2025印度AI愿景_生成式AI助力生产力提升_132页_8mb

报告摘要

The Problem of AI

The widespread progress in Generative AI has immense potential, but adoption in India remains limited due to challenges like data readiness, infrastructure gaps, and cost concerns. While 36% of enterprises budget for GenAI, only 15% have workloads in production.

Executive Summary

Generative AI is accelerating productivity across India’s economy, with potential gains:
- 2.61% in the organized sector by 2030
- 5.44% across the economy by 2030
India’s approach combines pre-designed infrastructure, curated datasets, and public-private partnerships to address adoption barriers.

Introduction to Generative AI in India

EY’s GenAI survey shows that 56% of enterprises have not yet stabilized their GenAI strategy. Despite these headwinds, 24% of companies are experimenting with GenAI. The report explores five key trends shaping GenAI adoption:

Voice, chat, and language tools
AI agents
Falling AI costs
Availability of local language GenAI
Growth of an Indian AI ecosystem

Economic Impact and Job Transformation

Productivity Gains: India could hit +38 million jobs affected, boosting productivity by up to 5.44%
Job Transitions: 24% of tasks are automatable, 42% can be augmented
Key Growth Sectors: Services show the highest productivity gains; India’s organized sector could see +2.61% boost by 2030

Transforming Industries with GenAI

Financial Services: GenAI can boost productivity by 34-38% by 2030
Healthcare: Could lead to ~30% productivity gains
Retail: Anticipated productivity impact: 35%

Future Roadmap: Recommendations

Increase India’s AI-readiness by improving infrastructure and local dataset creation
Strengthen Responsible AI measures
Revitalize the IndiaAI Mission with targeted private participation
Promote AI-driven innovation via public challenges

Introduction to India's GenAI Infrastructure

EY surveyed over 125 C-suite executives to map India’s journey toward AI adoption. Challenges include data readiness, syntax errors in implementation, and inconsistent adoption timelines between companies.

Digital Transformation and Enterprise Readiness

Currently, architecture and integration are early adoption challenges
22% have fully integrated GenAI strategies with clear execution processes, compared to 15% with clear alignment to business goals but deficits in execution
Talent, particularly AI expertise, and data platform readines are primary gaps

Recommendations for India's AI Ecosystem

Strengthen the India Dataset ecosystem to reduce dependence on proprietary datasets
Joint initiative between academia and industry can generate usable data
Prioritize AI applications in critical infrastructure domains

Government & Public Policy: Policy Initiatives

IndiaAI Mission: INR10,371 crore plan to build sovereignty and capabilities
Sovereign AI Development: Attracts private investment and facilitates national security
Responsible AI Governance: Mandates transparency and accountability frameworks

Concluding Assessment

Generative AI’s arrival in India is inevitable, but full-scale impact requires clear strategy, data assembly, and institutional support. Actions such as compliance, governance, and widespread application deployment represent the next critical phase of unlocking GenAI’s potential.