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NASSCOM AI Adoption Index 2022

Investments in Artificial Intelligence in India is still a small percentage (~1.5%) of the total worldwide spend. However, given its transformative nature and the rapid strides India has made in this space over the past few years, it is safe to state that the success of India’s Techade hinges on adoption of best-practices to convert this potential into tangible national value. Considering the centrality of Artificial Intelligence as a technology and its contribution to India’s GDP, it is important to track, assess and monitor its adoption at the enterprise level. NASSCOM have collaborated with Microsoft, Capgemini, Ernst & Young and EXL to bring the NASSCOM AI Adoption Index, the first-of-its-kind assessment in the Indian context. This comprehensive maturity assessment framework tracks and measures the degree of AI penetration in India and arrives at a composite score both at the country and sector levels.

India is positioned as an ‘Enthusiast’ on the 4-stage maturity scale with an aggregate score of 2.45; ‘making AI real’ is still some time away

  • Four key sectors – Industrials and Automotive, Healthcare, Retail & CPG and BFSI are expected to contribute ~60% of the potential AI-driven value add to India’s GDP by FY 2026
  • India starts from a low base in terms of investment volumes but scores significantly higher than mature economies in AI skills penetration; 3.09 times the global average between 2015-2021
  • End-users see value in moving ahead on the AI adoption curve but are hamstrung by legacy systems and silo-ed data

Most end-users are at the middle stages of the four-stage journey; incremental improvements needed to transition to “Expert’

  • AI strategy is not yet embedded with the broader corporate strategy for the majority; currently <15% have aligned their goals
  • Enterprise spending on Artificial Intelligence is still low; 67% allocate less than 10% of their IT budget to AI
  • 50% need adequate data at the BU level to develop data standards
  • Building dedicated teams for AI (particularly is limited to less than 40%

The Industrials & Automotive and CPG & Retail have higher relative scores among the four sectors. Diversity in choices indicate need for sector-specific strategies

  • Except for the Healthcare sector, the other 3 sectors score high on having a well-defined AI strategy
  • Majority of the companies across sectors have active POCs and defined use-case across multiple functions
  • Optimizing cost of operations is driving the majority to adopt AI; revenue growth and innovation also score higher

Impediments to adoption of AI varies greatly with the sectors; analysis reveal user inability to continually assess AI value delivery. The major challenges are

  • 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
    The Way Forward

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.

CT Bureau

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