2025年印度人工智慧（AI）发展现状

The State of Artificial Intelligence (AI) in India, 2025

出版日期: 2025年12月18日 | 出版商:

Frost & Sullivan | 英文 42 Pages | 商品交期: 最快1-2个工作天内

价格

简介目录

2025年Frost & Sullivan公司对IT和业务决策者关注点和成长机会的调查

人工智慧 (AI) 正在迅速改变印度企业的优先事项和运作方式。Frost & Sullivan的这份报告是基于对各行业 IT 和业务决策者的调查数据，对人工智慧的采用和部署进行了全面分析。该分析检验了印度人工智慧的现状，并指出了营运效率、客户体验和数据驱动决策等关键驱动因素。

本报告检验了各组织如何从早期实验过渡到全面实施，尤其关注自主人工智慧的兴起以及对混合云端基础设施日益增长的需求。报告检验了筹资策略（包括主要技术供应商、IT整合商、Start-Ups和内部开发部门的角色），并讨论了影响人工智慧应用的监管趋势、人才招聘和组织挑战。此外，报告还深入分析了医疗保健、零售和金融服务等各行业的具体趋势。

读者将了解影响印度人工智慧发展的战略驱动因素和障碍，以及商业相关人员不断变化的优先事项。这项调查结果旨在为技术领导者、投资者和政策制定者提供参考，帮助他们应对印度市场人工智慧整合的复杂性。

调查目标和调查方法
调查目标和调查方法
受访者概况，人工智慧，印度，2025 年
主要发现
企业认识到人工智慧/机器学习在实现其业务目标方面的重要性。
印度企业人工智慧应用正处于转型阶段
提高营运效率是人工智慧投资的主要驱动力。
优先在核心业务领域采用人工智慧
新用例
自然语言处理是印度公司最重要的技术投资重点。
预测分析和脸部辨识引领人工智慧应用案例
印度企业倾向于选择混合云端作为人工智慧采用的数位基础设施模式。
人工智慧部署的首选数位基础设施模式是混合云端。
监管的不确定性和人才短缺是印度采用人工智慧的主要障碍。
监管环境持续变化。

印度的基于代理的人工智慧

基于代理的人工智慧将彻底改变印度的科技格局。
基于代理的人工智慧的应用仍处于早期阶段
印度企业计画采用主动式人工智慧
第三方服务供应商发挥关键作用
企业首选的 AI/ML 实施平台
印度推广基于代理的人工智慧面临的主要障碍

印度人工智慧应用策略驱动因素

商业相关人员在人工智慧的采用过程中发挥关键作用。
印度企业在人工智慧应用方面优先考虑品质和可靠性。
采购趋势显示出成熟供应商和新兴创新者并存的局面。
透过全面的IT和咨询服务在印度推广人工智慧

附录

成长机会驱动Growth Pipeline Engine（TM）™
为什么经济成长变得越来越困难？
The Strategic Imperative 8（TM）
成长机会的益处和影响
下一步
附件清单
免责声明

简介目录

Product Code: KB6A-69

Highlights and Growth Opportunities from a 2025 Frost & Sullivan Survey of IT and Business Decision-Makers

Artificial intelligence (AI) is rapidly reshaping the priorities and operations of Indian enterprises. This Frost & Sullivan report provides a comprehensive analysis of AI adoption and deployment, drawing on survey data from IT and business decision makers across a range of industries. The analysis examines the current state of AI implementation in India, identifying key drivers such as operational efficiency, customer experience, and data-centric decision making.

The report explores how organizations are moving beyond initial experimentation and transitioning to full-scale execution, paying particular attention to the emergence of agentic AI and the growing preference for hybrid cloud infrastructure. It reviews procurement strategies-including the roles of large technology vendors, IT integrators, start-ups, and in-house development-and addresses regulatory developments, talent availability, and organizational challenges that influence AI adoption. Sector-specific trends are discussed, with insights into healthcare, retail, financial services, and other verticals.

Readers will gain an understanding of the strategic enablers and barriers shaping AI growth in India, as well as the evolving priorities of enterprise stakeholders. The findings are intended to inform technology leaders, investors, and policymakers seeking to navigate the complexities of AI integration in the Indian market.

AI Adoption Trends, Drivers, and Deployment Realities in India

Research Objectives and Methodology
Research Objectives and Methodology
Respondent Profile, AI, India, 2025
Key Findings
Enterprises Recognize the Importance of AI/ML in Achieving Business Priorities
Enterprise AI Deployments in a Transition Phase in India
Improving Operational Efficiency is a Key Driver for AI Investments
AI Deployments Prioritized in Core Operational Areas
Emerging Use Cases
NLP is the Top Tech Investment Priority for Indian Enterprises
Predictive Analytics and Facial Recognition Lead AI Application Use Cases
Indian Enterprises Prefer Hybrid Cloud as Digital Infrastructure Model for AI Deployments
Hybrid Cloud is the Preferred Digital Infrastructure Model for AI Deployments
Regulatory Uncertainty and Talent are the Top Barriers to AI Implementation in India
The Regulatory Landscape Continues to Evolve