How enterprises can turn AI experiments into real-world impactPresented 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 what an agent could do.”
As these systems become more autonomous, the challenge extends beyond how they operate to how they interact with enterprise data, systems, and teams.
AI is augmenting human work, not replacing it
Agentic AI is fundamentally an amplifier of human capability rather than a substitute for it, Maner 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 that there’s going to be love, peace, and harmony between AI, agentic tools, and robotics systems, and human capital,” Maner said. “That harmony can be optimized for better outcomes for businesses, enterprises, governments, and public sector organizations, if the right vendors provide the right tooling and the right services.”
How enterprises are getting started with AI at scale
In practice, the move from experimentation into real-world deployment is where the challenges become most visible. Despite the momentum, many are still working through how to scale AI beyond initial use cases.
As they do, organizations quickly run into practical constraints. Many start in the cloud because of easy access to resources and services, but practical considerations like data, governance and control, and cost quickly come to the forefront.
The cloud can be used to experiment, with the ultimate goal of bringing applications back on premises as they move toward production, using platforms that solve for security and cost.
The use cases gaining the most traction include document search and knowledge retrieval, security and predictive threat detection, software development and coding workflows, and customer support and service operations. In the security realm, banking customers and others in Europe and the U.S. are deploying AI-driven tools including facial recognition and predictive threat detection. Meanwhile, there’s a growing focus on end-to-end, 360-degree customer engagement, from pre-sales through post-sales advocacy, in the customer support industry.
Industry-specific AI transformation is already underway
Across industries, the shift from experimentation to real deployment is already taking shape in distinct ways. In retail, AI is transforming store operations with cameras and robotics used for targeted in-aisle marketing at the moment of purchase decision, while cashier-less checkout is replacing traditional POS systems, and the human capital freed up is being redeployed to back-office and merchandising functions.
In healthcare, Nutanix works with customers on applications spanning diagnosis, treatment, remote health, and hospital operations, with cloud partners including AWS and Azure. In manufacturing and logistics, the transformation is equally significant.
The operational challenges of scaling enterprise AI
As AI use cases scale, enterprises are running into a new class of operational challenges. Managing multiple AI workloads and agents, coordinating infrastructure access across teams, ensuring security and governance, and integrating AI systems with existing business processes are now top-of-mind concerns for IT and business leaders alike.
The gap between AI developers pushing for speed and access, and infrastructure teams responsible for security, uptime, and governance, is one of the defining challenges of this moment.
“Now I’m running agents, and they’re all going to fight to get access to resources to solve my problems,” Cornely said. “What you want now is infrastructure that allows you to set constraints, govern resources.”
The AI factory: a shared platform for production AI
These challenges are driving demand for what Maner and Cornely describe as the AI factory: a shared infrastructure environment that supports multiple users and workloads simultaneously, enabling both experimentation and production while balancing developer agility with enterprise governance.
At GTC 2026, Nutanix announced the Nutanix Agentic AI Solution, a complete platform spanning core infrastructure, Kubernetes-based container services running on a topology-aware hypervisor, and advanced services for building and governing agents.
“We’re launching a complete platform, from core infrastructure through PaaS and advanced PaaS services to the whole management framework for your AI factories,” Cornely said. “Really enabling self-service for the teams that will build these applications in the enterprise.”
Hybrid environments are essential to enterprise AI strategy
Operating this kind of environment requires flexibility across infrastructure. Hybrid infrastructure is not a compromise, but a requirement. Some workloads will always run in the public cloud, while others must remain on premises due to security requirements, regulatory compliance, data sovereignty, or competitive IP considerations.
“Especially in the regulated industries, as sovereignty becomes a bigger issue, data gravity becomes a bigger issue, security, and also a lot of competitive differentiation in the industry, it’s going to depend on what the company wants for their own IP,” Maner said.
This is the foundation of Nutanix’s platform position, he added.
“We are the perfect harmony, bringing those applications, that data, and all the optimization for these use cases end to end, from on-prem to off-prem and in a hybrid mode,” he said. “Doing it not only in one cloud, but for multiple clouds.”
That flexibility also extends to the broader ecosystem. Nutanix works across hyperscalers including AWS, Azure, and Google Cloud, as well as regional service providers and emerging neoclouds. Nutanix offers neoclouds a full software stack to run their own clouds and deliver advanced AI services, giving enterprise customers already running Nutanix a simple extension of compute, networking, and AI capabilities.
Maner described the arrangement as a win for both sides. For enterprises, it means simplified access to hybrid AI services. For neoclouds, it means a proven platform to build on. It’s all automated and secure by default, Cornely added.
“All of those governance problems that now come up with agentic AI are the same problems we’ve been solving for the last 16 years for every other application running in your cloud,” he said.
From pilot to production: operationalizing AI across the enterprise
Ultimately, the goal is not to run a successful AI pilot, but to operationalize AI across real-world use cases, 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 massive gap right now between people building AI applications, those AI engineers, those agentic AI developers, and your classical infra teams,” Cornely said. “They need tooling to enable the infra teams, so they can support your AI engineers. That’s what we deliver with our agentic AI solution.”
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Stop Overpaying for GPUs Your AI Workloads Rarely UseFor the last 24 months, one narrative justified every over-provisioned data center and bloated IT budget: the GPU scramble. Silicon was the new oil, and H100s traded like contraband. Reserve capacity now or your enterprise would be left behind.
The bill is now due, and the CFO is paying attention. Gartner estimates AI infrastructure is adding $401 billion in new spending this year. Real-world audits tell a darker story: average GPU utilization in the enterprise is stuck at 5%.
That utilization floor is driven by a self-reinforcing procurement loop that makes idle GPUs nearly impossible to release. What makes this shift more urgent is the CapEx reality now hitting enterprise balance sheets. Many organizations locked in GPU capacity under traditional three- to five-year depreciation cycles, with the hyperscalers being at five years. That means the infrastructure purchased during the peak of the “GPU scramble” is now a fixed cost, regardless of how much it is actually used.
As those assets age, the question is no longer whether the investment was justified. It’s whether it can be made productive. Underutilized GPUs are not just idle resources, they are depreciating assets that must now generate measurable return. This is forcing a shift in mindset: from acquiring capacity to maximizing the economic output of what is already deployed.
The scramble was a sideshow
For the "Tier 1" enterprise — the Intuits, Mastercards, and Pfizers of the world — access was rarely the true bottleneck. Leveraging deep-pocketed relationships with AWS, Azure, and GCP, these organizations secured capacity reservations that sat idle while internal teams struggled with data gravity, governance, and architectural immaturity.
The industry narrative of "scarcity" served as a convenient smokescreen for this inefficiency. While the headlines focused on supply chain delays, the internal reality was a massive productivity gap. Organizations were activity-rich (buying chips) but output-poor (generating near-zero useful tokens).
At 5% utilization, the math simply doesn't work. For every dollar spent on silicon, 95 cents is essentially a donation to a cloud provider’s bottom line. In any other department, a 95% waste metric would be a firing offense; in AI infrastructure, it was just called "preparedness."
The Q1 tracker: A market in pivot
VentureBeat’s Q1 2026 AI Infrastructure & Compute Market Tracker confirms that the panic phase has officially broken. The tracker is directional rather than statistically definitive — January surveyed 53 qualified respondents, February 39 — but the pattern across both waves is consistent. When we asked IT decision-makers what actually drives their provider choices today, the results show a market in rapid pivot:
The access collapse: “Access to GPUs/availability” factor dropped from 20.8% to 15.4% in a single quarter — from primary concern to secondary in 90 days.
The pragmatic pivot: “Integration with existing cloud and data stacks” held steady as the top priority at roughly 43% across both waves, while security and compliance requirements surged from 41.5% to 48.7% — nearly closing the gap with integration.
The TCO mandate: “Cost per inference/TCO (total cost of ownership)” as a top priority jumped from 34% to 41% in a single quarter, overtaking performance as the dominant procurement lens.
The era of the blank check is dead. Inference is where AI becomes a line item.
Training and even fine-tuning were a tactical project; inference is a strategic business model. For most enterprises, the unit economics of that model are currently unsustainable. During the initial pilot phase, flat-fee licenses and bundled token deals allowed for architectural waste. Teams built long-context agents and complex retrieval pipelines because tokens were effectively a sunk cost.
As the industry moves toward usage-based pricing in 2026, those same architectures have become liabilities. When metered billing is applied to an infrastructure stack that sits idle 95% of the time, the cost per useful token becomes a line-item emergency the moment a project moves into production.
From activity to productivity
The shift highlighted in our Q1 data represents more than just a budget correction; it is a fundamental change in how the success of an AI leader is measured.
For the last two years, success was about “securing” the stack. In the efficiency era, success is “squeezing” the stack. This is why cost optimization platforms saw the largest planned budget increase in our survey, becoming a top-tier priority as organizations realize that buying more GPUs is often the wrong answer.
Increasingly IT users are asking how to stop paying for GPUs they aren't using. They are moving away from measuring GPU activity (how many chips are powered on) and toward GPU productivity (how many useful tokens are generated per dollar spent).
The luxury of underutilization is now a liability. The next act of the enterprise AI play is more about finding a way to make the silicon you already have pay for itself.
Owning the mint: The choice between token consumer and producer
As organizations move from proof-of-concept to production, the focus is shifting away from the latest GPU and toward the architecture of token generation. In this new economic reality, every enterprise must decide its role in the token economy: will you be a token consumer, paying a permanent tax to a model provider, or a token producer, owning the infrastructure and the unit economics that come with it?
This choice is not just about cost; it is about how an organization decides to handle complexity. Owning inference infrastructure means overcoming KV cache persistence, understanding the storage architecture, knowing what are tolerable latency guarantees, and addressing power constraints. It also introduces real-world enterprise limitations, power availability, data center footprint, and operational complexity, that directly impact how far and how fast AI can scale.
At the core of this challenge is KV cache economics. Storing context in GPU memory delivers performance but comes at a premium, limiting concurrency and driving up cost per token. Offloading KV cache to shared NVMe-based storage can improve reuse and reduce prefill overhead, but introduces tradeoffs in latency and system design. As NVMe costs rise and GPU memory remains scarce, organizations are forced to balance performance against efficiency.
For a token producer, managing these tradeoffs, across memory, storage, power, and operations, is simply the cost of doing business at scale. For others, the overhead remains too high, requiring a different path.
The specialized cloud pivot
VentureBeat’s Q1 tracker shows that the market is already voting on this strategy. The top strategic direction for enterprises is now to move more workloads to specialized AI clouds, a category that grew from 30.2% to 35.9% in our latest survey.
These providers — including Coreweave, Lambda, and Crusoe — are evolving. While they initially gained ground by serving model builders and training-heavy workloads, their revenue mix is changing rapidly. Today, training represents roughly 70% of their business volume, but inference customers now make up 30%. We expect that ratio to flip by the end of 2026 as the long tail of enterprise inference begins to scale.
These specialized providers are gaining strategic attention because they are not just selling GPU access. They are selling the removal of infrastructure friction. They optimize the full stack — storage, networking, and scheduling — around inference-first economics rather than general-purpose cloud operations. For an organization aiming to be a token producer, these environments offer a more efficient factory floor than traditional hyperscalers.
The rise of managed inference
For organizations that realize they cannot efficiently build or manage their own inference factories, a different trend is emerging. Our survey found that the intention to evaluate inference outsourcing and managed LLM providers jumped from 13.2% to 23.1% in a single quarter.
This nearly 10-percentage-point increase represents a realization that building inference infrastructure internally often creates hidden costs. Providers like Baseten, Anyscale, FireworksAI, and Together AI offer predictable pricing and service-level agreements without requiring the customer to become experts in vLLM tuning or distributed GPU scheduling.
In this model, the enterprise remains a token consumer, but one that is actively looking to price away the complexity of the stack. They are learning that managing inference internally is only viable if they have the volume to justify the operational burden.
Simplifying the hybrid stack
The choice to be a producer is also being made easier by a new layer of hybrid-cloud AI platforms. Solutions from Red Hat, Nutanix, and Broadcom are designed to operationalize open-source inference infrastructure without forcing every company to become a systems integrator.
The challenge is that modern inference depends on complex open-source components like vLLM, Triton, and Kubernetes. These systems rely on a rapidly evolving stack, with vLLM for high-throughput serving, Triton for model orchestration, and Ray for distributed execution, each powerful on its own, but complex to integrate, tune, and operate at scale. For most enterprises, the challenge isn’t access to these tools, it’s stitching them together into a reliable, production-grade inference pipeline. The promise of these newer platforms is portability: the ability to build an inference stack once and deploy it anywhere, whether in a hyperscaler, a specialized cloud, or an on-premises data center.
Our Q1 2026 AI Infrastructure & Compute Market Tracker confirms that interest in these DIY-but-managed stacks is growing, jumping from 11.3% in January to 17.9% in February, alongside provider adoption, with a steady rise in organizations leaning into open source. This flexibility matters because enterprise AI will not be centralized in one place. Inference workloads will be distributed based on where data lives, how sensitive it is, and where the cost of running it is lowest.
The winner in the next phase of the token economy will not be the platform that forces standardization through restriction. It will be the one that delivers standardization through portability, allowing enterprises to switch between being consumers and producers as their needs evolve.
The architecture of efficiency: The technical levers of productivity
Fixing the 5% utilization wall requires more than just better software; it requires a structural overhaul of the efficiency stack. Many organizations are discovering that high activity is not the same as high productivity. A cluster can run at full tilt but remain economically inefficient if time-to-first-token is too high or if inference requests spend too much time in prefill.
Inference economics are determined by how much useful output a cluster generates per unit of cost. This requires a shift from measuring GPU activity — simply having the chips powered on — to measuring GPU productivity. Achieving that productivity depends on three technical levers: the network, the memory, and the storage stack.
Networking: The cost of waiting
The network is the often-ignored backbone of inference economics. In a distributed environment, the speed at which data moves between compute nodes and storage determines whether a GPU is actually working or merely waiting.
RDMA (Remote Direct Memory Access) has become the non-negotiable standard for this move. By allowing data to bypass the CPU and move directly between memory and the GPU, RDMA eliminates the latency spikes that traditional network architectures introduce. In practical terms, an RDMA-enabled architecture can increase the output per GPU by a factor of ten for concurrent workloads.
Without this level of networking, an enterprise is effectively paying a "waiting tax" on every chip in the rack. As model context windows expand and multi-node orchestration becomes the norm, the network determines whether a cluster is a high-speed factory or a bottlenecked warehouse.
Solving the memory tax: Shared KV cache
As models become larger and context windows expand toward the millions of tokens, the cost of repeatedly rebuilding the prompt state has become unsustainable. Large language models rely on key-value (KV) caches to maintain context during a session. Traditionally, these are stored in local GPU memory, which is both expensive and limited.
This creates a "memory tax" that crushes unit economics as concurrency rises. To solve this, the industry is moving toward persistent shared KV cache architectures. By storing the cache centrally on high-performance storage rather than redundantly across multiple GPU nodes, organizations can reduce prefill overhead and improve context reuse.
Newer architectures are already proving this out. The VAST Data AI Operating System, running on VAST C-nodes using Nvidia BlueField-4 DPUs, allows for pod-scale shared KV cache that collapses legacy storage tiers. Similarly, the HPE Alletra Storage MP X10000 — the first object-based platform to achieve Nvidia-Certified Storage validation — is designed specifically to feed data to inference resources without the coordination tax that causes bottlenecks at scale. WEKA.io is another provider in this space.
The compression edge
Beyond the physical hardware, new algorithmic contributions are redefining what is possible in inference memory. Google’s recent presentation of TurboQuant at ICLR 2026 demonstrates the scale of this shift. TurboQuant provides up to a 6x compression level for the KV cache with zero accuracy loss.
Techniques like these allow for building large vector indices with minimal memory footprints and near-zero preprocessing time. For the enterprise, this means more concurrent users on the same hardware estate without the "rebuild storms" that typically cause latency spikes. The caveat: compression standards remain contested — no open-source consensus has emerged, and the space is shaping up as a proprietary stack war between Google and Nvidia.
Storage as a financial decision
Storage is no longer just a backend decision; it is a financial one. Platforms like Dell PowerScale are now delivering up to 19x faster time-to-first-token compared to traditional approaches, according to Dell. By separating high-performance shared storage and memory-intensive data access from scarce GPU resources, these platforms allow inference to scale more efficiently.
When a storage layer can keep GPU-intensive workloads continuously fed with data, it prevents expensive resources from sitting idle. In the efficiency era, the goal is to drive the 5% utilization wall upward by ensuring that every cycle is spent on token generation, not on data movement.
But as the stack becomes more efficient, the perimeter becomes more porous. High-productivity tokens are worthless if the data powering them cannot be trusted.
Sovereignty and the agentic future: Building the trust foundation
The final barrier to achieving return on AI is not a technical bottleneck, but a trust bottleneck. As enterprise AI shifts from simple chatbots to autonomous agents, the risk profile changes. Agents require deep access to internal systems and intellectual property to be useful. Without a sovereign architecture, that access creates a liability that most organizations are not equipped to manage.
VentureBeat research into the state of AI governance reveals a stark disconnect. While many organizations believe they have secured their AI environments, 72% of enterprises admit they do not have the level of control and security they think they do. This governance mirage is particularly dangerous as agentic systems move into production. In the last 12 months, 88% of executives reported security incidents related to AI agents.
Sovereignty as an architecture principle
Data sovereignty is often treated as a geographic or regulatory checkbox. For the strategic enterprise, it must be treated as a core architecture principle. It is about maintaining control, lineage, and explainability over the data that powers an agentic workflow.
This requires a new approach to data maturity, modeled on the traditional medallion architecture. In this framework, data moves through layers of usability and trust — from raw ingestion at the bronze level to refined gold and, eventually, platinum-quality operational data. AI inference must follow this same discipline.
Agentic systems do not just need available context; they need trusted context. Providing the wrong data to an agent, or exposing sensitive intellectual property to a non-sovereign endpoint, creates both business and regulatory risk. Compartmentalization must be designed into the stack from the start. Organizations need to know which models and agents can access specific data layers, under what conditions, and with what lineage attached.
Bringing the AI to the data
The fundamental question for the agentic future is whether to bring the data to the AI or the AI to the data. For highly sensitive workloads, moving data to a centralized model endpoint is often the wrong answer.
The move toward private AI — where inference happens closer to where trusted data resides — is gaining momentum. This architecture uses sovereign clouds, private environments, or governed enterprise platforms to keep the data perimeter intact.
This is where the choice to be a token producer becomes a security advantage. By owning the inference stack, an enterprise can enforce governance and lineage at the infrastructure layer. It ensures that the intellectual property used to ground an agent never leaves the organization's control.
The next platform war
The battle for AI dominance will not be decided by who owns the largest GPU clusters. It will be won by the companies with the best inference economics and the most trusted data foundation.
The organizations that win the efficiency era will be those that deliver the lowest cost per useful token and the fastest path to production. They will be the ones that have moved past the hoarding hangover to focus on productive output.
Achieving return on AI requires a shift in mindset. It means moving from a culture of securing the stack to a culture of squeezing the stack. It requires architectural rigor, a focus on token-level ROI and a commitment to sovereignty. When an organization can generate its own tokens efficiently and securely, AI moves from a science project to an economically repeatable business advantage.
That is how ROI becomes real. That is where the next generation of enterprise advantage will be built.
Rob Strechay is a Contributing VentureBeat analyst and principal at Smuget Consulting, a research and advisory firm focused on data infrastructure and AI systems.
Disclosure: Smuget Consulting engages or has engaged in research, consulting, and advisory services with many technology companies, which can include those mentioned in this article. Analysis and opinions expressed herein are specific to the analyst individually, and data and other information that might have been provided for validation, not those of VentureBeat as a whole.
