What national AI plans get wrong and how to fix them

National AI plans in most countries seek to replicate a full domestic technology stack that includes computing resources, chips, foundation models and overarching regulations. This generalized model overlooks how artificial intelligence can best serve each nation's distinct industries, workforce skills, institutions and position in global supply chains, according to insights from the India AI Impact Summit. Discussions at the summit highlighted risks of duplication and incompatible standards when countries pursue fully sovereign AI systems. Instead, the emphasis fell on interoperability, cross-border cooperation on data, evaluation frameworks and governance, and specialization in areas that match existing economic strengths. AI functions as cognitive infrastructure that links data, human expertise and systems to raise productivity and support long-term competitiveness, the analysis stated. Funding patterns from 2014 to 2025 show concentration in infrastructure, models and research, with clear regional differences: the United States spans many domains, India focuses on fintech and edtech, Europe on enterprise software and medical applications, and East Asia on manufacturing and vision systems. Countries should identify where AI reinforces current advantages, such as robotics in energy for Norway or automotive systems for Germany, and pursue adjacent diversification from that base rather than building capabilities in isolation. This approach treats AI as a tool to enhance the real economy through jobs, efficiency and growth aligned with local conditions.