AI Agents to Lead Enterprise Transformation in 2025
Enterprises will increase spending on AI agents by 3-4 times in 2025, shifting budgets from classical and GenAI to autonomous systems, says Nasscom report.
New Delhi, May 29 (IANS) — Enterprises are poised to allocate 3-4 times more funds toward AI agents in 2025, marking a strategic shift away from classical AI and even generative AI (GenAI) toward interactive, autonomous agent-based systems, according to a report by Nasscom in partnership with Avasant.
Key Findings:
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Adoption Trends:
- 27% of enterprises already have AI agents in production or at scale.
- 31% are at the proof-of-concept (PoC) stage.
- 30% plan to initiate PoCs or scale deployments in 2025.
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Budget Shifts:
- In 2024, 40% of companies spent over 40% of their tech budget on digital, up from 15% in 2023.
- 67% of global enterprises allocated 10% or more of their digital budget to AI in 2024.
- By 2025, enterprises plan to spend over 18% of their digital tech portfolio on AI, up from 14% in 2024.
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Strategic Pivot:
- The report highlights a transition from "digital-first" to "AI-first" operations, embedding intelligence across workflows, decisions, and experiences.
- "2025 will mark a decisive turning point in enterprise transformation," said Sangeeta Gupta, Senior Vice President and Chief Strategy Officer at Nasscom.
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Future Outlook:
- Modular, agile architectures and AI-ready workforces will be critical for navigating uncertainty and capturing growth opportunities.
- "We’re seeing a decisive surge in digital investments," noted Akshay Khanna, Managing Partner at Avasant, with 81% of enterprises expecting higher digital spending in 2025.
Conclusion:
The report underscores AI agents as the next frontier of enterprise transformation, driving productivity and innovation through seamless human-machine collaboration. Organizations investing in synchronous AI architectures and workforce readiness are likely to lead the charge in 2025 and beyond.
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About the Author

Dr. Sarah Chen
AI Research Expert
A seasoned AI expert with 15 years of research experience, formerly worked at Stanford AI Lab for 8 years, specializing in machine learning and natural language processing. Currently serves as technical advisor for multiple AI companies and regularly contributes AI technology analysis articles to authoritative media like MIT Technology Review.