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The Enterprise Sprint and Marathon in the Agentic AI Race
The Enterprise Sprint and Marathon in the Agentic AI Race

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AI agents are real, here, and now—with 88% of CXOs from our global enterprise survey indicating plans to allocate funds for building agents. Global trends in growing adoption of AI agents have been captured in the recently launched nasscom report titled, “Enterprise Experiments with AI Agents- 2025 Global Trends”. The study is based on a global enterprise survey of 100+ companies across 10+ industries. Early analysis reveals that enterprises are strengthening their tech core- AI spending and talent, GenAI capabilities, data foundations, and process flexibility to build and deploy AI agents.

Enterprises are embracing a data-first approach and identifying tangible benefits from agentic AI and its intelligent, decision-centric automation. As organizational adoption evolves, there are four major aspects that could drive the pace of agentic adoption

§  Specific enterprise data strategies aimed at making efficient use of data relevant to AI models – as organizations now focus on feedback loops to prepare AI ready data, RAGs, and process reengineering around agentic flows that will eventually require rearchitecting data flows;

§  Understanding of the gap between perceived benefits from agentic AI and the ground enterprise ability to monetize the technological leap – enterprises realize that agentic AI can impact connected decision-making, but also, that human expertise is not a forgone capability;

§  The less highlighted but real implementation hurdles that organizations may likely be undermining – while majority consider data privacy, security, shifting compliance scenarios as major roadblocks, several consider mindset shifts to not be a major challenge, which may backfire as agents need massive human feedback in the initial learning stages;

§  The real expectations around competitive disruption timelines that could trigger rapid executive action, sooner rather than later – as majority organizations expect a disruption due to competition adopting agentic AI within less than 12months.

 

 

More in-depth analysis of the survey responses reveals interesting insights into how enterprises are gearing up for investments in agents, but that the implementations will be largely internal, with established datasets and processes, and with the aim to build from task to process to functional scope over the next 2-3 years.

·       88% of enterprises now have dedicated AI budgets and emerging AI expert teams, both crucial for building scalable AI applications.

·       AI spending, as a percentage of tech budgets, is rising, with two-thirds of companies allocating over 15% specifically to AI.

·       Encouraging that nearly 90% of global enterprises expect some form of dedicated agentic AI spend within their overall AI budgets.

·       62% of enterprises are across various stages of active implementation of AI agents – either piloting, or moving to production or limited scale

·       Around 76% of companies are prioritizing 'client zero' strategies—starting with internal deployments, particularly in IT operations—before expanding to external use cases.

·       Enterprises view the rapid transformation of information into intelligence for faster decision-making as the primary advantage of Agentic AI. This is closely followed by its ability to enhance responsiveness to new opportunities and reduce failure rates through human-led process optimization.

·       Data privacy and associated risks are identified as the top challenges in adopting Agentic AI, followed by concerns over the unknown risks of self-learning AI systems and the absence of consistent or clear regulations.

·       Contrary to popular belief, only 39% expect AI agents to significantly reduce workforce time, highlighting the continued importance of human coordination and oversight.

·       While adoption remains in its early stages, with around 77% of companies focusing on task-level automation involving human oversight, there is a strong intent to invest further.

 

Hence, the preference for human + AI synergistic agentic systems is expected to emerge as AI agents demonstrate autonomy and trust-worthy agency with human-assisted reinforcement learning, context awareness, real-world reasoning, and ethical alignment 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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AI Analyst and Learner with a background in AIML and Data science

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