Details
- IBM's Institute for Business Value released a global study, "The Calculus of AI Sovereignty," based on 1,000 senior executives, finding most enterprises are locked into AI systems they cannot easily change.
- The study reports 71% of respondents find switching their primary AI vendor or model difficult, while 68% struggle with data residency and sovereignty rules across geographies, complicating AI and data portability.
- Executives cite limited visibility into their AI stack: 91% do not fully understand dependencies across vendors, models, and infrastructure, despite reporting an average of six AI-related disruptions over two years and severe impact from a seven-day vendor outage.
- Organizations with advanced AI control and sovereignty capabilities—able to adapt data, models, and infrastructure as conditions change—experience less downtime and protect 55% more operating profit from AI-driven disruptions, yet only 7% of surveyed firms operate at this level.
- Although 73% describe their AI environment as multi-vendor, diversity is mainly driven by business unit decisions, geography, and legacy complexity rather than deliberate strategy, and 72% of executives would accept a 20% cost increase to preserve vendor relationships if it improved strategic flexibility.
Impact
The findings elevate AI sovereignty from a technical concern to a board-level economic and risk-management issue, as vendor lock-in, opaque dependencies, and regulatory constraints expose core operations to outages and policy shifts. Over the next 12–24 months, enterprises are likely to prioritize multi-vendor strategies, portability, and governance tooling that increase visibility and control, influencing buying criteria for hyperscalers, foundation model providers, and AI infrastructure platforms.
