What the IBM study found
In June 2026, IBM published research conducted with Oxford Economics covering 1,000 senior executives across 16 countries and 17 industries. The survey ran from February through April 2026. The headline findings: - 91 percent of respondents said they do not fully understand their AI dependencies across vendors, models, and infrastructure - 71 percent said switching their primary AI vendor would be difficult - 68 percent said meeting data residency and sovereignty requirements is challenging - The average organization reported 6 AI-related disruptions in the past two years, most driven by vendor services - 81 percent said a 7-day vendor outage would cause severe or critical disruption - 72 percent said they would accept a 20 percent cost increase to maintain their current AI vendors if it improved strategic flexibility The most significant finding was not about disruption frequency. It was about profit. Organizations that IBM classified as operating at the highest level of AI control protected 55 percent more operating profit from AI-driven disruptions than those without that level of control. Only 7 percent of respondents operated at that level.
What the numbers mean in practice
The 55 percent operating profit protection figure is the clearest way to read this study. IBM is not saying high-control organizations earn more from AI. They are saying these organizations lose less when something goes wrong. That is a more honest and more useful measure of value than most AI ROI claims. When a vendor raises prices, changes a model, deprecates an API, or experiences an outage, businesses with strong dependency awareness know exactly what is affected and have alternatives ready. Businesses without that awareness get surprised and respond under pressure. The 81 percent severe-or-critical figure for a 7-day outage reflects how deeply AI has been embedded in operations without a parallel investment in resilience planning. Most of those integrations happened fast, during a period when tools were cheap and downside scenarios felt theoretical. The 72 percent willing to pay a 20 percent cost premium reveals something important: most businesses already sense the dependency problem. They just have not quantified it or documented it well enough to act on it.
What business owners typically misunderstand
The first misunderstanding is that AI vendor dependency is a large enterprise problem. IBM's study covered enterprises, but the underlying dynamics apply directly to medium-size businesses. Medium-size businesses typically have more concentrated AI dependencies, fewer internal resources to manage vendor transitions, less negotiating leverage when vendor terms change, and less formal documentation of which workflows depend on which tools. The second misunderstanding is that switching vendors is the solution. IBM's data shows 71 percent of respondents find switching difficult. If switching is hard when planned in advance, it becomes significantly harder when forced under disruption conditions. The third misunderstanding is that buying more AI tools reduces exposure. It often adds dependencies without resolving the ones already in place. More tools without a dependency map means more surface area for vendor risk, not less.
The dependency map you do not have
Most medium-size businesses cannot answer basic questions about their current AI stack: - Which workflows would stop if a specific vendor went down for 48 hours? - Which vendors hold or process your customer data? - What is the actual cost per workflow, including API usage, licensing, and integration maintenance? - Which AI tools can be replaced in a week versus a month versus not at all? - Where does your data leave your control? These are not purely technical questions. They are operational and financial questions. The reason most businesses cannot answer them is that AI adoption happened tool by tool, team by team, solving individual problems. The stack was never designed. It accumulated. A dependency map does not need to be elaborate. It needs to cover the critical path: which AI-dependent workflows touch revenue, compliance, or customer-facing operations. That is the exposure that matters most.
The Atlacis view
IBM's research confirms what we see in practice with businesses at varying stages of AI adoption. The gap is not between companies using AI and companies not using AI. It is between companies that understand what they have built and companies operating on assumptions they have not tested. The 7 percent that operate at the highest control level are not necessarily using better AI. They use AI with better awareness of what they depend on, what the failure modes are, and what their options are if a vendor relationship changes. That is a planning discipline, not a technology advantage. For most medium-size businesses, the most valuable AI work right now is not adopting more tools. It is auditing the tools already in use: mapping the dependencies, identifying single points of failure, understanding the actual cost per workflow, and assessing which dependencies are acceptable and which create unacceptable exposure. That process takes days, not months. It does not require replacing your current AI stack. It requires understanding it. If you want to understand your current AI dependencies before a vendor change forces the conversation, that is the kind of review Atlacis does.
The short version
- IBM's June 2026 study of 1,000 senior executives found 91 percent do not fully understand their AI vendor dependencies
- Companies with the highest AI control protect 55 percent more operating profit from AI disruptions, but only 7 percent of respondents operate at that level
- 81 percent of respondents said a 7-day vendor outage would cause severe or critical business disruption
- 71 percent said switching their primary AI vendor would be difficult, and 72 percent would pay a 20 percent cost premium for better strategic flexibility
- Medium-size businesses face the same vendor dependency risks as enterprises, often with fewer resources to manage them
- The first step is not buying more AI. It is mapping which workflows you cannot run without your current AI vendors