In 2025, it is critical for companies to expand their AI strategy beyond the US and China to mitigate geographic concentration risk. A recent Harvard Business Review article by Yasuhiro Yamakawa and Thomas H. Davenport highlights that AI is already a multipolar field. Businesses that localize algorithms, talent, and infrastructure achieve faster deployments and stronger regulatory resilience.
The takeaway for executives is clear: instead of following a generic superpower playbook, they must strategically map the unique AI advantages of different countries.
Country strengths at a glance
- Japan: Excels in robotics integration for manufacturing and elder care.
- Canada & France: Lead in trustworthy AI research and policy development.
- United Arab Emirates: Offers capital-intensive data infrastructure funded by sovereign wealth.
- India: Provides vast, multilingual talent pools and robust digital public goods.
- Sweden & Norway: Supply low-carbon energy ideal for powering hyperscale data centers.
A global AI strategy reduces over-reliance on any single nation, mitigating geopolitical and regulatory shocks. By tapping into specialized hubs for talent, infrastructure, and innovation – from Japanese robotics to Indian tech talent – companies can accelerate growth, improve resilience, and capture emerging opportunities in the multipolar AI landscape.
Robotics shows the specialization dividend
The value of specialization is evident in robotics. The industrial robotics market outside the U.S. and China is set to reach nearly $50 billion in 2025, per ABI Research. Niche leaders like Japan’s Fanuc, Switzerland’s ANYbotics (autonomous inspection), and Europe’s KUKA (collaborative robots) demonstrate this trend, allowing global firms to source best-in-class technology without vendor lock-in.
Partnerships cut concentration risk
Strategic partnerships are essential for reducing concentration risk. For example, the OpenAI-Accenture alliance equips consultants with advanced AI tools, yet Accenture retains other model providers to ensure client flexibility. This multi-vendor approach is also seen in chip supply, with NVIDIA, AMD, and Broadcom co-investing in regional clouds across Europe to diversify compute resources.
A short checklist for executives seeking similar resilience:
- Benchmark local energy, talent, and regulatory scores before site selection.
- Pair engineers with anthropologists to align AI interfaces with cultural norms.
- Sign dual cloud or chip agreements to hedge supply shocks.
- Rotate pilot projects through universities in at least two regions per year.
Localization beats replication
Simply replicating a successful AI model in a new market is a recipe for failure. For instance, a U.S.-centric hiring algorithm faltered in Tokyo due to cultural and linguistic nuances like kanji name ordering. This highlights a crucial point: AI systems are sociotechnical and must be localized to align with cultural expectations.
Market implications
With the global AI market projected to hit $254.5 billion in 2025 (McKinsey), there is ample space for multiple national champions. By 2028, agentic AI workflows will be integral to a third of enterprise software. Companies that diversify their AI footprint now will gain early access to these advances, while those confined to one region risk falling behind as global standards evolve.
Ultimately, a resilient and scalable global AI strategy is built on three pillars: regional specialization, distributed infrastructure, and cross-border talent sharing. This approach is the most effective way to reduce exposure to geopolitical shocks and drive sustainable growth.
What happens if my company keeps focusing AI strategy only on the US and China?
Concentration risk is rising. In 2025, more than 65 % of enterprises already use generative AI regularly-up from 33 % in 2023-doubling dependency on the same two talent and infrastructure pools. A single geopolitical or regulatory shock can stall releases, inflate costs, and force emergency re-platforming. Mapping AI strengths country-by-country and signing secondary partnerships in Japan, Canada, or the EU reduces single-vendor and single-nation lock-in while giving you fallback chips, models, and hosting capacity.
Which countries offer ready-to-use AI capabilities that we can plug in right now?
- Japan leads industrial robotics and elder-care AI; Nvidia-Fujitsu just agreed on next-gen factory robots, and Bear Robotics opened a Tokyo hub to serve hotels.
- Germany plus Switzerland dominate collaborative robots (cobots) for shop-floor safety; cobot revenue will jump from USD 1.3 bn to USD 7 bn by 2030.
- Canada, UK, France supply trustworthy-AI research and regulation blueprints you can pilot before your home market legislates.
- France, Sweden, Norway sell surplus zero-carbon electricity-ideal for GPU clusters that face ESG pressure elsewhere.
- India, Israel, Singapore, Switzerland host large bilingual AI talent pools outside the superpowers.
How do we localize AI so it actually works in each market?
One-size-fits-all algorithms fail. A US hiring engine stumbled in Japan because it ignored cultural preference for team-based fit. Build multi-disciplinary squads (anthropologists, local SMEs, ethicists) and co-create with start-ups, universities, civic groups on interface language, autonomy level, and risk tolerance. Treat each country as a distinct product line with its own data governance, not a translation layer on a single codebase.
What concrete steps cut supply-chain risk and speed up innovation?
- Multi-vendor silicon: balance AMD and NVIDIA compute, and tap the USD 500 bn TSMC-NVIDIA foundry alliance so shortages in one node don’t stall training.
- Regional data-center consortia: join France-Italy-UK-Spain sovereign AI stacks or UAE’s Stargate to distribute workloads and comply with data-sovereignty laws.
- Partner with integrators that already mix platforms: Accenture plus OpenAI now delivers agentic AI playbooks while keeping competing models on tap for clients.
- Insert M&A scanning: 2025 AI deal value is up 242 % year-over-year; buying niche providers in robotics or reasoning models widens capability faster than organic R&D.
How big is the upside if we diversify now?
The global AI sector will reach USD 254.5 bn in 2025 and add an estimated USD 4.4 trn in productivity by 2030. Enterprise spend on agentic AI alone rockets from < USD 1 bn in 2024 to USD 51.5 bn by 2028, a 150 % CAGR. Teams that map specialized national assets today lock in lower compute prices, first-access to cobot hardware, and compliant frameworks before markets tighten, gaining cost and speed advantages that late movers will pay premiums to replicate.
Read the full argument in Your AI Strategy Needs to Expand Beyond the U.S. and China on Harvard Business Review.
