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NewsMaterial3 min readDisconfirmAI Editorial

Jeanette Manfra, Vice President of Risk and Compliance at Google Cloud: Nearly Half of Responding Enterprises Postpone or Cancel AI Projects Due to Legacy Systems

Based on a survey of 1,250 US business decision-makers, Jeanette Manfra, Vice President of Risk and Compliance at Google Cloud, argues that legacy software systems are acting as an 'innovation tax': 49% of respondents stated that on-premises software has actually delayed, reduced the scope of, or canceled AI projects, while 39% face licensing and negotiation constraints due to vendor lock-in. She proposes a zero-sum game mechanism—where IT engineering cycles are consumed by legacy maintenance, leaving no capacity for AI modernization—and recommends architectural choice freedom, secure-by-design foundations, and AI-driven defense.

2026-11-10

In November 2026, Jeanette Manfra, Vice President of Risk and Compliance at Google Cloud, published an analysis based on corporate research, with the core conclusion that the legacy software systems upon which enterprises rely for operations are acting as an 'innovation tax,' actively hindering the AI transformation that leaders are attempting to advance. She partnered with Hypothesis Group to investigate 1,250 enterprise IT and business decision-makers in the United States, aiming to quantify the specific forms of this friction.

The mechanism described by Manfra is a zero-sum game. As AI-driven vulnerability discovery accelerates, the number of flaws exposed in legacy codebases has exceeded the actual processing capacity of human security teams. IT teams are not unwilling to shift toward AI modernization; rather, their engineering cycles are fully occupied by daily patching and maintenance. In the survey, 49% of respondents indicated that reliance on on-premises software has actively delayed, reduced the scope of, paused, or canceled AI or other technology projects. This proportion approaches half, meaning that for nearly half of the responding enterprises, the maintenance costs of legacy systems have directly crowded out resources for AI investment.

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However, Manfra believes that enterprises' continued reliance on legacy environments may not stem from architectural preference, but from vendor lock-in. 39% of decision-makers reported that dependence on legacy software and their cloud providers forces them to navigate complex licensing rules, contract terms, or limited negotiating flexibility. She describes this concentration of single-vendor dominance—where operating systems, productivity applications, cloud hosting, and emerging AI models all come from one provider—as a massive single point of failure risk. She wrote that the security of an autonomous AI agent depends only on the operating system and network it traverses; when the underlying technology stack is vulnerable, organizations are not deploying resilient intelligence, but expanding their attack surface.

The survey also showed that 80% of leaders believe a provider's existing installed base should not determine which AI technology an organization adopts, and 78% believe that providers who led the previous era of enterprise technology may not be best suited to lead the AI era. Based on this, Manfra proposed three recommendations: demand freedom in architecture and vendor selection; promote secure-by-design foundations to eliminate entire classes of vulnerabilities before code release rather than patching afterward; and for organizations already running millions of lines of legacy code, deploy AI-driven defense to continuously discover active exploitation paths, prioritize critical exposures, and automate remediation.

It is worth noting that the publisher of this analysis is itself a supplier of cloud and AI services. Google Cloud mentions in the text that 'nearly 90% of the Fortune 100 are already using Gemini Enterprise to build, scale, and govern agents,' which serves as both background information and commercial positioning. The survey was commissioned by Google Cloud, and its directional conclusions—shifting from local legacy systems to cloud-native architectures—are consistent with the publisher's commercial interests. This does not mean the data itself is unreliable, but readers should be aware of this interest structure when applying the 'innovation tax' framework to their own decision-making, and independently assess whether the technical debt in their own organizations truly constitutes an obstacle to AI modernization to the same extent.

Sources

  • cloud.google.comGoogle Cloud Blog↗
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