Enterprises Overwhelmingly Expanding Use of AI Agents: Cloudera

Channel Post MEA

21-04-2025

Cloudera has released the findings of its latest survey report, 'The Future of Enterprise AI Agents.' The survey polled nearly 1,500 enterprise IT leaders across 14 countries to understand their adoption patterns, use cases, and sentiments around AI agents. Results show an overwhelming 96% of respondents have plans to expand their use of AI agents in the next 12 months, with half aiming for significant, organization-wide expansion. The applications for this deployment include performance optimization bots (66%), security monitoring agents (63%), and development assistants (62%).
For business and IT leaders alike, agentic AI marks a new frontier—moving beyond traditional automation to systems that can reason, act, and adapt in real-time. When implemented effectively, these intelligent agents unlock operational agility, drive cost savings, and dramatically improve customer engagement. As a result, AI agents are quickly becoming a key source of competitive advantage, with 83% of organizations stating that investing in them is crucial to maintaining their edge in the market.
In addition to the benefits of the technology, Cloudera's survey answered some of the biggest questions around agentic AI, including: How widely is this being adopted? Adoption is already underway. A majority (57%) of enterprise IT leaders report they've implemented AI agents in the past two years—21% in just the last year—signaling rapid momentum that's only expected to grow.
Adoption is already underway. A majority (57%) of enterprise IT leaders report they've implemented AI agents in the past two years—21% in just the last year—signaling rapid momentum that's only expected to grow. How are organizations deploying agents? Two-thirds (66%) are building agents on enterprise AI infrastructure platforms, while 60% are leveraging agentic capabilities embedded in existing core applications. This hybrid approach reflects a clear preference for scalable, secure, and close-to-data deployments.
Two-thirds (66%) are building agents on enterprise AI infrastructure platforms, while 60% are leveraging agentic capabilities embedded in existing core applications. This hybrid approach reflects a clear preference for scalable, secure, and close-to-data deployments. What's getting in the way? The top three barriers are data privacy (53%), integration with legacy systems (40%), and high implementation costs (39%). These pain points all stem from a common root: the need for robust, unified data management and governance.
The top three barriers are data privacy (53%), integration with legacy systems (40%), and high implementation costs (39%). These pain points all stem from a common root: the need for robust, unified data management and governance. Where should companies begin? Start with a contained, high-impact project—such as an internal IT support agent. These 'fast-to-value' use cases help teams prove ROI, build internal confidence, and lay the foundation for broader, scaled deployments.
'AI agents have moved beyond experimentation—they're now delivering real automation, efficiency, and business results. We're seeing enterprises run hundreds of models in production, all demanding high-fidelity, well-managed data to drive better outcomes,' said Abhas Ricky, Chief Strategy Officer, Cloudera. 'In 2025, agentic AI is taking center stage, building on the momentum of generative AI but with even greater operational impact. Cloudera is enabling this transformation through a robust Enterprise AI Ecosystem, helping global organizations design secure, scalable, and integrated AI workflows that turn data into action.'
Cloudera's report also addresses what enterprises are actually doing with AI agents. The top use cases vary by industry, shaped by the specific needs and priorities of each sector: Finance & Insurance : Fraud detection (56%), risk assessment (44%), and investment advisory (38%) are the leading use cases. AI agents are flagging suspicious transactions in real time, simulating market scenarios to evaluate risk, and supporting advisors with personalized investment suggestions.
: Fraud detection (56%), risk assessment (44%), and investment advisory (38%) are the leading use cases. AI agents are flagging suspicious transactions in real time, simulating market scenarios to evaluate risk, and supporting advisors with personalized investment suggestions. Manufacturing : Top applications include process automation (49%), supply chain optimization (48%), and quality control (47%). Agents are monitoring production lines to catch defects early, rerouting logistics to avoid delays, and streamlining repetitive tasks to improve efficiency.
: Top applications include process automation (49%), supply chain optimization (48%), and quality control (47%). Agents are monitoring production lines to catch defects early, rerouting logistics to avoid delays, and streamlining repetitive tasks to improve efficiency. Healthcare : Appointment scheduling (51%), diagnostic assistance (50%), and medical records processing (47%) are the most common use cases. AI agents are reducing admin burden by coordinating schedules, surfacing relevant EMR data, and helping clinicians identify conditions in imaging data.
: Appointment scheduling (51%), diagnostic assistance (50%), and medical records processing (47%) are the most common use cases. AI agents are reducing admin burden by coordinating schedules, surfacing relevant EMR data, and helping clinicians identify conditions in imaging data. Telecommunications: The telecoms industry is seeing substantial innovation fueled by AI. Customer support bots (49%), customer experience agents (44%), and security monitoring agents (49%) are key deployments. Agents are resolving service issues instantly, flagging at-risk customers using behavior data, and protecting networks from emerging threats. 0 0