India’s AI adoption surges as 47% of enterprises scale multiple GenAI use cases

India’s enterprise AI ecosystem has entered a decisive phase of acceleration, as revealed in the latest EY–CII report The AIdea of India: Outlook 2026. Nearly half of Indian organisations now run multiple Generative AI use cases in production, signalling a clear movement from pilot testing to performance-led deployment. An additional 23% remain in pilot mode, reflecting widespread interest in operationalising the technology.

Business confidence in AI continues to rise, with 76% of leaders expecting a significant business impact and a majority expressing readiness to embed GenAI into core workflows. The report notes that enterprises are increasingly steering their transformation strategies towards agentic AI frameworks, which are becoming central to large-scale automation efforts across industries.

While optimism is high, investments remain conservative. More than 95% of companies allocate under 20% of their IT budgets to AI, creating a gap between conviction and financial commitment. This imbalance is beginning to shape how effectively organisations realise returns from AI programmes. As enterprises deepen AI adoption, many are moving towards a broader five-dimensional ROI framework that factors efficiency, time, business upside, differentiation and resilience.

Speed has emerged as a dominant priority, with 91% of leaders identifying rapid deployment as the primary factor influencing buy-versus-build decisions. In the year ahead, spending is expected to focus on operations, customer service and marketing as organisations embed AI into high-impact business functions.

Partnerships with OEMs and startups are becoming essential, with close to 60% of organisations co-innovating with young technology firms. Hybrid models now account for 78% of AI execution, reflecting the need for agility and accelerated experimentation.

The report also outlines a shifting workforce structure. While 64% of enterprises are reshaping roles involving standardised tasks, a shortage of skilled AI talent persists. Organisations are progressively adopting AI-first architectures of work, where human expertise and machine intelligence are combined to elevate decision-making and operational precision.