For most of the last three decades, global business leaders could treat geopolitics as background noise. The dominant questions were commercial: where to find new growth, how to build global brands, and how to extract the maximum value from sprawling supply chains. Today, the mood is very different. Artificial Intelligence (AI) is transforming how value is created and captured at the same time as the political environment is becoming more fragmented and more suspicious of unfettered flows of data, talent, technology, and capital. The result is not just more uncertainty, but a qualitatively different kind of uncertainty: one in which business strategy, national security, and debates over values are visibly entangled.
AI exemplifies this new entanglement. AI capabilities have become both a source of new security threats and a key tool of the national security apparatus. As a dual-use technology, governments increasingly see advanced models, training data, and compute infrastructure as strategic resources, even when they were developed for commercial purposes. Export controls on high-end chips, debates over open-source foundation models, and competing regulatory regimes are forcing companies to ask not just what they can build, but where, with whose data, and in accordance with which values.
This shift is particularly stark for the large multinational firms that once thought of themselves as global corporate citizens. In the years following the end of the Cold War, customers, workforces, and supply chains became ever more international, and the location of corporate headquarters seemed to matter less and less.
If something made economic sense in a global market, it was usually assumed to be politically acceptable as well. That assumption is now hard to sustain. Over the past decade, more assertive national agendas, contested multilateral institutions, and a renewed focus on security and sovereignty have begun to reshape where production can take place, how supply chains must be structured, and which business strategies societies will tolerate – particularly around data and AI infrastructure. Multinationals are being pulled back from an abstract “global” identity into concrete national and regional ecosystems.
There is one sector that has never enjoyed the luxury of thinking in purely global-market terms: defense. Defense organizations and the industries that support them have always operated under tight national constraints. Defense leaders are used to living with contested geopolitics, to treating technology as a strategic capability rather than a neutral tool, and to planning for crises that may never come. As leaders rethink their AI and digital strategies for 2026 and beyond, two practices from the defense world are especially worth adapting: how defense companies position themselves within national and regional ecosystems, and how contractors and the militaries they support build systems and architectures that outlast any individual.
“AI has already become an arena in which this kind of positioning matters.”
Business leaders have long been comfortable thinking in terms of “ecosystems”: networks of partners, platforms, and customers that create value together. In the last decade of the 20th century and the first two decades of the 21st century, the implicit picture was a single global web. Today, that vision of a unified global ecosystem is breaking down. Around critical technologies, infrastructure, and resources in particular, we are seeing the emergence of new ecosystems based on national and regional interests. Ports and logistics hubs, energy grids, semiconductor fabs and AI chips, cloud and telecoms infrastructure, and key materials are no longer being treated simply as commercial assets; they are increasingly viewed as key components in national security policies and industrial development strategies.
For multinationals, especially in strategically sensitive sectors such as energy, finance, and technology, this shift is no longer abstract. While global supply chains and international customer bases remain, businesses increasingly find themselves belonging to a set of national or bloc-based systems with hard edges. China’s dominance in rare earths, concerns about over-reliance on Taiwan’s semiconductor industry, conflicts over access to COVID-era vaccines, and U.S. restrictions on high-end chip sales to competitors have all made one point clear. Questions such as “Which government has the right to guide and limit our actions?” and “Where do we really belong?” are now returning to the foreground of business strategy. In practical terms, many companies will find that they cannot remain equally embedded everywhere. They will be expected to deepen their ties in some jurisdictions, loosen them in others, and, in certain parts of their business, make an explicit choice about whose rules and priorities come first – not least when those choices concern AI infrastructure and data.
One striking feature of the defense industry is how openly its major contractors describe and conduct themselves as national assets. Their strategy documents and public messaging rarely present them as neutral global firms that simply happen to have contracts in a given country; instead, they emphasize their contribution to national security, technological sovereignty, and domestic employment, and frame their capabilities as contributing to the country’s long-term resilience. That positioning is not just rhetoric: it shapes where they place facilities, which partnerships they build, and how they respond when national priorities shift.
Over time, the stance is reinforced by how they operate on the ground – investing in local supply bases, co-funding research with universities, building long-term training pipelines, and adapting products to local doctrines and regulatory preferences. This web of relationships makes them hard to dislodge and gives governments a stake in their success. For multinationals in other strategically sensitive sectors, there is a clear lesson: in some markets, it will be more realistic – and ultimately safer – to behave as deeply embedded contributors to a shared national project than as detached global operators who simply happen to do business in a country.
AI has already become an arena in which this kind of positioning matters. Reliance on global providers for GPUs, datasets, and cloud services once seemed innocuous; now, national security reviews scrutinize these dependencies as potential vulnerabilities. A technology firm might build products on internationally developed models yet face pressure to localize training in “friendly” jurisdictions, keep certain categories of data onshore, or integrate with state-approved frameworks for “trustworthy AI”.
In response, defense and security agencies in many countries are working with industry and universities to develop more sovereign AI capabilities – from language models tuned to local legal and cultural norms to analytic systems certified for compliance with national regulatory regimes. For commercial businesses, the implication is similar: AI strategies will need to signal clear “ecosystem allegiance,” using local partners not just for market access but to demonstrate compliance, resilience, and alignment with national expectations about how sensitive technologies are governed.
Defense contracting also shows that firms do not have to make a binary choice between being globally capable and locally acceptable if they are thoughtful about their partnerships. Many global technology providers draw heavily on technology and talent from around the world, yet still play key roles in national defense ecosystems because government-focused contractors sit between them and the state, acting as a translation layer that handles adaptation, compliance, and the day-to-day relationship with government agencies. For multinationals under pressure to demonstrate loyalty to particular jurisdictions, a similar pattern can be attractive. By using specialist partners to localize offerings, align with regulatory and security expectations, and connect to national value chains – including for AI and data-intensive services – they can remain plugged into global innovation while behaving, where it matters, as long-term participants in the national project rather than footloose visitors.
With the global environment becoming harder to predict and manage, internal continuity and reliability matter more than ever.
With the global environment becoming harder to predict and manage, internal continuity and reliability matter more than ever. As organizations have become more interconnected and complex, their internal systems have also become more fragile. Frequently, too much now depends on a few special individuals, a reversal of a trend in leadership styles that had persisted for decades. In many companies, a handful of “heroes” carry the knowledge, relationships, and tacit understanding that make key parts of the business work: the one executive who can unlock budget, the one engineer who really understands how a critical system behaves, the project manager who can keep a complex transformation on track, the superstar CEO who knows the strings to pull to improve market sentiment and supercharge the stock price. When they move on, initiatives stall, governance frays, and confidence evaporates. The question is how to build organizations that remain resilient when individuals change.
Defense organizations actively work to prevent the special individual or the unique leader from defining a unit. Officers rotate posts every few years in peacetime; in a conflict, key leaders may be lost overnight. Systems must therefore be built on the assumption that no one is indispensable. To cope, modern militaries embed highly disciplined knowledge management mindsets into their everyday practice: specialists are responsible for capturing doctrinal learning and ensuring its wide transmission, continuity materials spell out all the key functions of roles, while detailed formal handover processes ensure that projects and operational plans are not disrupted by frequent changes in staffing.
Crucially, these knowledge systems are purpose-driven, not built on an abstract ideal of universal access. Units organize information around specific missions and responsibilities: what a new commander must know on day one, what a logistics team needs to ensure a safe handover, what a staff officer requires to contribute to planning exercises. Silos are used deliberately to protect security and to keep sensitive knowledge closely aligned with its intended use, with attention focused on defining the right boundaries and bridges rather than on eliminating silos.
For businesses, this approach offers a valuable model that departs radically from many of the dogmas that underpin current knowledge-management efforts. Instead of trying to capture everything for everyone in a single repository, leaders should start by identifying the points at which continuity matters most: the roles whose turnover routinely derails projects, the processes that collapse when one person leaves, the decisions that are repeatedly revisited because nobody can see the history. Around those points, they can then design targeted knowledge flows and supporting architectures: concise continuity materials that every successor is expected to use; clear decisions about which information must be visible across functions and which should remain compartmentalized; and an up-to-date map of systems and processes that shows where responsibilities and information actually sit. New AI-powered tools that can surface and summarize internal knowledge in seconds do not remove the need for this discipline. On the contrary, their value depends entirely on what has been documented in the first place and on a purpose-driven approach to defining access needs.
Used carelessly, those same tools can introduce new fragilities. Models trained on partial or poorly governed datasets may become unreliable as people and processes change; systems that make it easier to query sensitive information can expose more than they should if access is not tightly controlled; AI-driven workflows can become opaque if nobody is accountable for how they evolve. Here again, defense offers a useful mindset. AI systems that support core operations should be treated as mission-critical assets, with clear ownership, version histories, and audit trails. Access should be structured around roles and responsibilities rather than left to informal practice. Leaders should periodically rehearse “loss of key personnel” scenarios – asking what would happen to critical AI-enabled processes if particular individuals left – to check that knowledge still flows when people move on. The aim is to turn AI from a hero-dependent accelerator into a durable backbone for organizational memory.
We are now entering a new era in which national interests, technological capabilities, and social values are increasingly shaping the way multinational companies do business.
The era of the neutral global corporate citizen is fading. We are now entering a new era in which national interests, technological capabilities, and social values are increasingly shaping the way multinational companies do business. AI sits squarely at the center of this shift, both as a source of competitive advantage and as a focus for concerns about security, sovereignty, and societal impact.
The defense sector offers a set of disciplines that other industries cannot afford to ignore. Thinking of your business as a part of specific national and regional ecosystems, rather than as a free-floating global actor, and building systems, architectures, and knowledge flows that keep working when star individuals move on, are becoming necessities in the fractured environment that is now emerging. The lesson is not that companies should become more militarized in their culture or rhetoric, but that they should borrow from a sector that has long been forced to live with high stakes, contested geopolitics, and deep uncertainty.
For AI in particular, the implication is to stop treating it as a generic plug-and-play disruptor and start treating it as a sovereign capability that is anchored in trusted ecosystems and stewarded with a healthy dose of skepticism about both its promises and its risks. In a fractured world, firms that approach AI in this way will not only be better placed to withstand shocks; they will also be better positioned to shape the next wave of resilient innovation.
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