AI & Technology 2047: Challenges, Priorities and an Action Agenda

Executive summary

AI can raise productivity and improve public services, but compute, skills, trustworthy data and governance must develop together. Build shared compute and evaluation infrastructure; multilingual public-interest datasets; applied AI labs; procurement and risk controls. This working paper proposes a phased, measurable agenda for ai & technology and identifies evidence that expert contributors should test before a final policy recommendation is published.

1. Problem definition and scope

The mission focuses on ai & technology as a determinant of India's living standards and long-run resilience. Progress cannot be judged by a single headline number: outcomes must be assessed across regions, genders, income groups and access to opportunity.

Three priority constraints shape this research agenda: AI skills and compute; Responsible use; Research commercialization. Each requires a baseline, causal diagnosis and an explicit view of which levers belong to national government, state government, municipalities, enterprises or civil society.

2. Key research questions

1. What is the scale and distribution of ai skills and compute, and which public datasets can establish a reproducible baseline?
2. What is the scale and distribution of responsible use, and which public datasets can establish a reproducible baseline?
3. What is the scale and distribution of research commercialization, and which public datasets can establish a reproducible baseline?
4. Which interventions show credible evidence of impact in Indian states or comparable economies?
5. What implementation costs, institutional capabilities and unintended effects must be evaluated?

3. Proposed interventions

3.1 — Research partnerships

Design: Test research partnerships in a limited geography or sector, using clear eligibility criteria and published operating rules. Delivery: Identify a lead implementing body, local partners, an independent evaluator and a route for citizen or business feedback. Evidence: Track a baseline, a comparison group where feasible, costs per beneficiary and quality-of-service measures.

3.2 — Open innovation

Design: Test open innovation in a limited geography or sector, using clear eligibility criteria and published operating rules. Delivery: Identify a lead implementing body, local partners, an independent evaluator and a route for citizen or business feedback. Evidence: Track a baseline, a comparison group where feasible, costs per beneficiary and quality-of-service measures.

3.3 — AI safety and governance

Design: Test ai safety and governance in a limited geography or sector, using clear eligibility criteria and published operating rules. Delivery: Identify a lead implementing body, local partners, an independent evaluator and a route for citizen or business feedback. Evidence: Track a baseline, a comparison group where feasible, costs per beneficiary and quality-of-service measures.

4. Illustrative demonstration project

5. Phased implementation roadmap

6. Measurement framework

The following indicators are proposed for an MDI dashboard. Definitions, sources, frequency and disaggregation should be finalized with domain experts.

• AI adoption in SMEs
• research output
• AI-skilled workers
• public-service quality and audit outcomes.

7. Risks, trade-offs and safeguards

Implementation risk: Ambitious national targets may obscure weak local delivery. Mitigation: publish state and district dashboards and stage investments around independent evaluations.

Equity risk: Benefits may concentrate among better-connected communities. Mitigation: track distributional outcomes and design accessibility, language and inclusion safeguards.

Measurement risk: Correlation may be mistaken for impact. Mitigation: document baselines, methods, data limitations and alternative explanations.

Financial risk: Costs may exceed expected benefits. Mitigation: assess unit economics, fiscal sustainability and opportunity costs before scaling.

8. Expert consultation agenda

1. Which assumption in this brief is weakest, and what evidence could falsify it?
2. Which state, district or industry offers the best first pilot?
3. What would a realistic five-year target look like?
4. Which institution should be accountable for the outcome?
5. What should be excluded to keep the intervention feasible?

9. Reference sources and evidence plan

• NITI Aayog — Strategic Imperatives for Viksit Bharat @2047
• NITI Aayog — DPI@2047 roadmap
• World Bank — India data
• NITI Aayog AI strategy
• UNESCO AI ethics.

This is an original editorial working brief, not a peer-reviewed paper. It intentionally avoids presenting unsupported numerical forecasts as established facts. Consult linked primary sources for current statistics.