Presented by Nutanix Across industries, organizations are focused on how to move from AI pilots, proofs of concept, and cloud-based experimentation to deploying it at scale — across real workloads, for real users, in real business environments. VentureBeat spoke with Tarkan Maner, president and chief commercial officer at Nutanix, and Thomas Cornely, EVP of product management, about what that tran...
Presented by Nutanix Across industries, organizations are focused on how to move from AI pilots, proofs of concept, and cloud-based experimentation to deploying it at scale — across real workloads, for real users, in real business environments. VentureBeat spoke with Tarkan Maner, president and chief commercial officer at Nutanix, and Thomas Cornely, EVP of product management, about what that transition demands, and what it will take to get it right. “AI in general is shifting everything we do, not only in technology, but across all vertical industries, from regulated industries like banking, health care, government, education to non-regulated industries like manufacturing and retail,” Maner said. “As a complete platform company, we welcome this change. It’s creating more opportunities for us as a company to serve our customers in better ways as we move forward.” But there’s still a practical gap between experimentation and production, Cornely said. “It’s one thing to do an experiment, to do a prototype. It’s a different thing to take that prototype and deploy it for 10,000 employees,” he explained. “We went from people focusing on training models to chatbots to now doing agents, where the demand and pressures on AI infrastructure are growing exponentially.” Agentic AI introduces a new layer of enterprise complexity The rise of agentic AI is what makes this transition especially consequential. These systems introduce multi-step workflows across applications and data sources, along with a degree of autonomy that creates new operational demands. Enterprises now have to contend with multiple agents running simultaneously, unpredictable and real-time workloads, and the need to coordinate access to infrastructure across teams. “OpenClaw is making it very easy now for anybody to build agents and run with agents,” Cornely said. “You want those agents to be running on premises with your data. You need to have the right constructs around it to protect the enterprise from wha...