As enterprises accelerate their adoption of generative AI, many quickly discover that enthusiasm alone does not guarantee value. In a recent interview, Abhas Ricky, Chief Strategy Officer at Cloudera, explains why understanding the economics and architecture behind AI is now just as important as the models themselves.
Abhas highlights a core principle that guides Cloudera’s strategy: AI must move to where the data already lives. Instead of transferring sensitive or high-volume datasets into external AI systems, Cloudera enables organisations to train and run models across public cloud, private cloud, on-premises and even edge environments without allowing data to leave their control. For highly regulated industries such as financial services, healthcare and the public sector, this architectural shift is becoming essential.
During the conversation, Abhas discusses how private AI can dramatically reduce costs. He shares the example of a customer able to deploy 400 private AI models for the same cost as 100 models in public cloud environments, simply by keeping infrastructure and data close together.
The interview also explores emerging trends including shared GPU infrastructure, sovereign cloud requirements, and the growing focus on national data residency laws. Abhas outlines Cloudera’s work with AWS in Europe and ongoing collaborations in India and Singapore to support governments and public entities that need AI systems to remain within national borders.
For organisations looking to deploy private AI behind firewalls, he points out three consistent challenges:
• the need for a unified knowledge hub to map relationships between data assets
• designing new workflows built for AI instead of digitising old processes
• implementing strong governance and guardrails to maintain trust, safety and compliance
The conversation closes with a look at the evolving workforce. As AI becomes embedded in enterprise operations, Abhas expects new roles to emerge across data engineering, model operations, automation and AI-first workflow design.
The full interview offers a clear view into how enterprises can balance innovation with responsibility while building scalable and cost-efficient AI systems.
