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Organizations across industries 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 Nutanix president and chief commercial officer Tarkan Maner and EVP of product management Thomas Cornely about what this transition entails and what it will take to get it right.
“AI in general is changing everything we do, not just in technology, but in all vertical industries, from regulated industries like banking, healthcare, government, education to unregulated industries like manufacturing and retail,” Maner said. “As a complete platform company, we welcome this change. It gives us more opportunities to better serve our customers as we move forward as a company.”
But there’s still a practical gap between practice and production, Cornelius said.
“It’s one thing to experiment, to prototype. It’s another thing to take that prototype and deploy it to 10,000 employees,” he said. “We’ve gone from people focusing on training models to chatbots to dealing with agents where the demand and pressures on AI infrastructure are growing exponentially.”
Agent AI introduces a new layer of enterprise complexity
The rise of agent AI is what makes this transition particularly consequential. These systems introduce multi-step workflows between applications and data sources, along with a degree of autonomy that creates new operational requirements.
Enterprises must now contend with multiple agents working simultaneously, unpredictable and real-time workloads, and the need to coordinate access to infrastructure across teams.
“OpenClaw now makes it very easy for anyone to build agents and work with agents,” Cornely said. “You want those agents working with your data on the premises. You have to have the right architecture around it to protect the enterprise from what an agent can do.”
As these systems become more autonomous, the challenge goes beyond how they work to how they interact with enterprise data, systems and teams.
Artificial intelligence does not replace human labor, but increases it
Maner said agent AI is essentially an augmentation of human capabilities, not a replacement. The goal for enterprises is not to eliminate human labor, but to find the right balance between human decision-making, AI-driven automation, and agent-based workflows.
“We believe that there will be love, peace and harmony between artificial intelligence, agent tools and robotic systems, and human capital,” Maner said. “If the right vendors provide the right tools and the right services, this harmony can be optimized to deliver better outcomes for businesses, enterprises, governments and public sector organizations.”
How enterprises are getting started with AI at scale
In practice, the transition from testing to real-world deployment is where the challenges are most apparent. Despite the pace, many are still working on moving AI beyond its original use cases.
When this happens, organizations quickly run into practical limitations. Many are getting started in the cloud thanks to easy access to resources and services, but practical considerations such as data, management and control, and cost quickly come to the fore.
The cloud can be used for testing, with the main goal being to bring applications back on-premise as they move towards production, using platforms that address security and cost.
The most prominent use cases include document search and knowledge retrieval, security and predictive threat detection, software development and coding workflows, and customer support and service operations. In security, banking clients in Europe and the US and others are deploying AI-based tools, including facial recognition and predictive threat detection. Meanwhile, in the customer support industry, there is a growing focus on comprehensive, 360-degree customer engagement, from pre-sales to post-sales advocacy.
Industry-specific AI transformation is already underway
Across industries, the transition from experimentation to real deployment is already taking place in different ways. In retail, AI is transforming store operations with cameras and robotics used for instant aisle marketing, cashier-less checkouts replacing traditional POS systems, and freed-up human capital being redeployed to back-office and merchandising functions.
In healthcare, Nutanix works with customers on applications spanning diagnostics, treatment, telehealth and hospital operations with cloud partners including AWS and Azure. Transformation in production and logistics is equally important.
Operational challenges of scaling enterprise AI
As AI use cases increase in scale, enterprises face a new class of operational challenges. Managing multiple AI workloads and agents, coordinating access to infrastructure across teams, ensuring security and governance, and integrating AI systems with existing business processes are now top concerns for both IT and business leaders.
The gap between AI developers pushing for speed and access and the infrastructure teams responsible for security, uptime and governance is one of the defining challenges of the moment.
“Now I’m managing agents and they’re all going to be fighting to get access to the resources to solve my problems,” Corneli said. “What you want now is an infrastructure that allows you to set limits, to manage resources.”
AI factory: A shared platform for AI manufacturing
These challenges drive demand for what Maner and Cornelius describe as an AI factory: a shared infrastructure environment that supports multiple users and workloads simultaneously, enables both experimentation and production, and balances developer agility with enterprise management.
Nutanix announced at GTC 2026 Nutanix Agentic AI Solutiona complete platform covering core infrastructure, Kubernetes-based container services running on a topology-aware hypervisor, and advanced services for setting up and managing agents.
“We run a complete platform, from core infrastructure through PaaS and advanced PaaS services to the entire management framework for your AI factories,” Corneli said. “It really enables self-service for the teams that will be building these applications in the enterprise.”
Hybrid environments are critical to an enterprise’s AI strategy
Exploiting such an environment requires infrastructure flexibility. Hybrid infrastructure is a requirement, not a compromise. Some workloads will always run in the public cloud, while others must remain on-premises due to security requirements, regulatory compliance, data sovereignty, or competing IP considerations.
“Especially in regulated industries, as sovereignty becomes a bigger issue, the weight of data becomes a bigger issue, security and a lot of competitive differentiation in the industry will depend on what the company wants for its IP,” Maner said.
This is at the core of Nutanix’s platform position, he added.
“We’re a perfect harmony, bringing those applications, that data and all the optimizations for those use cases end-to-end, off-premise and hybrid,” he said. “Doing it not just in one cloud, but in multiple clouds.”
This flexibility also extends to the wider ecosystem. Nutanix works on hyperscalers including AWS, Azure and Google Cloud, as well as regional service providers and new clouds. Nutanix offers neoclouds a complete suite of software to manage their clouds and deliver advanced AI services, giving enterprise customers already running Nutanix a simple extension of their computing, networking and AI capabilities.
Maner assessed the agreement as a win for both sides. For enterprises, this means simplified access to hybrid AI services. For neoclouds, this means a proven platform to build on. It’s all automated and secure by default, Cornelius added.
“All the management problems that arise with agent AI are the same problems that we’ve been solving for the last 16 years for every other application running in your cloud,” he said.
From Pilot to Production: Enabling AI in the Enterprise
Ultimately, the goal is not to launch a successful AI pilot, but to deploy AI in real life, manage infrastructure as a shared resource, support collaboration between infrastructure teams and AI developers, and scale from initial projects to enterprise-wide deployment.
“There’s a big gap right now between the people building AI applications, those AI engineers, agent AI developers, and your classic infra teams,” Corneli said. “They need a tool to enable their infra teams so they can support your AI engineers. We deliver that with our agent AI solution.”
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