Just a few years ago, enterprises were experimenting with machine learning models and predictive analytics. Then came generative AI, enabling organizations to create content, accelerate workflows, and unlock new productivity gains. Today, we're entering the next phase: agentic AI. Industry analysts have identified agentic AI as one of the most significant emerging technology trends, with autonomous agents expected to play an increasingly important role in enterprise operations. Gartner named Agentic AI one of its top strategic technology trends and describes it as autonomous, goal-driven digital workers.
Unlike traditional AI systems that respond to prompts, agentic AI systems can reason, make decisions, execute tasks, and interact autonomously with applications, data sources, APIs, and even other AI agents.
These systems have the potential to transform how work gets done, driving greater efficiency, faster decision-making, and new business opportunities.
Yet amid the excitement surrounding agentic AI, many organizations may be overlooking a critical question:
Is the underlying AI network infrastructure prepared to support it?
The answer matters more than many leaders realize.
Recent HPE research found that 87% of IT leaders believe their organization's network can handle increased AI traffic. However, less than half report a strong understanding of how their network supports essential AI lifecycle activities such as model development, training, tuning, and inferencing. This confidence gap raises important questions about enterprise readiness as AI becomes more sophisticated and increasingly autonomous.
The challenge isn't whether organizations will adopt AI.
The challenge is whether their agentic AI infrastructure can support what comes next.
Generative AI changed how people interact with technology. Agentic AI is changing how technology interacts with itself. Industry observers increasingly view AI as evolving beyond task automation and toward autonomous decision-making and workflow execution.
Unlike traditional AI systems that wait for prompts and respond to requests, agentic AI can independently initiate actions, coordinate workflows, retrieve information, interact with business applications, and execute tasks across multiple environments. This shift represents a significant evolution in how organizations use AI and introduces a new level of operational complexity.
Consider a customer service scenario. A generative AI assistant might answer a customer's question based on available information. An agentic AI system, however, could take the process much further. It might access customer records, verify account details, analyze support history, open a service ticket, schedule an appointment, trigger a fulfillment workflow, and communicate with other AI agents to complete the request and all without requiring human intervention.
Every one of these actions generates data movement across the network. As AI systems become more autonomous, the volume and complexity of these interactions increase dramatically. Unlike traditional application traffic, agentic AI introduces continuous machine-to-machine communication, dynamic workload placement, real-time inferencing requirements, and highly distributed data flows that span applications, clouds, data centers, and edge environments.
As a result, the network is no longer simply responsible for transporting data between users and applications. It has become critical infrastructure required for agentic AI. It is becoming the operational foundation that enables AI systems to function effectively, securely, and at scale.
HPE's latest research highlights this transformation, noting that networks must evolve beyond providing foundational support across the AI lifecycle. To support agentic AI, the network must play a more strategic role—actively shaping how AI is deployed, connected, protected, and scaled throughout the enterprise.
Many organizations believe they are well-positioned for AI because they have already invested in modernization initiatives. They've increased bandwidth, adopted cloud services, deployed next-generation networking technologies, and in many cases, successfully launched AI pilot projects. These are important steps, but they do not necessarily indicate readiness for the next wave of AI innovation.
Agentic AI places fundamentally different demands on AI network infrastructure than traditional enterprise applications. Supporting autonomous AI systems requires networking for AI workloads, including the ability to move vast amounts of data quickly and efficiently, maintain ultra-low latency, provide access to high-performance computing resources, and enable distributed processing across multiple environments. At the same time, organizations must ensure continuous security monitoring and support real-time decision-making as AI systems interact with applications, data sources, and other agents.
These requirements create new dependencies across networking, infrastructure, security, and operations. When any one of these areas becomes a bottleneck, AI performance, reliability, and scalability can suffer.
This is why HPE's research is so significant. While confidence in AI readiness remains high, many organizations still lack a complete understanding of how their networks support critical AI functions throughout the lifecycle, from data acquisition and model training to inferencing and optimization.
The disconnect between perceived readiness and actual readiness can introduce significant business risks. Organizations may find that AI pilots perform well in controlled environments but struggle when deployed at scale. Performance bottlenecks, security gaps, rising infrastructure costs, and operational complexity can quickly undermine expected outcomes and slow broader AI adoption.
As one IT operations executive quoted in HPE's research noted, "Without robust infrastructure and unified data governance, scaling AI beyond pilots is a pipe dream."
For organizations pursuing AI-driven transformation, understanding and addressing these foundational gaps may be just as important as the AI initiatives themselves.
For decades, networking was viewed primarily as a utility—a critical but largely invisible layer of infrastructure responsible for keeping users, devices, and applications connected. Success was often measured by uptime and connectivity, with the network serving as the foundation upon which other technologies operated.
Today, that role is evolving as organizations embrace AI-native networking strategies. As organizations invest in AI, hybrid cloud, edge computing, and digital transformation initiatives, the network is becoming far more than a conduit for data. It now plays a direct role in determining AI performance, strengthening security, shaping user experiences, improving operational efficiency, supporting infrastructure scalability, and enabling organizations to respond more quickly to changing business demands.
This shift is being driven by the growing complexity of modern IT environments. Organizations are moving away from centralized, static architectures and toward dynamic, distributed ecosystems that span data centers, public clouds, edge locations, campuses, and branch offices. As workloads, applications, and data become more dispersed, the network serves as the connective tissue that brings these environments together.
Its ability to deliver consistent performance, visibility, security, and automation increasingly influences business outcomes. In many respects, the success of future AI initiatives will depend not only on the sophistication of the models being deployed, but also on the strength of the network architecture supporting them. Organizations that recognize the network as a strategic enabler rather than simply an operational necessity will be better positioned to scale AI, accelerate innovation, and realize greater value from their technology investments.
Many organizations won't discover infrastructure limitations in their AI network infrastructure until AI initiatives begin scaling.
By that point, remediation becomes more expensive and disruptive.
Traditional enterprise traffic patterns were largely predictable.
AI changes that dynamic.
Modern AI applications continuously exchange data between users, applications, models, and data repositories. Agentic systems add even more complexity through constant machine-to-machine interactions.
Organizations are increasingly seeing demand for symmetrical traffic patterns where uploads become just as important as downloads.
AI performance depends heavily on speed.
The longer it takes data to move between systems, the slower AI responses become.
This challenge becomes even more pronounced as organizations distribute workloads across multiple data centers, cloud providers, and edge environments.
Real-time inferencing and retrieval-augmented generation require near-instant access to data and resources.
AI is creating unprecedented demand for computing resources.
Analysts project dramatic increases in data center power consumption over the coming decade as organizations deploy larger AI environments and more powerful GPU clusters. Goldman Sachs Research forecasts that global power demand from data centers will increase by as much as 165% by 2030 compared to 2023 levels, driven largely by AI workloads and the infrastructure required to support them.
Organizations that fail to optimize network efficiency may find infrastructure costs escalating faster than expected.
Traditional cybersecurity models were designed around a relatively predictable environment in which human users accessed applications, systems, and data through defined pathways. Security teams focused on managing identities, controlling access, and monitoring interactions between people and technology.
Agentic AI introduces a fundamentally different operating model. Rather than simply responding to user requests, AI agents can independently interact with other agents, large language models, enterprise applications, external systems, APIs, and data repositories. These interactions occur continuously and often at machine speed, creating complex webs of communication that extend far beyond traditional user-driven activity.
As organizations deploy more autonomous AI capabilities, the volume and complexity of these interactions will continue to grow. The result is a significantly expanded attack surface that can introduce new security risks, governance challenges, and visibility gaps. To effectively manage these environments, organizations will need greater levels of automation, intelligence, and real-time monitoring to identify threats, enforce policies, and maintain control across an increasingly dynamic ecosystem.
The rapid growth of AI is accelerating a broader shift toward distributed infrastructure. As organizations generate and consume more data across locations, devices, and applications, they increasingly need to process information closer to where it originates. This approach helps reduce latency, supports data sovereignty requirements, improves resilience, optimizes costs, and enables the real-time performance required for modern AI inferencing.
As a result, data and workloads are no longer confined to a single data center or cloud environment. Instead, they are distributed across a combination of on-premises infrastructure, public clouds, edge locations, campuses, and branch offices. While this distributed model offers greater flexibility and performance, it also introduces new operational challenges. Gartner describes agentic AI as autonomous systems capable of planning and taking actions to achieve goals with limited human intervention.
Organizations must maintain consistent security policies, governance standards, operational visibility, and performance levels across an increasingly diverse technology landscape. Managing these environments in isolation can create complexity, increase risk, and make it more difficult to scale AI initiatives effectively.
This is why modernization efforts today are about more than upgrading infrastructure. The goal is to create a unified foundation that can securely connect people, applications, data, and AI workloads wherever they reside. Organizations that successfully establish this foundation will be better positioned to support current AI initiatives while remaining agile enough to adapt to future demands.
Preparing for agentic AI requires a new way of thinking about infrastructure modernization.
Instead of focusing solely on individual technologies, organizations should evaluate how infrastructure components work together to support AI outcomes.
Several priorities are emerging:
Organizations need unified management capabilities that reduce operational overhead and improve visibility across environments.
Networks must be capable of supporting increasingly demanding AI workloads without sacrificing reliability or user experience.
Security can no longer be treated as a separate layer.
It must be embedded throughout the infrastructure stack and capable of adapting to evolving threats.
Organizations need infrastructure strategies that support growth while controlling costs, energy consumption, and operational complexity.
AI itself is increasingly being used to optimize network operations, automate troubleshooting, improve visibility, and accelerate issue resolution.
These priorities align with a broader industry movement toward AI-native operations, where infrastructure becomes more autonomous, adaptive, and resilient.
The organizations gaining the greatest value from AI are not simply deploying models.
They are modernizing the environments that support them.
That includes networking.
As AI becomes more autonomous, the network's role will continue expanding.
It will influence how quickly organizations can innovate, how securely they can operate, and how effectively they can scale new capabilities.
The question for IT leaders is no longer whether AI will impact infrastructure planning.
The question is whether current modernization strategies are accounting for the realities of agentic AI.
Because while many organizations believe they are prepared, the data suggests otherwise.
The gap between perceived readiness and actual readiness may be one of the biggest obstacles standing between AI experimentation and AI-driven business transformation.