IBM Study: Limited Control and Rising Dependencies Leave Enterprises Exposed in the Age of AI

2026-06-17 · Source: IBM - Announcements (Artificial intelligence) · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management · Depth: Fundamental Awareness, short

Summary

A new global study by the IBM Institute for Business Value, released on June 17, 2026, reveals that enterprises are increasingly exposed due to limited control over their AI systems and rising dependencies. The study, based on insights from 1,000 senior executives across 16 countries and 17 industries, found that 71% of respondents face difficulty switching primary AI vendors or models. A significant 68% struggle with data residency and sovereignty requirements, while 91% do not fully understand their AI dependencies across vendors, models, and infrastructure. This lack of visibility limits risk assessment, with surveyed leaders reporting an average of six AI-related disruptions in two years. Organizations with advanced AI control capabilities protect 55% more operating profit from AI-driven disruptions, yet only 7% operate at this level. Most (73%) maintain multi-vendor AI environments, often driven by independent business unit decisions (69%) and geographic necessity (69%) rather than deliberate strategy.

Key takeaway

For Directors of AI/ML and VPs of Engineering managing complex AI deployments, you must prioritize understanding and mitigating AI dependencies. Your organization's operational continuity and profitability are at risk from vendor outages, price changes, and model deprecations. Implement strategies to enhance AI sovereignty, focusing on adaptable systems and clear oversight across data, models, and infrastructure. This proactive approach will protect your operating profit and ensure compliance amidst evolving AI ecosystems.

Key insights

Unmanaged AI dependencies and lack of control expose enterprises to significant economic and operational risks, making AI sovereignty critical.

Principles

• AI dependencies evolve faster than traditional governance.
• Loss of AI control directly impacts margin and compliance.
• Adaptable AI systems reduce downtime and protect profit.

In practice

• Prioritize understanding AI vendor and model dependencies.
• Design AI systems for adaptability and flexibility.
• Strengthen control over data, models, and infrastructure.

Topics

• AI Sovereignty
• AI Dependencies
• Enterprise AI
• Risk Management
• Vendor Management
• Data Residency

Best for: CTO, Director of AI/ML, VP of Engineering/Data, Executive

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Editorial summary, takeaway, and curation by AIssential. Original article published by IBM - Announcements (Artificial intelligence).