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

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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.

Key Findings

India’s AI market is growing rapidly and has strong support tailwind
  • 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.
The 2024 AI Adoption Index 2.0 is bigger and differentiated compared to the 2022 Index 1.0
  • 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”.
India’s aggregate 2024 AI maturity is at the Enthusiast stage with a score of 2.47
  • 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 and telecom, media and entertainment (TM&E) companies have moved to the Expert stage
  • 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.
Large and SMB companies need differentiated approaches to building scale with AI
  • 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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