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Across industries, organizations are focused on how to move from AI pilots, proofs of concept, and cloud-based experiments to deployment at scale—in real workloads, for real users, in real business environments. VentureBeat spoke with Tarkan Maner, president and chief commercial officer of Nutanix, and Thomas Corneli, executive vice president of product management, about what this transition entails and what it will take to get it right.
« AI in general is shifting everything we do, not just in technology, but across all vertical industries, from regulated industries like banking, healthcare, government, education to unregulated industries like manufacturing and retail, » Manner said. « As an end-to-end platform company, we welcome this change. It creates 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, Corney said.
« It’s one thing to do an experiment, to make a prototype. It’s another thing to take that prototype and roll it out to 10,000 employees, » he explained. « We’ve gone from humans focusing on learning models to chatbots to acting agents where the demand and pressure on AI infrastructure is growing exponentially. »
Agentic AI introduces a new layer of enterprise complexity
The rise of agentic AI is what makes this transition particularly significant. These systems introduce multi-step workflows across applications and data sources, along with a degree of autonomy that creates new operational requirements.
Enterprises must now contend with multiple agents running concurrently, unpredictable real-time workloads, and the need to coordinate infrastructure access across teams.
« OpenClaw makes it easy now for anyone to create agents and work with agents, » Cornelli said. « You want those agents to work in place with your data. You have to have the right structures around it to protect the enterprise from what an agent can do. »
As these systems become more autonomous, the challenge extends beyond how they work to how they interact with enterprise data, systems and teams.
AI augments human work, not replaces it
Agentic AI is fundamentally an augmentation of human capabilities, not a replacement for them, Manner said. The goal for enterprises is not to eliminate human work, but to find the right balance between human decision-making, AI-driven automation and agent-based workflows.
« We believe there will be love, peace and harmony between AI, agent tools and robotic systems and human capital, » Maner said. « This harmony can be optimized for better outcomes for businesses, enterprises, governments and public sector organizations if the right providers provide the right tools and the right services. »
How enterprises get started with AI at scale
In practice, moving from experimentation to real-world implementation is where the challenges become most visible. Despite the momentum, many are still working on how to scale AI beyond initial use cases.
As they do so, organizations quickly run into practical limitations. Many start in the cloud due 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 experimentation with the ultimate goal of bringing applications back on premises as they move to production using platforms that address security and cost.
Use cases gaining the most popularity include document search and knowledge mining, security and predictive threat detection, software development and coding workflows, and customer support and service operations. In security, bank customers and others in Europe and the US are deploying AI-driven tools, including facial recognition and predictive threat detection. Meanwhile, there is a growing focus on holistic, 360-degree customer engagement, from pre-sale to post-sale advocacy, in the customer support industry.
AI transformation for industry is already underway
In different industries, the transition from experimentation to real implementation is already taking place in different ways. In retail, artificial intelligence is transforming store operations with cameras and robotics used for targeted marketing in the aisle at the point of purchase, while cashierless checkouts are replacing traditional POS systems and freed-up human capital is being redirected 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. In manufacturing and logistics, transformation is equally important.
The operational challenges of scaling enterprise AI
As AI use cases grow, enterprises face a new class of operational challenges. Managing multiple AI workloads and agents, coordinating infrastructure access across teams, providing 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, business continuity and management 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 resources to solve my problems, » Cornelli said. « What you want now is an infrastructure that allows you to set limits, to manage resources. »
The AI Factory: A Shared AI Production Platform
These challenges are driving the demand for what Maner and Cornely describe as an AI factory: a shared infrastructure environment that supports multiple users and workloads simultaneously, enabling both experimentation and production while balancing developer flexibility with corporate governance.
At GTC 2026, Nutanix announced the Nutanix Agentic AI solution, a comprehensive platform spanning core infrastructure, Kubernetes-based container services running on a topology-aware hypervisor, and advanced services for building and managing agents.
« We’re launching a complete platform, from core infrastructure through PaaS and advanced PaaS services to the entire framework for managing your AI factories, » Cornelli said. « It really enables self-service for the teams that are going to build these applications in the enterprise. »
Hybrid environments are essential to enterprise AI strategy
Working with this kind of environment requires flexibility in the infrastructure. Hybrid infrastructure is not a compromise, but a requirement. Some workloads will always run in the public cloud, while others must remain on-premise due to security requirements, regulatory compliance, data sovereignty or competitive IP considerations.
« Especially in regulated industries, as sovereignty becomes more of an issue, data gravity becomes more of an issue, security, and also very competitive differentiation in the industry, it’s going to depend on what the company wants for its own IP, » Manner said.
This is the foundation of Nutanix’s platform position, he added.
« We’re the perfect harmony, delivering those applications, that data, and all the optimization for those use cases end-to-end, on-prem to off-prem and in hybrid mode, » he said. « I’m not just doing it in one cloud, I’m doing it for multiple clouds. »
This flexibility also extends to the wider ecosystem. Nutanix works with hyperscalers including AWS, Azure and Google Cloud, as well as regional service providers and emerging neoclouds. Nutanix offers neoclouds a complete software stack to manage their own clouds and deliver advanced AI services, giving enterprise customers already using Nutanix a simple extension of compute, networking and AI capabilities.
Manner described the settlement as a win for both sides. For enterprises, this means simplified access to hybrid AI services. For neoclouds, this means a proven upgrade platform. Everything is automated and secured by default, Corney added.
« All these management problems that are now arising 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: Deploying AI in the Enterprise
Ultimately, the goal is not to run a successful AI pilot, but to apply AI to real use cases, manage the infrastructure as a shared resource, support collaboration between infrastructure teams and AI developers, and scale from initial designs to enterprise-wide implementation.
« There’s a huge gap right now between the people who build AI applications, those AI engineers, those agent AI developers, and your classic infra teams, » Cornelli said. « They need tools to enable infra teams so they can support your AI engineers. That’s what we deliver with our agent AI solution. »
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