Context:

• The global AI race is dominated by investments in advanced chips, data centres, computing infrastructure and frontier AI models.
• However, the economic benefits of Artificial Intelligence (AI) will depend not merely on generating intelligence but on deploying it at scale to solve practical problems.
• The World Bank’s World Development Report 2026 - ‘The Promise of Artificial Intelligence’ - highlights AI’s potential to sustain economic growth, improve productivity, strengthen public services and expand opportunities.
• For India, the priority should be to translate AI capabilities into widespread economic and social benefits rather than compete solely with global technology giants.

AI as an Engine of Economic Growth:

• The history of general-purpose technologies shows that substantial economic gains often accrue to downstream users who adopt technologies, adapt them to local conditions and diffuse them across the economy.
• India is well-positioned to benefit because of its rapid economic growth and experience in deploying digital technologies at population scale.
• Its Digital Public Infrastructure (DPI) demonstrates how technology, institutions and market incentives can reduce costs, expand access and encourage innovation.
• Therefore, India's AI strategy should focus on practical applications that improve productivity across agriculture, healthcare, education, businesses and public administration.

AI and Employment - Complementing Human Capabilities:

• The employment impact of AI is a major concern. Only 4.5% of existing jobs in low- and middle-income countries are highly exposed to automation by generative AI, compared with 14.2% in high-income countries.
• Meanwhile, 16.2% of jobs in low- and middle-income countries could experience meaningful productivity gains, compared with 18.7% in advanced economies.
• These estimates suggest that AI may complement rather than replace many workers in developing economies, although its impact will vary across occupations and sectors.
• Affordable, task-specific AI tools can -
  • Help farmers access agricultural advice and weather forecasts.
  • Assist health workers with diagnosis and disease screening.
  • Support teachers in improving learning outcomes.
  • Enhance business productivity and government administration.
  • Expand access to essential public services.

Major Challenges to AI-Led Development:

• Concentration of AI capabilities:
  • Advanced semiconductors, computing infrastructure, data, research talent and risk capital remain concentrated in a few firms and countries.
  • Excessive dependence on these providers may create technological vulnerabilities, restrict strategic autonomy and increase the risk of market dominance and technological lock-in.
• Inadequate supporting ecosystems:
  • AI's effectiveness depends on complementary investments in -
    • Reliable electricity and digital connectivity.
    • Affordable devices and computing resources.
    • Local-language datasets and relevant applications.
    • Digital literacy, technical skills and workforce reskilling.
    • Institutional capacity to evaluate, procure and regulate AI systems.
  • Without these foundations, technological progress may fail to generate development gains and could widen existing inequalities.

India's Emerging AI Applications:

• India has begun using AI to address practical challenges in key sectors.
  • Agriculture: Bharat VISTAAR, the government's digital initiative for agricultural information, aims to improve farmers' access to AI-enabled advisory services, including through multilingual applications.
  • Healthcare: Wadhwani AI's tuberculosis-screening tools demonstrate the potential of AI to support disease detection.
  • Public services: Locally adapted AI applications can improve weather forecasting, administrative efficiency and access to government services.
• The central challenge is to move beyond successful pilot projects and ensure that effective solutions are adopted by millions of people.

DPI - Lessons for AI Adoption:

• Platforms such as Aadhaar, the Unified Payments Interface (UPI), DigiLocker and emerging data-sharing systems provide shared digital infrastructure that reduces costs, enables interoperability and supports innovation.
• UPI is particularly instructive because its interoperable architecture promotes competition and reduces dependence on a single service provider.
• Similar principles should guide AI development and adoption -
  • Encourage open standards and interoperability.
  • Ensure data portability and technological flexibility.
  • Prevent excessive dependence on individual AI providers.
  • Promote competition and innovation.
• Emerging economies need not match the enormous investments made by global AI hyperscalers.
• Instead, India should build strategic capabilities through domestic research, Indian-language models, shared computing resources and a stronger deep-tech investment ecosystem.
• Initiatives such as IndiaAI; the Research, Development and Innovation (RDI) Fund; and the Anusandhan National Research Foundation (ANRF) can support this objective.

Way Forward:

• Design for scale: Every AI pilot should have a credible pathway to wider adoption, supported by sustainable financing, institutional ownership and integration with existing systems.
• Prioritise evidence-based evaluation: AI interventions should be tested under real-world conditions to assess their effectiveness, costs and development outcomes.
• Replicate success and learn from failure:
  • Successful solutions should be scaled across states and ministries, while ineffective interventions should be discontinued or redesigned.
  • Sharing evidence will prevent duplication and strengthen institutional learning.

Conclusion:

• India's AI opportunity lies not simply in adopting advanced technologies but in converting them into economy-wide productivity gains and inclusive development.
• A strategy centred on large-scale deployment, DPI, evidence-based policymaking and domestic capabilities can maximise AI's economic impact while ensuring that its benefits are distributed more equitably.