When AI inherits your technical debt

The real challenge facing organizations isn't whether to adopt AI

by · TechRadar

Opinion By Scott Francis Published 3 August 2026

(Image credit: Getty Images)

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As enterprises rush to deploy AI, many are discovering an uncomfortable reality: their greatest obstacle isn't the technology itself, it is the decades-old systems underpinning their operations.

While AI promises productivity gains, automation, and smarter decision-making, legacy platforms were never designed to support the data accessibility, interoperability, and real-time intelligence modern AI requires.

In fact, legacy infrastructure continues to be one of the most significant barriers to successful AI adoption today.

Latest Videos FromTechRadarWatch full video here: Scott Francis

Technology Evangelist at PFU America, Inc.

The real challenge facing organizations isn't whether to adopt AI, but whether their existing technology foundations can support it at scale.

Enterprises that fail to modernize their underlying systems may find themselves trapped in a cycle of rising costs, fragmented workflows, and underwhelming AI outcomes, making it harder to compete and capture meaningful return on investment.

Despite major initiatives and large amounts of capital invested into digital transformation over the past decades, the journey still isn’t over. According to research by Synergy Labs, 62% of organizations in the U.S. still rely on outdated software in 2026, with maintenance alone consuming up to 80% of IT budgets.

Shockingly, McKinsey reports that as much as 70% of the business software used by Fortune 500 companies was developed more than 20 years ago. Bottom line: enterprises are collectively losing $370 million annually on technical debt and addressing it is increasingly becoming a business imperative rather than just another IT project.

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