‘You need those experts to even define what these transformations are’: COBOL developers will always be needed, even as AI takes the lead on modernization projects

COBOL experts aren’t at risk of losing out to generative AI, even as the technology helps noticeably speed up mainframe modernization, according to an AWS executive.

Asa Kalavade, VP of AWS Transform, told ITPro that AI holds immense potential for workload modernization – but that it can’t and shouldn’t replace experts in the field.

AWS Transform is the firm’s agentic AI modernization platform, intended to speed up the translation and update process for legacy code, infrastructure, and applications.

First unveiled in May 2025 it was expanded upon at AWS re:Invent, and is a core offering for firms looking to make

AWS Transform for Mainframe is specifically designed for AI translation of mainframe codebases in languages such as COBOL, JCL, and BMS, and systems including CICS, DB2, and VSAM, into a modern language such as Java.

Generative AI has made it easier than ever to translate code from one language to another. However, at an enterprise level leaders need to know that this process won’t introduce logic errors or otherwise make workloads unpredictable or harder to audit.

Translating COBOL is no mean feat

When it comes to translating COBOL, AWS Transform gets around this by extracting system management facility (SMF) records, logs generated in IBM z/OS mainframes.

These contain details on the correct steps for implementation, execution, and workload routines to ensure that code generated by Transform retains the integrity of the original code.

Kalavade added that using AWS Transform, organizations can compare the efficiency of migrated workloads against existing P90 and P95 records to confirm no loss in performance.

AI within the tool can also be used to generate test cases to stress test the migrated mainframe code. However, Kalavade stressed that human experts are still needed at all stages of the modernization process.

For example, she explained that human experts with familiarity of mainframe systems are needed to help pressure test cloud systems post-migration.

In February, shares in firms such as IBM plummeted after Anthropic claimed its latest Claude release could translate COBOL into modern programming languages.

“You do need those COBOL experts, because how do you validate this code? And the point is, the COBOL expertise is declining.

“So in fact, we find in our team some of our unicorns are those that have come from years of doing COBOL, but now they know how to translate and they can verify so that whole human reinforcement that is needed to verify that the output is matching the input that is required.”

Kalavade added that COBOL is not just one language and that experts are still needed for proper understanding and handling of code translation.

“There are so many variants of COBOL, there are so many languages within a mainframe application, whether it's PL/I [or] Easytrieve, so you need those experts to even define what these transformations are.”

AWS has benefited in modernization efforts from its 2021 acquisition of Blu Age, a French company that had over 20 years of experience in like-for-like code translation.

“The core asset that came out of that understanding was to build the control flow graph of that old application, and that is what gives us the ability to combine the old and the new,” Kalavade explained.

IBM z/OS mainframes are still widely used across financial services, government, and healthcare among other critical industries, so it’s important that any migrated workloads are done.

“Our mainframe workloads have got the most signal where customers who thought they could previously never modernize these applications are now really accelerating some of this modernization.

Kalavade pointed to customers like BMW and Brazil-based Itao bank as customers that are already leaning heavily into AWS’ mainframe modernization tools.

“What was most exciting to me was, we launched Transform back in May as GA and at re:Invent we had four or five of these mainframe customers providing references and experiences,” Kalavade noted.

“One might wonder, why is it so exciting? These projects typically took two, three, five years, so even if it's a start, having customers finish these projects within at least some workloads – it's not like they finished their entire mainframe project – has been very rewarding to see.”

Not every workload will move right to the cloud, Kalavade added, and IBM is a strong AWS partner for on premises workload management.

VMware licence spurring cloud migration

A noticeable factor in cloud migration has been the VMware licence changes, which have spurred 86% of customers to reduce their VMware footprint.

While one option is to go cloud native via a lift and shift process from VMware workloads to AWS EC2, others are finding the move a laborious undertaking involving unpicking numerous interdependencies.

Kalavade told ITPro that VMware licences have not only motivated customers to move to the cloud, but also to use this shift as an opportunity to modernize and adopt containers. AWS is currently beta-testing a dedicated agent for containerization, while AWS Transform can handle the code contained in migrating VMs.

“Those VMs often will have a database that is on that VM, or you’ll have a Windows Server, so some customers will look to move those VMs, and then once they're in the cloud then they might want to modernize the database from SQL Server to [Amazon Aurora MySQL], or to go from .NET Framework to Core.”

AWS is working to make the entire process easier for customers through integrated AI agents. For example, Kalavade described how administrators can use a chat window to ask questions about code modernization plans directly within AWS Transform, which can be connected with a coding agent to generate code directly from a user’s natural language inputs.

“What we see could be happening is over time, as these coding agents become more autonomous, once you've defined the spec, then the agent can go and do more and more long-running tasks to generate the code, create the test cases, and validate which is what the LLMs are starting to get really good at.

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