There’s a whole lot of pleasure proper now about AI enabling mainframe utility modernization. Boards are paying consideration. CIOs are getting requested for a plan. AI is a real accelerator for COBOL modernization however to get outcomes, AI wants further context that supply code alone can’t present.Right here’s what we’ve realized working with 400+ enterprise clients: mainframe modernization has two very completely different halves. The primary half is reverse engineering, understanding what your current programs truly do. The second half is ahead engineering, constructing the brand new functions.
The primary half is the place mainframe initiatives reside or die. Nevertheless, coding assistants are genuinely good at solely the second half. Give them a transparent, validated spec they usually’ll construct fashionable functions quick.
We’ve got realized that delivering profitable COBOL modernization requires an answer that may reverse engineer deterministically, produce validated and traceable specs, and assist these specs circulate into any AI-powered coding assistant for the ahead engineering. A profitable modernization requires each reverse engineering and ahead engineering.
What a profitable mainframe modernization requires
Bounded, full context
Mainframe functions are massive. Actually massive. A single program can run tens of 1000’s of strains, pulling in shared knowledge definitions from throughout the system, calling different applications, orchestrated via JCL that spans all the panorama. In the present day, AI can solely course of a restricted quantity of code at a time. Feed it one program and it could possibly’t see the copybooks, the referred to as subroutines, the shared information, or the JCL that ties all the pieces collectively. It’ll produce output that appears affordable for the code it could possibly see however miss dependencies it was by no means proven. In working with clients, we clear up this by extracting all implicit dependencies deterministically first, then feeding AI bounded, full items with all the pieces it wants already resolved. That manner AI focuses on what it’s nice at (understanding enterprise logic, producing specs) as an alternative of guessing at connections it could possibly’t see.
Platform-aware context
Right here’s one thing that surprises folks: the identical COBOL supply code behaves in another way relying on the compiler and runtime. How numbers get rounded, how knowledge sits in reminiscence, how applications speak to middleware. These aren’t within the supply code. They’re decided by the particular compiler and runtime atmosphere the code was constructed for. Many years of hardware-software integration can’t be replicated by merely shifting code. We discovered that AI does its finest work when platform-specific conduct has already been resolved. Feed AI clear, platform-aware enter, and it delivers. Feed it uncooked supply code, and it’ll generate output that appears proper however behaves in another way than the unique. In monetary programs, a rounding distinction isn’t a beauty subject. It’s a cloth error.
A traceable basis
If you happen to’re in banking, insurance coverage, or authorities, your regulators will ask one query: are you able to show you didn’t miss something? AI by itself isn’t sufficient to extract enterprise logic and generate documentation that regulators will settle for. Regulatory compliance requires each output to have a proper, auditable connection again to the unique system. We realized early that traceability doesn’t come from AI studying supply code. It comes from structuring the code into exact, bounded items so we all know precisely what goes into the AI and may hint each output again to its supply. For purchasers in regulated industries, that is typically the distinction between a challenge that strikes ahead and one which stalls.
How we set AI up for achievement in AWS Remodel
We constructed AWS Remodel to modernize mainframe functions at scale. The concept is easy: give AI the correct basis, and clients get traceable, right, and full outcomes they will take to manufacturing. AWS Remodel begins by constructing an entire, deterministic mannequin of the applying. Specialised brokers extract code construction, runtime conduct, and knowledge relationships throughout all the system — not one program at a time, however the entire panorama. This produces a dependency graph aligned with the precise compiler semantics, capturing cross-program dependencies, middleware interactions, and platform-specific conduct earlier than AI will get concerned. From there, massive applications get decomposed into bounded, processable, items. Platform-specific conduct is resolved deterministically. The items are sized for AI to course of successfully. Then AI extracts enterprise logic in pure language, and each output will get validated towards the deterministic proof we’ve already extracted. Specs map again to the unique code. When a regulator asks “did you miss something?”, there’s a verifiable reply. What units this aside is that AI by no means operates at midnight. Each unit it processes has recognized inputs and anticipated outputs, so we will validate what comes again. No different strategy in the marketplace closes that loop. What comes out is a set of validated, traceable technical specs that plug into any fashionable improvement atmosphere. The exhausting a part of modernization is knowing what exists at present. When you’ve captured that in exact specs, AI-powered IDEs can construct the brand new utility with confidence.
An end-to-end platform for enterprise transformation
No one modernizes one app. Our clients are watching portfolios of a whole bunch or 1000’s of interconnected functions, they usually want far more than evaluation assist. AWS Remodel automates throughout the total lifecycle: evaluation, check planning, refactoring, reimagination. The entire thing. And inside that, completely different apps want completely different paths. Some get re-imagined from scratch. Some simply want a clear, deterministic conversion to Java. Some have to get out of the info middle first and modernize later. Some will stay on the mainframe. We realized the exhausting manner that treating all of them the identical is how initiatives blow up. The portfolio resolution (which app, which path, what order) issues as a lot because the tech. In our expertise, that is the one manner enterprise modernization truly finishes. One-size-fits-all approaches are why these initiatives fail. Yet one more factor that will get missed always: check knowledge. You’ll be able to’t show the modernized app works with out actual manufacturing knowledge and actual situations. We’ve watched groups get all through code conversion after which stall as a result of no one deliberate for knowledge seize. So, we constructed check planning and on-prem knowledge seize into the platform from day one. Not a cleanup train on the finish. That’s what this truly seems to be like when it really works. Finish-to-end automation, the correct path for every app, validation baked in.
The way to get this proper
The query isn’t “ought to we use AI for COBOL modernization?” In fact you must. The query is the way you set AI as much as ship: traceability for regulators, platform-specific conduct dealt with appropriately, consistency throughout your utility portfolio, and the power to scale to a whole bunch of interconnected applications. That’s what we discovered constructing AWS Remodel. Deterministic evaluation as the inspiration. AI because the accelerator. An AWS service that covers the total vary of modernization patterns.
And it’s working.
BMW Group decreased testing time by 75% and elevated check protection by 60%, considerably decreasing threat whereas accelerating modernization timelines.
Fiserv accomplished a mainframe modernization challenge that might have taken 29+ months in simply 17 months.
Itau reduce mainframe utility discovery time and testing time by greater than 90%, enabling groups to modernize functions 75% sooner than with earlier guide efforts.
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