On June 18, 2026 Gartner published a prediction that should make every CIO sponsoring a mainframe exit pause:
“More than 70% of mainframe exit projects initiated in 2026 will fail to produce the intended benefits due to an overestimation of generative AI (GenAI) tooling capabilities.”
I agree with Gartner. We see it every week.
Gartner’s recommendation underneath the headline
It’s worth clarifying what Gartner means. Mainframe modernization encompasses both migrating off the platform and modernizing in place. The 70% failure figure applies specifically to full exits. For most workloads, Gartner is recommending in-place modernization instead.
The 70% figure will generate most of the talk, but the body of the release is making a sharper point.
“For many mainframe customers, GenAI can be more effectively used to enable modernization in place rather than accelerate migration off the platform.” — Alessandro Galimberti, VP Analyst at Gartner
Gartner is recommending a platform-smart approach – evaluating workloads individually and placing them in the right environments, rather than chasing a wholesale exit. Organizations should balance strategies focused on optimizing existing mainframe investments while limiting full platform exits to select, case-by-case scenarios – efforts that, in Gartner’s words, require high-risk transformation and often result in suboptimal outcomes.
That isn’t a story about better migration tooling. It’s a story about asking better questions and a fact-based assessment before the migration question is even on the table.
What GenAI can and can’t do in a mainframe exit
Gartner contends that the high failure rate is due to the expectation that generative AI will fix complex legacy code easily. Based on my experience with multiple transformation initiatives involving mainframes, I’ve observed and dealt with what generative AI can and cannot do well.
What GenAI can do well: read code at scale, surface technical debt, summarize what a module appears to be doing, generate first-pass documentation. It’s useful and time-saving work.
What GenAI can’t do, at least not reliably, in a mission-critical mainframe environment:
- Tell you why a calculation produces the result it does today.
- Provide reliably consistent and complete explanation, especially when functional threads traverse nested modules.
- Tell you what a branching condition in a COBOL module is actually checking against, and what business rule that condition encodes.
- Tell you whether a hard-coded value in a 1998 program was a temporary patch or a deliberate business decision that downstream systems now depend on.
These gaps – between code description and business meaning – are where mainframe exit projects fail. And it’s these gaps that a generative model, however capable, can’t close on its own.
The cost of not understanding what’s there – today
The Gartner finding focuses on exits – but the cost of not understanding your mainframe shows up long before any exit project starts.
Every time a business requirement changes – a new regulation requires a different calculation methodology, a product team needs to update how interest is accrued, an auditor asks where a number came from – someone has to go into the mainframe and answer the question. Before they can change a single line of code, they need to trace what the change will affect: which modules read the variable, which tables get updated, which downstream processes depend on the output, which conditional branches treat it differently.
In a typical environment, that investigation can take weeks or months – and it depends on a shrinking pool of mainframe specialists who are simultaneously running the system. The risk of getting it wrong is real: unintended consequences that show up weeks later in a reconciliation break or a misstated customer statement.
This is the recurring cost the modernization conversation usually skips over. It’s the cost of operating the mainframe without visibility into it – every quarter, every change request, every audit cycle. The 70% of exits that will fail isn’t the only story. The other story is the daily tax that organizations are paying for systems they can’t fully explain.
Explainability is the precondition
Explainability is what makes data AI-ready in a regulated environment. The same principle applies to legacy systems: a system is decision-ready – for modernization, for regulators, for the next code change – when you can explain, with traceable evidence, how it produces what it produces.
Gartner’s framing is correct: AI is being asked to do work it cannot reliably do. But the deeper lesson in the 70% failure number is that the work AI is being asked to skip is the strategic work that determines whether the right path was chosen in the first place. And that work pays for itself long before modernization day – it pays for itself every time the business asks a question the mainframe is supposed to answer.
What the teams who get this right are doing
The mainframe programs that succeed – whether the answer is a full exit, modernization in place, a hybrid, or just running the system safely for the next several years – share a pattern. They treat understanding the legacy environment as a first-order capability, not a one-time pre-migration task.
They invest in surfacing the actual business logic embedded in mainframe code – the calculation logic, the conditional branches, the field-level relationships, the cross-module dependencies – before they decide what to replicate, retire, redesign, or leave alone. That investment doesn’t just serve the eventual migration. It makes today’s mainframe safer to manage, today’s regulatory questions faster to answer, and today’s code changes less risky to make.
At one Fortune 100 financial institution where Zengines Contextual Data Lineage is used every day, the team’s question started with “For each module that touches a regulated calculation, what does it actually do, and what depends on it?” That question scopes an honest mainframe modernization program – what to exit, what to modernize in place, what to leave alone, and how to operate the mainframe safely in the meantime.
The difference isn’t only faster code conversion. The difference is that they know what they have – every day, not just on modernization day.
Mainframes aren’t going anywhere
Despite the industry’s push toward cloud migration and modernization, many financial institutions still rely on mainframe systems to process millions of daily transactions, calculate interest accruals, manage account records, and run core business operations. And they will for years to come.
Modernization is the eventual reality for most organizations still running on mainframes. For many financial institutions, a full modernization effort is on the roadmap but years away – dependent on budget cycles, vendor timelines, regulatory considerations, and a hundred other competing priorities. For others – and this is increasingly what Gartner is pointing toward – modernization will mean working with the mainframe, not off of it.
Either way, the system runs every day in between. And every day, it has to be safely managed, changed, audited, reconciled, and explained.
How Zengines bridges today and tomorrow
This is the bridge Zengines was built to be.
Zengines Contextual Data Lineage parses COBOL, RPG, and PL/1 at scale and surfaces what is actually inside legacy code: the data paths, calculation logic, conditional branches, hard-coded values, and module-to-module dependencies that determine how a legacy system produces what it produces. Analysts get the answer to a reverse-engineering question in minutes – in plain English, with business context – instead of waiting weeks for a mainframe SME to dig through code by hand.
That visibility pays off on two timelines. Today, it makes the mainframe safer to manage: business requirement changes get scoped accurately, regulators get answers in hours instead of months, and engineers can make code changes with confidence about the blast radius. Tomorrow, whenever modernization day arrives – whether that means a full exit, modernization in place, or a workload-by-workload approach – the team isn’t starting from scratch. The understanding is already there.
The mainframe isn’t the problem. The lack of visibility into it is.
If you are managing a mainframe today, planning to modernize tomorrow, or – as Gartner is increasingly suggesting – deciding whether modernization should mean staying on the platform and changing how you work with it, we’d like to show you what Contextual Data Lineage surfaces in your environment.
Frequently asked questions
What is mainframe modernization?
Mainframe modernization covers two different paths: migrating workloads off the platform entirely, and modernizing in place while continuing to run on the mainframe. Gartner’s 70% failure prediction applies specifically to full exits – for many workloads, in-place modernization is the lower-risk route.
Why do mainframe exit projects fail?
Most fail because the business logic embedded in decades-old code is never fully understood before the migration is designed. Generative AI can summarize what a module appears to do, but it cannot reliably explain why a calculation produces a given result, or whether a hard-coded value was a patch or a deliberate business rule. Those gaps become failed reconciliations later.
Can generative AI migrate mainframe code?
It can assist, not replace. GenAI is effective at reading code at scale, surfacing technical debt, and generating first-pass documentation. It is unreliable at automated conversion of complex, mission-critical legacy applications – which is precisely the overestimation Gartner identifies as the cause of most 2026 exit failures.
Should we exit the mainframe or modernize in place?
It depends on the workload, and the honest answer requires knowing what each application actually does first. Evaluating workloads individually – rather than committing to a wholesale exit – is what lets an organization retire what should be retired, modernize what benefits from it, and safely keep running what should stay.