AI Adoption Index 2.0: Tracking India’s Sectoral Progress in AI Adoption

The 2024 AI Adoption Index 2.0 highlights the significant advancements and opportunities in India's AI landscape amid rapid AI technology evolution. This study offers in-depth insights into the AI adoption trends across seven sectors and 500 surveyed companies assessed on a nine-dimensional AI adoption framework.

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Key Findings

India’s AI market is expected to grow at a 25-35% CAGR by 2027.
Strong national-scale support pillars have emerged in promoting AI adoption – IndiaAI mission, supportive policy framework, AI and GenAI-ready tech services, AI-focused startup ecosystem, and the 2nd largest installed AI talent base.
Rapid digitalization by enterprise users – 70% spend over 20% of IT budgets on digital.

Coverage across seven sectors representing 75% of India’s GDP, 500 companies, and nine dimensions, compared to four sectors, 315 companies, and six dimensions in 2022.
Index 2.0 assesses enterprise AI execution ability over strategic planning alone.
Index 2.0 focuses on pushing the objective of “Building Scale with AI”.

87% companies are in the middle stages of Enthusiast and Expert AI adopters.
2X rise in the number of companies in the Expert stage indicating significant progress.
Factors that have improved – data standardization, modernization of tech stack, Cloud adoption, and more formalized AI risk and ethics frameworks.
Factors that need urgent attention – consistent leadership commitment driving up budgets and execution discipline, use case selection, and AI-led innovation

Manufacturing led by automotive and electronics, while TM&E sector maturity is driven by digital-native business models.
Sectors generally struggle with AI use case identification, while some leapfrog to PoC and production rapidly.
More mature sectors have central IT teams leading AI efforts.
Healthcare, a data-sensitive sector, majorly lacks these AI risk management frameworks.

Both need certain common strategi actions to leapfrog from AI-ready to AI-first.
Large enterprises should prioritize data standardization, strategic partnerships, swift PoC-to-production, balanced AI use for both efficiency and innovation, and risk-managed sustainable AI strategies.
SMBs should emphasize contextual use cases, partnerships with tech SMEs, strong leadership drive, knowledge of data regulations, and peer learning to overcome adoption barriers.

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