CEOs eager to revitalize the tech estate

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“Cost base is another driving factor for modernization,” says Tom Coggrave, Principal Consultant, Enterprise Modernization Platforms and Cloud at ������. “CIOs are more responsible for that bottom line, and are seeing the maintenance of legacy systems as an ever-growing item on their balance sheet. They’re feeling the need to act, but the required business process transformation cuts right to the core of their technology estate.”��

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Another factor reshaping the landscape is cloud hyperscalers, often to the benefit of organizations that may have struggled with modernization in the past, Coggrave notes. As cloud vendors recognize companies are being held back and develop more services and products to address their pain points, “it’s reduced the bar in terms of getting started,” he says. “But there are still a lot of pitfalls on the path to be aware of.”

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Mainframe modernization: The before and after

“A lot of mainframes have been around longer than I’ve been alive, and run on a combination of customized packages and custom code, because back in the day, off-the-shelf options didn’t exist,” says Coggrave. “The biggest issue is that the people that worked on the system or wrote that code may not even be in the industry anymore, and the workforce that can handle that kind of legacy modernization is diminished.”�� ��

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Despite the difficulty, Shodhan urges businesses not to feel overwhelmed. “What I tell our customers is: I know you're getting scared because you see 50 million lines of code you have to deal with, but that doesn't mean your new system will be the same,” he explains. “It will probably end up being closer to 5 or 10 million lines of code, because there'll be a lot of stuff in there that you no longer need. Engineering practices have changed, and there’s a lot more you can offload. You might still have to pay for software as a service, but it changes your total cost of ownership, because there's less code you need to own and worry about.”��

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Mainframe modernization is delicate because it can require companies to do two things simultaneously. Adopting an incremental approach to modernization means “you go through a transitional period where some things are running off the mainframe and some things are running on the mainframe,” Coggrave explains. “People stumble when they haven’t thought about how to handle operating two worlds, keeping both working properly for an extended period of time.”��

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“When you're modernizing technology, one of the big questions is how you manage the old and new, sometimes at the same time,” adds Subramanian. “That can include things like budgeting, managing people and distributing work. Ultimately, it’s also about peoples’ growth and development, because if someone is just working on the old systems, that will have a lot of implications for capabilities and motivation.”��

Making the most of AI in the modernization process

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“You need labels that are well understood to describe what a system is doing, and with mainframe programs, it might take three months just to achieve a first version of that,”��Shodhan explains. “With automated capability detection, we can ask the LLM what a piece of code is doing, it will give us an answer. Then we can start grouping similar functionalities together. In legacy systems, that's very powerful, because similar functionalities exist in very different parts of the code. 50 million lines of code is probably not what you actually need, because there's a lot of duplication in there – but it’s not always easy to detect. The solution can highlight where the duplications and connections are, and help improve the design of your system.”��

The impact of this tool has already proven transformative for several ������ clients. Coggrave cites the example of an automotive manufacturer that had launched an effort to shift its complex architecture to the cloud, but was struggling to translate code into requirements that could be moved or rebuilt.����

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“We started with a proof of concept on a small part of the code, using CodeConcise to reduce the effort needed to get from code to current system requirements,” he says. “We were able to remove 8 person weeks of effort being spent to document each portion of legacy code, produce a report, then pass that through a couple of reviews to make it available for forward engineering. Scaling this to the client’s 15 million lines of code, this represents a huge savings, in the order of 60,000 person days of effort.”������

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Similar results were achieved with a client where CodeConcise was deployed to accelerate defect resolution. “When you want to implement something new or fix a bug, you need to know how the system works, to really navigate it and find the places where a fix is needed,” says Shodhan. “Optimistically, it can take around three months for developers to start becoming familiar with legacy systems. We introduced a new ������ pair to the client teams equipped with CodeConcise. In the best cases, our pair could find a fix in three hours for an issue that previously took three weeks to resolve.”������

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“The basic premise of modern software engineering is the ability to change software at pace with confidence, and to know that if something breaks, you have that golden safety net,” Subramanian says. “Usually, in a legacy system, the safety net is provided by a separate team that goes through a long testing cycle. Automating those tests is one area where the potential tooling of products around GenAI can come into play, to accelerate that development-testing feedback loop.”��

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Subramanian also sees significant potential for GenAI to enable smoother translations of regulation into software features, and accelerate the validation process, simply by reducing the need for teams to digest or generate documentation. “We’re trying to look beyond areas like just helping you type faster as a programmer, to those that actually have value,” he says.��

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Often, evolving will mean adopting new tools and approaches that promise to make modernization less painful. “We’ve told our clients; it’s hard to define a blueprint for what your technology looks like over the next five to seven years,” says Coggrave. “If you’re developing seven data pipelines, by the time you've done four of them, there's going to be some new capability a cloud provider will bring out that makes it even easier. So we shouldn't lock ourselves in; blueprints need to be able to change quickly."

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“Evolutionary architecture is one of the things that people need to be abreast of, because the rate of change in technology is ever-increasing,” he adds. “What’s most important is making sure that you have the validation and testing that sits around the system that makes technology more interchangeable, so whatever new technology you’re looking at can be absorbed in the future.”������

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Companies that have pursued an evolutionary and incremental path will generally be in a good position to manage and benefit from future change. “Breaking things down into smaller systems allows you to make experiments and look at the new technology out there more quickly, whereas if you have one huge, monolithic system that runs everything for you, introducing new data pipeline technology is near impossible, because it’s like changing the foundations of a building while it’s still standing,” says Coggrave.����

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Ultimately, according to Subramanian, “the best measure of modernization is resilience – how quickly can the organization adapt, and how quickly can the people within the organization adapt?”��

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“Success can be seen as the muscle memory of the organization itself developing, to expect a regular cadence of change, rather than deciding it’s reached an end-goal,” he says.��

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