Enterprise Experiments with AI Agents–2025 Global Trends

AI technology has evolved rapidly from generation to action with AI agents. Nasscom’s study, “Enterprise Experiments with AI Agents – 2025 Global Trends,” first of a two-part study set, offers a peak into the evolution journey of current AI agents and potential models of the future. The study offers a comprehensive analysis of the current state of enterprise interest, adoption, preferences, challenges, and risk perception associated with deploying AI agents in the near – mid - term, based on a global survey of over 100 large and medium – sized enterprises. The upcoming second part, “Agentic AI Adoption Models,” of this study will explore the emerging tech ecosystem supporting the rapid evolution of agentic AI solution space.

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Key Findings

AI agents are software programs that can sense, assess, act, learn to pursue complex goals with autonomy and predictability.
LLM-based agents are a recent but rapidly evolving generation of AI technology aimed at delivering human-like process and functional automation at scale.
AI agents can be segmented based on their objectives, learning techniques, or the operating architecture.
Core characteristics of AI agents include Reasoning, Perception, Learning & Adaptation, Action Execution, and Decision Making.

~90% companies report having dedicated AI budgets, and nearly 70% have dedicated AI specialist teams already.
GenAI model readiness, the core foundation for building agents is taking shape and is yet to mature as only about 50% of the enterprises are actively working on fine-tuning LLMs for enterprise use and developing use-case specific SLMs.
Enterprises are prioritizing various data processing governance activities, such as integrating unstructured data and data pipeline building to accelerate AI agent adoption.

88% of the enterprises have indicated readiness to allocate specific budgets to test and build AI agents in 2025.
Over 60% are working across various stages – from pilots to production and some even scaling-up agent adoption. More momentum is expected within the next 6-12 months.
Majority of the enterprises are focusing on task-based agents, process automation with human feedback, or those at the interface of physical or industrial AI and humans.
Organizations are currently focusing on internal initiatives led by select functions and early client-facing use cases.

Global companies believe that the benefits of agentic AI are eventually going to be more enterprise-scale, such as increasing the pace of organizational decision-making, etc.
Contrary to belief, workforce time savings are not the top driver for deploying agents, suggestive of the heavy human expertise needed.
Enterprises cite data privacy and security risks as the top challenges in scaling AI
Unknown risks and inconsistent regulations are the other major challenges.
Most companies are yet to establish AI-specific risk management frameworks.

Over 50% of the enterprises expect either deploying agents at scale or getting disrupted by competitors who would do so within 12 months.
While the pursuit for autonomous gathers headlines, it is a well thought human + AI collaborative approach that is expected to be a more sustainable and ethical approach to scaled agentic AI deployments.

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