As companies move from pilots to deploying AI at scale, they are becoming more dependent on external providers for the components that power their AI stacks, including data, models, infrastructure, and tooling. Recent events have underscored the risks of this dependency: Access to advanced AI models can be disrupted by national policy decisions, export controls, geopolitical constraints, and other forces beyond companies’ direct control.

Such disruptions reflect a broader shift. Increasingly, governments see AI infrastructure as a strategic national asset, fueling a growing focus on AI sovereignty. In a recent white paper, Bain & Company and the World Economic Forum explored how economies can balance domestic ownership with trusted international partnerships when designing sovereign AI infrastructure strategies.

For companies, the considerations are similar: deciding which parts of the AI stack to control, which to access through trusted partners, and how to remain agile as technologies, policies, and business needs change. The objective isn’t to own the full AI stack; it’s to design a stack that remains resilient and adaptable as conditions shift. That means prioritizing flexibility, preserving optionality across external providers, and maintaining trust and compliance across markets.

Three practical questions for leaders

1. How do we design for flexibility as models, data requirements, and deployment needs change?

The first question is architectural. Many companies prioritize speed by building on integrated platforms, single-model ecosystems, or tightly coupled architectures. That may accelerate early deployment, but it can also create fragility. A resilient AI stack must be flexible enough to adapt when conditions change. This includes the ability to switch or add models, deploy workloads across different environments, process data where regulation requires it, and evolve individual components without rebuilding the full system.

For boards and executives, a practical starting point is to ask their tech leaders how flexible the company’s systems really are; how quickly they could swap a model, migrate a workload, or comply with a new data requirement; and at what cost. A Harvard Business School study found that, after the EU’s General Data Protection Regulation (GDPR) came into effect, companies with stronger modular data architectures and data portability were better positioned to absorb the impact on revenue and IT costs than peers with more rigid systems.

This doesn’t mean every company needs a fully modular, multi-cloud, multi-model architecture from day one. Instead, leading companies are making deliberate choices about where flexibility matters most. 

2. When do vendor dependencies become vulnerabilities, and how can we preserve optionality?

The second question is operational. Partnerships will remain essential for enterprises to scale AI. Few companies can or should build every layer of the stack themselves. However, reliance on external providers becomes risky when companies lose sight of where dependency is accumulating and when the conditions underpinning those dependencies can change without warning.

Vendor dependency has always carried commercial risk. Today it has also become a geopolitical concern. A government directive, an export control, or a shift in bilateral relations could make a critical capability inaccessible overnight, and no contract provides protection against that.

The question business leaders should raise is not build vs. buy, or full control vs. full delegation. It is knowing where to partner and where to diversify, such that no single dependency grows to the point where the business can no longer continue to operate if conditions change. The risks of dependency are already visible in practice: In a 2026 Zapier survey of more than 500 US executives, 74% said losing their primary AI vendor would disrupt operations. Among those who have attempted a migration, 58% said it failed or took far more effort than expected.

Companies should map their concentration across model providers, cloud infrastructure, data platforms, and orchestration tools, paying particular attention to where those dependencies cluster geographically. They should then assess whether their exit paths work in practice (not just on paper) and how portable the stack would be if vendors needed to be switched or workloads migrated. Ultimately, the goal is to maintain operational continuity when a vendor relationship changes, a model becomes unavailable, or a jurisdiction imposes new restrictions.

3. How do we ensure the AI stack stays compliant, accountable, and trusted as complexity grows?

The third question is about governance. As AI scales across markets and use cases, companies face mounting pressure to comply with evolving regulations, maintain clear accountability for AI decisions, and sustain the confidence of regulators, partners, and customers. Trust cannot depend only on the reputation of a vendor or the existence of an internal policy. It should be deliberately built and operationalized from within, early on.

This requires concrete governance mechanisms: clear decision rights and human oversight protocols, traceable accountability and escalation procedures, auditability across the full AI life cycle, pre-deployment validation, and post-deployment monitoring. Leading companies are already putting these measures in place, and the trend is accelerating. According to the World Economic Forum’s Global Cybersecurity Outlook 2026, the share of organizations assessing the security of their AI tools before deployment has nearly doubled, from 37% in 2025 to 64% in 2026.

Governance needs to be built into the AI stack from the start. Business leaders who treat it as an afterthought will struggle to scale AI with confidence. 

Designing for what comes next

For leadership teams, these questions are no longer optional. Answers will differ by company and must be considered in light of each organization’s external environment, strategy, risk appetite, and capabilities. The next phase of AI adoption will reward companies that treat resilience as a design principle, not a remediation exercise. The winners will be those that design for flexibility, anticipate critical dependencies, and embed governance and trust from the start.