In an interaction with Express Computer, Piyush Agarwal, SE Leader, Cloudera outlines the factors behind the rapid adoption of Agentic AI, key roadblocks in its enterprise-wide implementation, and the company’s preparedness to address customer requirements in the AI domain.
How is Agentic AI rising to prominence in 2025, and what are the factors driving its increased adoption in enterprises?
Agentic AI is at the forefront of enterprise innovation in 2025 because it represents a significant evolution beyond traditional automation. Unlike basic rule-based bots, AI agents are capable of reasoning, learning, and taking autonomous actions based on real-time data. This ability to act independently yet intelligently is reshaping how enterprises think about operational efficiency and business agility.
There are several factors driving this surge, such as the explosion of enterprise data, which requires real-time decision-making; maturity of foundational AI models and cloud-native infrastructure; and demand for faster time-to-value in customer service, IT, and business processes.
Our global survey found that 96% of enterprises plan to expand their use of AI agents in the next 12 months, with 83% seeing them as key to maintaining competitive advantage. The momentum is even stronger in India, where 98% of surveyed enterprises are planning expansion in their use of AI agents, indicating the market’s readiness to operationalise AI in meaningful ways.
What are the key challenges organisations face today in implementing Agentic AI?
The most significant barriers stem from data management complexity. Many enterprises struggle with data privacy concerns, which in India is cited by 63% of IT leaders in the survey, followed by integration with legacy systems reported by 50%, and high implementation costs by 58%. This illustrates that trust and compatibility issues are primary roadblocks, as enterprises worry about safeguarding sensitive data and transforming legacy environments.
These challenges all point to one core issue that is data fragmentation. To make agentic AI work at scale, businesses need a unified, well-governed data architecture that ensures high-fidelity, secure, and accessible data across environments. Without this, AI agents cannot operate effectively or safely.
Furthermore, businesses must recognise that implementing robust data privacy policies and governance cannot be an afterthought, but a foundational element of sustainable and responsible innovation.
What are Cloudera’s imperatives for Agentic AI preparedness, and how is this reflected in a real-world customer case study?
At Cloudera, we are addressing the challenges of the AI market by offering the only true hybrid platform for data, analytics, and AI, ensuring seamless data movement, unified data governance, and secured AI integration across both cloud and on-premises environments. Our open data lakehouse serves as the core for developing and deploying AI applications such as chatbots, document summarisation, and code generation. Cloudera empowers customers to manage data across its full lifecycle, simplifying its use with LLMs and enabling them to achieve industry firsts or bests.
Taking this a step further, Cloudera’s AI Inference Service provides a secure, scalable environment for hosting predictive and generative AI models, ensuring high availability and fault tolerance. This fully private AI deployment capability allows organisations to operate AI agents securely within their own infrastructure, maintaining data sovereignty, compliance, and control.
One key example is in Security Operations Centers (SOCs), where Cloudera-powered AI agents analyse vast amounts of security data, detect threats, and automate incident responses in real-time. This AI-driven SOC transformation helps mitigate alert fatigue, enhances threat detection accuracy, and optimises security workflows.
Beyond security operations, AI agents are transforming other critical industries through intelligent automation and data-driven decision-making at scale. In financial services, they support fraud detection, automated credit risk analysis, and personalised advising, enabling banks to scale decisions securely and efficiently. Additionally, in telecommunications, autonomous agents enhance network monitoring, predictive maintenance, and real-time customer support, improving service quality and operational efficiency.
How are AI regulations evolving, and what are the current perspectives on governance and compliance?
Globally and in India, we are seeing a rapid evolution in AI governance frameworks. Regulations are focusing on ethical use, explainability, data protection, and traceability of AI systems. India’s Digital Personal Data Protection Act and forthcoming AI governance guidelines reflect this shift.
As governments tighten data privacy regulations, compliance has grown more complex, particularly in India, where sectors like banking, insurance, public services, and healthcare must navigate strict data residency and privacy mandates. For these industries, data sensitivity and strict governance are non-negotiable, and AI must be deployed where data can be controlled securely, which often means on premises or in virtual private clouds.
Compliance must be built into the data layer, not added later. Our platform provides end-to-end data lineage, access control, and policy enforcement to help organisations stay compliant with both domestic and international regulations while accelerating AI adoption responsibly. As Agentic AI becomes more embedded in business operations, this proactive approach to governance will be critical.
What are the key drivers on Cloudera’s FY26 vision board for India?
Cloudera’s strategic priorities for FY26 are focused on delivering business value through three foundational pillars – delivering true hybrid, enabling modern data architectures, and accelerating enterprise AI. Our goal is to help organisations reduce cost, complexity, and risk by supporting scalable advanced analytics and AI across any cloud or on-premises environment. With our open data lakehouse, we deliver secure, efficient, and streamlined data and AI operations, backed by strong security, governance, and observability via a shared data experience.
At Cloudera, we are dedicated to enabling the rapid deployment of trusted AI by connecting any AI model with secure, well-governed data. This effort is reinforced by strategic alliances with key industry players such as NVIDIA and CrewAI. Cloudera’s AI Ecosystem extends this commitment through focused partnerships with AI vendors, cloud providers, and system integrators. Our broader strategy includes ongoing investment in partner development, enablement programs, marketing initiatives, and the promotion of governance best practices.