Most Enterprises Lack Visibility Into AI Dependencies, IBM Study Finds

Enterprises are embedding artificial intelligence deeper into operations without understanding the dependencies they're creating, according to new research from the IBM Institute for Business Value.

The study, based on responses from 1,000 senior executives across 16 countries and 17 industries surveyed between February and April 2026, found that 91% of organizations don't fully understand their dependencies across AI vendors, models, and infrastructure. This visibility gap comes as 71% of respondents report that switching their primary AI vendor or model would be difficult.

Why it matters

As AI moves from experimental projects to core business systems, dependency lock-in creates operational and financial risk that most organizations haven't adequately mapped. The combination of limited visibility and high switching costs means enterprises may be unable to respond quickly when vendors change pricing, deprecate models, or experience outages—events the study shows are already common.

Disruptions Already Occurring

Surveyed leaders reported an average of six AI-related disruptions over the past two years, primarily driven by vendor service issues. Despite this track record, 81% said a seven-day vendor outage would cause severe or critical disruption to their operations.

Executives cited unexpected changes including price increases, usage restrictions, model deprecations, and performance degradation across the AI ecosystem. Meanwhile, 68% said meeting data residency and sovereignty requirements across different geographies remains challenging, adding complexity to any effort to migrate AI systems or data.

The Control Gap

The research identified a small group of organizations—just 7% of those surveyed—with advanced AI control capabilities that design systems to adapt data, models, and infrastructure as conditions change. These organizations see less AI downtime and protect 55% more operating profit from AI-driven disruptions compared to peers, according to IBM's analysis.

"AI has introduced new forms of dependency that evolve faster than traditional governance, procurement, or technology cycles were designed to handle," wrote Ana Paula Assis, IBM Senior Vice President and Chair for EMEA and APAC, in the study's foreword. "Any loss of control can translate directly into margin pressure, compliance exposure, or outright business disruption."

Multi-Vendor Reality

While 73% of surveyed organizations describe their AI environments as intentionally multi-vendor, the drivers appear more reactive than strategic. Independent business unit decisions and geographic necessity each drove vendor diversity for 69% of respondents, while 57% cited legacy complexity from mergers, acquisitions, and historical technology choices.

The study also found that 72% of executives would accept a 20% cost increase to maintain AI vendors if it improved strategic flexibility, suggesting awareness of the problem even as most organizations lack the control mechanisms to address it.

The findings were first reported by IBM through a press release announcing the study, titled "The Calculus of AI Sovereignty." The full research was conducted in collaboration with Oxford Economics.