- Most enterprises are deeply tied to their AI vendors, with 71 per cent saying switching would be difficult and many facing data residency challenges across regions
- Organizations lack visibility into their AI dependencies, leading to frequent disruptions and high risk of severe operational impact during vendor outages
- A small group with stronger AI control and adaptability performs significantly better, but most companies remain constrained despite growing demand for flexibility and sovereignty
- IBM stock (NYSE:IBM) last traded at US$269.86
A new global study from the IBM (NYSE:IBM) Institute for Business Value highlights mounting challenges enterprises face as they integrate artificial intelligence more deeply into core operations, with many organizations finding themselves increasingly dependent on AI systems that are difficult to replace or modify.
The report, titled “The Calculus of AI Sovereignty,” is based on insights from 1,000 senior executives and points to a growing tension between rapid AI adoption and the need for greater control over technology infrastructure, data, and vendor relationships.
According to the study, 71 per cent of respondents say switching their primary AI vendor or model would be difficult, suggesting that organizations are becoming locked into AI ecosystems with limited flexibility. At the same time, 68 per cent of executives report challenges in meeting data residency and sovereignty requirements across different geographic regions, complicating efforts to move AI systems or data between environments.
These constraints are contributing to increased pressure on enterprises to strengthen oversight and governance as regulatory requirements evolve and reliance on AI intensifies.
This article is a journalistic opinion piece that has been written based on independent research. It is intended to inform investors and should not be taken as a recommendation or financial advice.
Limited visibility, rising risks
Despite the growing importance of control, the study finds that most organizations lack a clear understanding of their own AI dependencies. A significant 91 per cent of surveyed executives say they do not fully grasp how their systems rely on specific vendors, models, or infrastructure components. This lack of visibility can hinder risk assessment and make it more difficult to plan for potential disruptions.
Operational risks appear to be more than theoretical. Surveyed organizations reported an average of six AI-related disruptions over the past two years, many tied to vendor services. Furthermore, 81 per cent of respondents indicated that a vendor outage lasting seven days would cause severe or critical disruption to their operations, effectively halting business activity.
Executives also cited a range of unexpected changes within the AI ecosystem, including price increases, new usage restrictions, model deprecations, and performance issues—all of which can affect system stability and organizational planning.
Performance linked to adaptability
The report suggests that organizations investing in adaptable AI architectures—capable of shifting across data, models, and infrastructure—are better positioned to manage these risks. This approach, described in the study as a core component of “AI sovereignty,” enables enterprises to maintain continuity even as external conditions change.
According to IBM’s analysis, organizations with the most advanced AI control capabilities experience less downtime and are able to protect 55 per cent more operating profit from AI-related disruptions compared to their peers. However, only 7 per cent of surveyed organizations currently operate at this level, indicating a significant gap between leading adopters and the broader market.
The findings also point to a willingness among enterprises to invest in greater flexibility. Approximately 72 per cent of respondents said they would accept a 20 per cent increase in costs if it improved their ability to switch vendors or maintain strategic flexibility.
Multi-vendor reality driven by complexity
While 73 per cent of organizations describe their AI environments as “intentionally multi-vendor,” the study suggests that this diversity is not always the result of deliberate strategy. Instead, it is often driven by structural and operational factors.
Independent decision-making by business units and geographic requirements were each cited by 69 per cent of respondents as key reasons for adopting multiple vendors. Legacy complexity—stemming from mergers, acquisitions, and historical technology choices—was also identified by 57 per cent of executives as a contributing factor.
Together, these dynamics illustrate how multi-vendor environments can emerge organically rather than through coordinated planning, adding another layer of complexity to AI governance.
Growing focus on sovereignty
The study underscores the increasing importance of AI sovereignty, defined as an organization’s ability to maintain control, flexibility, and resilience across its AI systems. As regulatory frameworks expand and reliance on AI deepens, enterprises are being pushed to reassess how they manage dependencies and ensure continuity.
IBM’s findings suggest that while many organizations recognize the importance of control, the path to achieving it remains uneven. A combination of technical complexity, regulatory pressures, and entrenched vendor relationships continues to shape how enterprises navigate the evolving AI landscape.
“AI has introduced new forms of dependency that evolve faster than traditional governance, procurement, or technology cycles were designed to handle,” IBM’s Senior Vice President and Chair, Ana Paula Assis, stated in a media release. “That is why AI sovereignty has become one of the most defining leadership issues of this moment. The stakes are no longer technical; they are economic. Any loss of control can translate directly into margin pressure, compliance exposure, or outright business disruption.”
About IBM
International Business Machines Corp. is a global hybrid cloud, AI and consulting company with clients in more than 175 countries.
IBM stock (NYSE:IBM) last traded at US$269.86 and has risen more than 7 per cent in the past three months, but has lost 4 per cent since this time last year.
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