The race for Artificial Intelligence is reshaping corporate infrastructure
Artificial Intelligence is no longer a distant promise — it has become the main driving force behind infrastructure modernization across organizations. The latest Enterprise Cloud Index, published by Nutanix in March 2026, makes it clear that this year marks a true turning point for companies of all sizes. On one side, executive leadership is pushing technology teams to get more AI applications and agents into production as fast as possible. On the other, infrastructure teams face the challenge of keeping data centers and hybrid environments running smoothly while juggling containers, compliance, data sovereignty, and security — all without slowing down the innovation the business demands.
This is the eighth annual edition of the report, which measures global enterprise progress in cloud adoption. This year, the focus landed specifically on the hurdles IT executives face as they navigate the rapid growth of AI usage and the urgent need to modernize applications and infrastructure. The landscape is complex, but also packed with opportunities for those who position themselves wisely.
The survey reveals a striking data point: 85% of respondents say AI is accelerating container adoption to improve speed, reliability, and scalability. Companies that were barely dipping their toes into containerization now treat the technology as a centerpiece of their strategies. At the same time, Shadow IT is growing at an alarming rate, with employees adopting AI tools and agents on their own without any oversight or approval from IT departments. The result is a dangerous combination: new organizational risks, even stronger data silos, and a widening gap between AI ambitions and the actual capacity infrastructure can deliver.
Containers as the foundation of modernization
The Enterprise Cloud Index shows that container adoption is no longer a trend limited to tech companies — it now spans virtually every sector of the economy. The reason is straightforward: containers offer portability, scalability, and efficiency that traditional models based solely on virtual machines simply cannot match at the same pace. With Artificial Intelligence demanding faster development cycles, more flexible testing environments, and continuous deployments, containers have become the natural choice for teams that need to deliver results without waiting weeks for infrastructure provisioning.
The report numbers back this trend up in a big way. 87% of respondents expect container usage for applications to increase over the next three years, and 83% are already building new applications directly in containers. In practice, organizations that have already invested in containerization are getting AI models and intelligent agents into production far more quickly than those still relying on monolithic architectures.
Beyond speed, there is a governance dimension that makes containers even more relevant right now. Hybrid environments — combining public cloud, private cloud, and on-premises infrastructure — need an orchestration layer capable of ensuring AI workloads run in the right place, with the appropriate level of security, and in compliance with local regulations. Kubernetes, for example, has solidified its position as the standard for container orchestration, and the majority of companies surveyed already use it or plan to adopt it in the coming months.
Lee Caswell, SVP of Product and Solutions Marketing at Nutanix, summed it up well when he noted that the findings point to the need for enterprise-grade security, resiliency, and portability, since AI workloads can run anywhere. According to him, organizations would benefit from a common operating environment for virtual machines and containers that allows IT leaders to scale AI with confidence across hybrid environments.
The bottom line is that simply adopting containers in isolation is not enough. Infrastructure modernization requires VMs and containers to coexist in a unified environment that can be managed centrally, with full visibility into what is running, where it is running, and who has access.
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Platform engineering and operational maturity
Another important aspect is that containerization opens the door to a more mature operations model, often referred to as platform engineering. In this model, infrastructure teams stop being bottlenecks and instead offer internal self-service platforms for developers. This reduces friction, speeds up delivery, and — most importantly — keeps governance at the heart of operations.
For companies investing heavily in Artificial Intelligence, this kind of approach is practically mandatory, because the number of experiments, models, and agents that need to be tested and deployed grows exponentially. Without a solid foundation of well-orchestrated containers, the risk of losing control over the environment increases with every sprint.
Shadow IT and the organizational risks nobody wants to ignore
If containers represent the structured side of modernization, Shadow IT is the side that keeps CISOs and CTOs up at night. The Nutanix survey confirms something many IT professionals have been feeling firsthand: with the rise of generative Artificial Intelligence tools and autonomous agents, employees across various departments have started adopting solutions on their own, without going through the technology team.
The data is quite revealing. 79% of respondents report finding AI applications or agents being deployed by employees outside of IT. It could be a marketing team using a chatbot to generate content, a sales team feeding sensitive data into an external AI platform, or even developers spinning up test environments on public clouds without approval. Each of these actions, no matter how small it may seem, creates a blind spot in the infrastructure and significantly expands organizational risks related to data leaks, regulatory non-compliance, and security vulnerabilities.
To make matters worse, 87% of respondents believe that unauthorized AI usage introduces real risks, including exposure of sensitive data and intellectual property. This scenario highlights the need for closer collaboration between IT teams and business stakeholders to ensure AI deployments remain secure, compliant, and aligned with organizational goals.
Shadow IT has taken on a whole new dimension with AI
The Shadow IT problem is not new, but Artificial Intelligence has given it an entirely different dimension. Previously, Shadow IT was mostly about spreadsheets shared on personal cloud services or software installed without proper licenses. Now, we are talking about AI agents that make decisions, process confidential data, and interact with customers — all outside the company’s official security perimeter.
The Enterprise Cloud Index shows that most organizations still lack clear policies to address this scenario, and many cannot even map how many AI tools are being used unofficially. This creates internal silos that hinder data integration, compromise the quality of AI-driven decisions, and can ultimately lead to serious legal consequences, especially in markets with strict data protection regulations.
Organizational silos amplify the problem
Beyond Shadow IT, the report highlights that silos between business units and IT teams remain a significant obstacle. 82% of respondents believe these silos hinder the effective execution of technology initiatives, delaying deployment timelines and increasing operational complexity. In a context where the speed of AI adoption is critical, this kind of internal barrier can be the factor that separates companies that scale successfully from those stuck in never-ending pilot projects.
To tackle the organizational risks created by Shadow IT and silos, the most effective path is not to simply ban AI tools — that rarely works and tends to create friction with business teams. The approach the survey suggests is actually the opposite: provide a secure, governed, and accessible internal environment where teams can experiment with and use Artificial Intelligence without needing to turn to outside solutions. This is where infrastructure modernization connects directly to the Shadow IT issue.
When an organization offers a unified platform with support for containers, VMs, and AI workloads, it dramatically reduces the incentive for employees to seek alternatives on their own. On top of that, centralizing operations allows security, monitoring, and compliance policies to be applied consistently, shrinking the attack surface and strengthening the overall security posture.
AI agents and their transformative potential for organizations
Not everything in the report is about warnings and concerns. One of the most exciting takeaways from the Enterprise Cloud Index is the potential of AI agents within organizations. The majority of IT executives surveyed see these agents as catalysts for transformation across multiple fronts.
61% of IT executives expect AI agents to improve customer or employee experience. Additionally, 58% anticipate these agents will boost productivity and operational efficiency. And there is more: 57% see potential for AI agents to create entirely new products, services, or revenue streams. We are talking about a technology that does not just optimize existing processes but can open up completely new paths for value creation.
That optimism, however, needs to come paired with infrastructure capable of supporting the demand. Sophisticated AI agents require significant computing power, access to real-time data, and integration with multiple systems. Without a solid foundation of containers and well-configured hybrid environments, the potential of these agents stays limited to proofs of concept that never reach production at scale.
Data sovereignty and the balance between innovation and compliance
Another topic gaining prominence in the Enterprise Cloud Index is data sovereignty, which has become a central concern for companies operating across multiple regions and needing to comply with legislation like LGPD in Brazil, GDPR in Europe, and various other local regulations.
The numbers speak for themselves: for 80% of respondents, data sovereignty is a high priority when making infrastructure decisions, including where to use containers. Compliance obligations frequently lead organizations to keep data physically within the country where it was collected. More than half of respondents — 57% — feel the need to run their infrastructure within a single country, whether on-premises or through a local cloud region, primarily due to security and data protection concerns.
With Artificial Intelligence processing ever-larger volumes of data — much of it sensitive — ensuring that information remains stored and processed in the correct jurisdictions is a requirement that directly impacts infrastructure decisions. Infrastructure modernization needs to account for this factor from the start, not as an afterthought tacked on later. Companies that ignore data sovereignty when designing their hybrid environments risk facing fines, sanctions, and reputational damage that can be extremely difficult to undo.
In practical terms, this means the choice between public cloud, private cloud, or on-premises environments cannot be guided solely by cost or performance. Data location, access policies, and traceability of operations performed by Artificial Intelligence need to sit at the center of the strategy. Containers help significantly in this context because they allow workloads to be moved between environments with relative ease, as long as orchestration is properly configured. But governance goes beyond technology: it involves processes, internal policies, and above all, visibility.
The survey indicates that organizations with unified environments — where VMs and containers are managed from a single platform — find it easier to implement data sovereignty controls without compromising the agility of development and data science teams.
The directive comes from the top, but infrastructure still has not caught up
Perhaps one of the most revealing data points in the report is the disconnect between strategic ambition and infrastructure readiness. 59% of respondents anticipate their organizations will have more than five AI-enabled applications within the next three years. The push to deploy AI comes directly from executive leadership, which makes sense given the competitive potential of this technology.
However, when asked about their company’s ability to run AI workloads on-premises, 82% consider their current infrastructure not fully prepared to provide that support. This is a significant gap that reinforces the urgency of infrastructure modernization. There is no point in having an ambitious AI strategy if the technology foundation cannot sustain the operation.
This gap between vision and execution is one of the biggest challenges for 2026 and beyond. Closing it requires investment in containers, orchestration, hybrid environments, and — most importantly — the people and processes capable of managing this complexity in a sustainable way.
What to expect going forward
The big challenge for 2026, according to the report, is finding the balance between innovating with Artificial Intelligence and staying on top of compliance. Companies that manage to build a modern infrastructure foundation — with well-orchestrated containers, clear policies against Shadow IT, and robust data sovereignty controls — will be in a prime position to scale their AI projects without piling up organizational risks.
Those that treat infrastructure modernization as a secondary project will likely discover that the distance between ambition and execution only grows — and that the cost of course-correcting later is far greater than planning it right from the start.
The central message of the 2026 Enterprise Cloud Index is clear: AI is not going to wait for infrastructure to be ready. The companies that move now, investing in the right foundation and breaking down internal silos, are the ones that will reap the rewards down the road. 🚀
