Context: Recently, NASSCOM AI Adoption Index 2022 has been released which says that AI adoption in four key sectors -- BFSI, consumer packaged goods (CPG) and retail, healthcare, and industrials/automotive  can contribute 60 per cent of the total $ 500 billion opportunity.

Key highlights

• The adoption of artificial intelligence (AI) and data utilisation strategy can add $500 billion to India's GDP by 2025.
• Banking, financial services and insurance (BFSI), consumer packaged goods (CPG) and retail, healthcare, and industrials and automotive are the four key sectors of Artificial Intelligence.
• They contribute 60% of the total $ 500 billion opportunity.
• The current rate of AI investments in India is growing at a compound annual growth rate (CAGR) of 30.8% and supposed to reach $881 million by 2023.
• It will only account for 2.5% of the $340 billion in total worldwide AI investments.

Role of India

• India needs a significant association between the adoption maturity of AI for it to reach its $1 trillion GDP objective by FY 2026-2027.
• 65% of organisations have AI strategy defined either at a functional or enterprise level.
• India currently is the second largest global hub in training and hiring AI talent.
• As per the findings, 44% of companies already have a dedicated or cross-functional AI team structure, while 25% completely rely on outsourcing as their main source for AI skills.
• The use of AI in improving healthcare systems can potentially generate $25 billion of economic value add for India by 2025.
• India has a poor starting point in terms of investment quantities, but its level of AI skills adoption is 3.09 times higher than the world average between 2015 and 2021, exceeding developed nations.

Challenges

• Quantifying the benefits of AI
• Low maturity of the external ecosystem
• Budgetary constraints
• Cultural and behavioral challenges
• Mature enterprises track the return on investments and PoC to production (35%), 45% track advanced digital RoI metrics.

Road Ahead

• Continual impact assessment of AI projects
• Integrating technology and business architecture
• Streamlining IT and business alignment to weed out legacy
• Continual upskilling in AI technologies ‘and’ domain
• Effective lab-to-market for value-driven innovation