Over 70% of Mainframe Modernization Projects Fail, Driven by Overreliance on Generative AI

The shift from mainframes to cloud-based environments or other modern infrastructures is often complicated by the presence of legacy systems, such as COBOL-based applications and ERP systems. The report highlights that relying too heavily on generative AI during these critical system migrations has contributed to project failures.

PLUS ULTRAby Amenoyomi

The high failure rate stems from a fundamental misunderstanding of the role generative AI plays in legacy system migration. While AI is effective at translating COBOL syntax into modern languages or adapting code to new specifications, it cannot decipher the underlying "intent"—the specific business logic and original purpose that the legacy code was designed to implement.

When companies rely solely on AI for conversion, they risk creating "black boxes" where the new system's behavior is accepted without a deep understanding of the original business requirements. Because the AI cannot explain the "why" behind a process, the resulting system may appear functional but exhibit unintended behaviors that are difficult to detect. This gap makes rigorous verification nearly impossible, often leading to critical errors and significant project delays.