23 - 05 - 2026

India strong on AI capacity, lag in enterprise conversion: Karnataka Digital Economy Mission

B D Narayankar

India’s artificial intelligence (AI) ecosystem is witnessing rapid expansion in talent, infrastructure and policy support, but the country continues to face a critical challenge in converting this momentum into scalable enterprise impact, Karnataka Digital Economy Mission CEO Sanjeev Kumar Gupta said.

Speaking at a closed-door media roundtable on “India’s AI Moment: Enterprise Prioritisation Behind Scalable Impact” organised by Avaali Solutions, Gupta said India today stands at a strong inflection point in the global AI race, supported by large-scale investments, a growing startup ecosystem, and one of the world’s largest AI talent pools.

He pointed to initiatives such as a ₹10,300 crore enterprise AI mission, rapid expansion of GPU infrastructure, and the emergence of AI-focused innovation clusters across the country. Karnataka, he noted, continues to play a leading role, hosting a significant share of India’s global capability centres (GCCs) and deep-tech talent.

However, Gupta cautioned that despite these structural advantages, enterprise-level AI adoption remains uneven, with most organisations still operating in pilot or experimental phases rather than achieving production-grade deployment at scale.

“The real challenge is not adoption. Enterprises are already interested and willing to invest in AI. The issue lies in prioritisation—where to invest, how to build governance systems, and how to ensure data and business processes are AI-ready,” he said.

He observed that while AI discussions have become widespread across boardrooms and technology teams, execution often remains fragmented. As a result, only a small proportion of AI initiatives are translating into measurable productivity gains or revenue impact.

Gupta emphasised that AI must move beyond being a technology conversation confined to CIOs and CTOs, and instead become a board-level strategic priority directly linked to business outcomes. “If AI does not become a boardroom agenda tied to measurable value creation, it will remain tactical and limited in scale,” he said.

He identified three structural bottlenecks slowing enterprise AI transformation—lack of data readiness, weak integration between domain expertise and AI capabilities, and insufficient board-level accountability for outcomes. The discussion also highlighted governance and data quality challenges, with Ohmna Sinha, Global Head – Data & Analytics Governance at Nielsen, stressing the importance of strengthening data foundations before scaling AI deployment.

She advocated a “data-quality-first, AI-faster” approach, arguing that organisations must prioritise clean, structured and reliable data systems to ensure sustainable AI success. Gupta further said that India’s AI ecosystem is strong on the supply side, with robust infrastructure development, funding support, and innovation activity.

However, he warned that the demand side—enterprise execution and prioritisation—must catch up to realise the full economic potential of AI. He also called for greater transparency from large enterprises in sharing successful AI use cases, saying that visible outcomes would help MSMEs, policymakers and industry stakeholders better align with practical implementation strategies.

“The challenge for India is not capability, but conversion—turning AI capacity into consistent enterprise impact,” Gupta said, summing up the central theme of the discussion.