What changed
Google Cloud, in partnership with Hypothesis Group, surveyed 1,250 enterprise IT and business decision-makers across the United States. The findings reveal that legacy software systems are acting as an innovation tax, actively holding back AI transformation efforts. Nearly 90% of Fortune 100 companies are already using Gemini Enterprise to build, scale, and govern agents, but the integration of AI into autonomous workflows is being constrained by existing technology environments.
What this means in practice
The survey defines 'legacy' technology as software running locally on computers and servers rather than natively in the cloud. Decision-makers report that maintaining this footprint exerts constant friction on AI ambitions. Nearly half (49%) of respondents say relying on on-premises software has delayed, reduced in scope, paused, or canceled AI or other technology initiatives.
Routine patch application and maintenance have emerged as a primary operational bottleneck. As AI-enabled vulnerability discovery accelerates, the rate at which flaws are surfaced in legacy codebases has outpaced what human security teams can realistically address.
This creates a zero-sum game between routine maintenance of legacy systems and AI modernization, with IT teams spending time and resources on maintaining legacy systems instead of advancing AI initiatives.
Limitations and availability
The survey highlights that vendor lock-in is a significant factor in the continued reliance on legacy environments. 39% of decision-makers report that reliance on legacy software and cloud providers forces them to navigate complex licensing rules or contracts, or limited negotiating flexibility. Additionally, the systemic operational risk created by single-vendor concentration is a concern, as concentrating operating systems, productivity applications, cloud hosting, and emerging AI models within a single vendor's monoculture creates an immense single point of failure.
The solution proposed is to ensure enterprise buyers and policymakers guarantee genuine choice and interoperability in software, cloud, and AI architectures. 80% of leaders agree that a provider’s installed base should not determine which AI technology an organization uses, while 78% believe that providers who led the previous era of enterprise technology may not be best positioned to lead the AI era.
