Regional Vice President, India & South Asia
Cloudera
Today, AI agents are enabling a new wave of productivity and efficiency, with assistants that not only generate content but also take action. But what will that look like in practice, and where should enterprises focus their agentic aspirations? Cloudera’s recent survey of enterprise IT leaders in India suggests that 2025 is the year we find out.
Survey respondents believe that adoption of agentic AI is urgent, that the ROI will justify the effort and spend, that infrastructure is key to enabling AI agents, and that model bias is still a problem. Let’s dive into the details.
The Future of Enterprise AI is Agentic, and the Clock is Ticking
Interactive agentic systems are already reasoning, planning, and collaborating with users in new ways. The adoption of AI agents is no longer optional, it’s a strategic necessity. According to Cloudera’s survey, 84% of Indian respondents reported implementing AI agents within the past two years, with 36% starting in the last year. Even more telling: half of them are aiming for widespread, enterprise-level implementation.
This uptick in AI adoption is relatively new for many enterprises. This trend is set to continue in India, with 98% planning to expand their use in the coming year, and 78% aiming for a significant, organization-wide expansion.
Primarily, organizations are deploying (73%) stronger data privacy and security features (72%) and improved interoperability (68%) in their AI agent tools focused on enhancing both productivity and resiliency in IT and customer service. The vast majority (95%) report that their prior investments in GenAI prepared them well to implement AI agents, which is encouraging for those who are just beginning to experiment.
As adoption accelerates, enterprises must assess whether their infrastructure is ready to fully enable AI agents. Notably, 50% of surveyed IT leaders in India admit that the perceived complexity and usability challenges of AI agents have hindered widespread deployment, limiting their ability to leverage agentic capabilities embedded within their existing core applications.
AI Agents Will Significantly Increase ROI
As enterprises explore where agentic AI can deliver the most impact, early use cases are already demonstrating value.
AI agents are being used for customer support (91%), process automation (78%), and sales & market personalization (73%) showing that many companies are starting adoption in well-defined, ROI-driven domains. These areas provide ample opportunity to drive tangible results via automation, whether deploying IT helpdesk agents or leveraging predictive analytics to stay ahead of cyber attacks. As teams deploy and scale these AI agents, having low-code and no-code tools will be critical to ensure their success along the way.
CIOs also recognize that it is the tight coupling between GenAI capabilities and agentic AI applications that holds the key to unleashing substantial ROI.
GenAI assistants improve human resource efficiency and increase departmental capability by taking on tasks humans couldn’t make the time for. But those advantages often benefit only a few individuals. Agentic AI is a force multiplier. It extends the capabilities and benefits across the enterprise. For example, vendor contract summarization and clause management can be extended to partner and customer contracts; models trained on core marketing messaging can be made available to all market-facing contributors.
Additionally, agents leverage specialized LLMs to plan and orchestrate tasks and reason through complex challenges before selecting the most appropriate actions. As APIs are integrated into the agentic framework using low-code/no-code tools, agents will be able to perform an increasing number of day-to-day tasks in workflows throughout the organization.
That’s when ROI will skyrocket.
Bias Concerns Must Be Addressed
With greater autonomy comes an increased need for accountability, which is top of mind for IT leaders considering agentic AI. In Cloudera’s survey, over (72%) of enterprise leaders reported significant concerns about bias in AI systems.
As AI agents take control over mission-critical tasks, enterprises are working to establish accountability and proper governance. A sizable number of respondents (53%) are implementing processes that include human reviews, diversified training data, and formal fairness audits, with another 28% having introduced some bias-check measures.
Data quality and availability issues remain significant technical challenges in AI implementations of any kind. The solution lies in the strength and flexibility of enterprise data infrastructure.
Enlisting a Trusted Partner
Organizations are turning their agentic AI ambitions into enterprise-grade applications with substantial business outcomes. Our Enterprise AI platform combines trusted data infrastructure with scalable AI development tools, including low-code/no-code capabilities with AI Inference services and AI studios, which facilitate the safe deployment of AI agents at scale.
Organizations must also accelerate the pathway from experimentation to production and facilitate the embedding of cloud-native models inside private, highly-secure data estates, to effectively reduce the risk of AI to the current risk exposure of data environments.
AI agents will be implemented at an even faster pace than generative AI assistants, so 2025 is the year to act. Those with the right tools and partners will become outcome-generators, and distance themselves from the out of touch.
Authored By: Mayank Baid, Regional Vice President, India & South Asia, Cloudera