Context: The World Development Report 2026: The Promise of Artificial Intelligence highlights AI’s potential to accelerate economic growth, improve productivity and expand opportunities. However, its benefits depend on wider adoption, supporting infrastructure, institutional capacity and equitable distribution of gains.
AI and Economic Growth: Beyond Frontier Models
- Concentration of benefits: Investment in advanced chips, data centres and powerful AI models is concentrated among a handful of firms and countries, raising concerns that economic gains may accrue disproportionately to technology owners and upstream suppliers.
- Importance of downstream adoption: The wider economic value of AI emerges when businesses, governments and households adopt the technology, adapt it to local needs and integrate it into everyday activities.
- India’s advantage: India has demonstrated the ability to deploy digital technologies at population scale. Its experience shows how technology, institutions and market incentives can reduce costs, broaden access and encourage innovation.
AI’s Impact on Employment and Productivity
- Uneven exposure to automation: The article cites estimates suggesting that 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; another 16.2% of jobs in developing countries could experience meaningful productivity gains, compared with 18.7% in advanced economies.
- AI as a complement to labour: In developing economies, affordable, task-specific AI tools may assist workers rather than replace them—for example, by helping farmers access advice, health workers improve diagnosis, teachers support learning and officials deliver public services.
- Policy implication: Governments should focus on enabling productivity-enhancing adoption, workforce skills and access to AI tools rather than treating AI primarily as a race to automate jobs.
Two Major Challenges to AI Adoption
- Concentration of technology: Advanced chips, large-scale computing, data, research talent and risk capital remain concentrated among a limited number of firms and countries. This can restrict access, increase dependence on technology providers and create market power.
- Missing complementary infrastructure and institutions: AI's usefulness depends on electricity, connectivity, affordable devices, local-language data, skills and effective institutions. Governments must also develop the capacity to evaluate, procure and regulate AI systems; technology alone cannot generate development gains.
India’s Opportunities for Inclusive AI
- AI for public services: Locally adapted AI applications can strengthen agricultural extension, weather forecasting, health diagnostics and access to public services.
- Bharat VISTAAR: The Government of India’s digital platform for farmers aims to integrate agricultural information and services, with AI-enabled assistance offering potential benefits for agricultural decision-making.
- Wadhwani AI: Its tuberculosis-screening tools illustrate how AI can support health workers and improve the reach of diagnostic services.
- Digital Public Infrastructure (DPI): Aadhaar, the Unified Payments Interface (UPI), DigiLocker and emerging data-sharing systems demonstrate how interoperable platforms can reduce costs, enable large-scale access and support innovation.
- UPI as a model: Its open architecture has encouraged competition while reducing dependence on a single provider. Similar principles can guide AI adoption through portability, flexibility and interoperability.
Three Principles for Maximising AI’s Economic Impact
- 1. Design for scale: AI pilots should have a credible pathway to widespread adoption, including financing, institutional ownership and integration with existing systems.
- 2. Test rather than assume: Decisions should be evidence-based. India’s scale provides opportunities for rigorous, relatively inexpensive evaluation and experimentation under real-world conditions.
- 3. Scale what works, stop what does not: Successful interventions should be expanded across States and ministries, while failures and lessons should be documented to avoid repeatedly testing ineffective approaches.
Strategy for Emerging Economies
- Build domestic capabilities: Emerging economies need not match the massive investments of global AI leaders in frontier models; they should develop strategic capabilities aligned with domestic needs.
- Promote locally relevant innovation: Priorities include domestic research, Indian-language models, shared computing resources, access to quality data and stronger deep-technology investment ecosystems.
- Ensure broad-based gains: The objective should be to move from isolated AI applications to economy-wide productivity improvements, ensuring that benefits reach businesses, workers, farmers and public institutions.
Way Forward
- Expand foundational infrastructure: Improve reliable electricity, broadband connectivity, affordable computing access and digital literacy.
- Strengthen human capabilities: Integrate AI skills into education, vocational training and professional development to enable workers to use AI effectively.
- Create enabling institutions: Develop procurement standards, regulatory capacity, data governance frameworks and mechanisms for evaluating AI systems.
- Encourage competition and interoperability: Avoid technological lock-in by promoting open standards, portability and systems that can integrate with existing digital infrastructure.
- Measure outcomes: Evaluate AI projects through productivity, cost reduction, service quality, adoption and distributional impact rather than the number of pilots or models developed.
Additional Information
1. World Development Report
- Published annually by the World Bank, the report examines major development issues and provides research-based analysis relevant to policymakers.
- The 2026 edition cited in the article focuses on the promise of artificial intelligence and its implications for development.
2. Digital Public Infrastructure (DPI)
- Meaning: Shared digital systems that enable essential services and transactions at scale, often through interoperable platforms.
- Examples in India: Aadhaar for digital identity, UPI for digital payments and DigiLocker for digital document access.
- Significance: DPI can reduce transaction costs, widen access, encourage innovation and support AI adoption when combined with appropriate data safeguards, cybersecurity and institutional capacity.
