Advancing India’s AI Sovereignty with a Strategic Evaluation Framework

Sarah J · 2026-02-26T17:20:42.000Z

In a thought-provoking analysis published in Forbes, technology strategist Janakiram MSV argues that India’s pursuit of AI sovereignty must go beyond building indigenous large language models or foundational AI systems. Instead, the country needs a measurable, transparent “scoreboard” of AI capability one that evaluates ecosystem depth, infrastructure readiness, data governance, talent pipelines and industry adoption alongside model performance. The article contends that sovereign AI is not defined solely by the existence of a domestically developed model. Rather, true sovereignty rests on the ability to control and scale the entire AI value chain from semiconductor access and cloud infrastructure to datasets, safety frameworks, regulatory clarity and enterprise integration. India has increasingly focused on developing indigenous AI capabilities, reflecting broader global conversations around digital sovereignty. However, Janakiram notes that AI ecosystems are multi-layered. Sovereignty requires: Access to compute infrastructure and advanced chips Availability of domain-specific datasets Strong AI research institutions and talent pipelines Clear regulatory and governance standards Widespread industry adoption and commercial deployment Without a systemic evaluation framework, measuring progress becomes difficult. A scoreboard approach would provide benchmarks across these dimensions, allowing policymakers, industry leaders and startups to track gaps and opportunities. The article emphasises that India’s AI ambitions must be supported by investments in data centres, GPU clusters, open-source research collaboration and cloud-native platforms. Equally important is talent not only in machine learning research, but in applied AI engineering, cybersecurity, compliance and responsible AI design. Janakiram also highlights the need for coordinated action between government, academia and private industry to avoid fragmentation. AI sovereignty, he argues, is as much about ecosystem cohesion as technological capability. A national AI scoreboard would assess metrics such as: Number of AI startups and funded ventures Indigenous chip and hardware capacity AI patent filings and academic publications Enterprise AI adoption rates Regulatory readiness and safety frameworks Cross-border AI collaborations Such a framework would move the conversation from symbolic achievements to measurable competitiveness positioning India strategically in the global AI race. Ultimately, AI sovereignty is not a zero-sum national pursuit but a strategic capability shaped by alliances, ecosystem maturity and shared governance models.

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Advancing India’s AI Sovereignty with a Strategic Evaluation Framework | SEINET