A comprehensive survey conducted by Vertesia among 1,500 senior IT leaders reveals that nearly all enterprises (96.8%) face significant limitations in deploying desired AI use cases due to the pace and direction of their Enterprise Content Management (ECM) vendors' roadmaps. While many organizations continue to increase AI budgets—with 40.2% planning higher investments—71.9% report that vendor timelines moderately or significantly constrain their ability to execute specific AI strategies. This growing misalignment signals an inflection point where enterprises are actively exploring alternative paths to sustain AI momentum.
As enterprises shift from basic AI features like enhanced search to more transformative applications—such as automated risk detection, large-scale summarization, and compliance monitoring—current ECM offerings and release cycles are falling short. The Vertesia survey indicates that vendor-driven constraints are no longer merely inconveniences but active inhibitors of strategic progress. With budgets continuing to rise, the inability to execute high-value use cases creates frustration and prompts reevaluation of traditional dependencies on single-vendor ecosystems.
“As organizations move beyond basic search toward more valuable, high-impact automation, current ECM features and release schedules are no longer aligning with what enterprises are trying to achieve,” said Chris McLaughlin, Chief Revenue Officer at Vertesia. “Driven by this misalignment, enterprises are pursuing alternative approaches to accelerate AI adoption and address pressing business needs.”
The identified capability gaps underscore a core challenge: content remains fragmented, poorly prepared, and difficult to analyze systematically. Until organizations can reliably extract insights from stored documents across environments, advanced AI applications will remain limited. Respondents emphasized the need for capabilities that go beyond storage to deliver actionable intelligence on risks, compliance, duplicates, and sensitive data.
“These gaps point to a foundational issue,” McLaughlin added. “Content preparation and readiness remain a bottleneck for enterprise AI. Until organizations can consistently prepare and understand their content, it’s difficult to apply AI across fragmented environments. What’s changing now is how leaders think about that problem—moving toward approaches that separate intelligence from where content is stored, rather than anchoring AI to a single ECM system.”
Faced with these constraints, organizations are adopting divergent strategies. Approximately 34% are investing heavily to build proprietary AI capabilities from the ground up, accepting substantial cost and complexity. Meanwhile, a growing segment—32.3%—is turning to external platform providers that offer AI solutions capable of operating consistently across heterogeneous ECM environments, including leading systems from Hyland, OpenText, IBM, and Microsoft. This platform approach decouples intelligence from storage, enabling faster deployment, broader applicability, and reduced vendor lock-in.
The survey, conducted in late 2025 among Director-level and above IT, Operations, and Product Management leaders in organizations with at least $100 million in annual revenue, highlights a clear shift in mindset: enterprises are prioritizing speed, flexibility, and strategic control over rigid adherence to vendor timelines.
About Vertesia
Vertesia is a SaaS platform purpose-built to accelerate the development and deployment of custom generative AI (GenAI) applications, agents, and services. Combining enterprise-grade infrastructure with a low-code environment, Vertesia helps organizations overcome the complexity and cost typically associated with deploying GenAI at scale – reducing time to value while enhancing output accuracy and enabling comprehensive governance. Featuring an API-first architecture, the platform provides flexible system integration, intelligent content preparation, and powerful tooling for autonomous agents. Vertesia transforms GenAI initiatives from one-off experiments into strategic competencies.
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