Enterprises rethink AI infrastructure as systems take control

For years, digital infrastructure functioned quietly in the background as a collection of data centres, APIs, cloud services, and pipelines engineered to keep systems running. It processed workloads, scaled compute, and ensured business continuity.

This, however, is changing in 2026. Today, infrastructure is no longer passive. Instead, it is being reshaped to participate—to govern, to interpret, and in some cases, to decide. The trend is also reflected in three recently published studies—Cisco’s 2026 Data and Privacy Benchmark, New Relic’s AI Impact Report, and HCLSoftware’s Tech Trends 2026.

The reports suggest that the change is not about migrating to the cloud faster or modernising data pipelines incrementally. Rather, it is about building trusted, observable, explainable, and adaptive infrastructure because it operates beneath systems that think and act.

This transformation, the reports establish, is being driven by a single force: artificial intelligence (AI). And not just AI as a tool, but AI as an actor—systems that can analyse, reason, and execute autonomously. Enterprises are no longer just deploying AI in applications; they are having to rethink how infrastructure enables and controls intelligence at scale.

The shifts are also exposing a widening gap between the pace of AI capability advancement and the pace of underlying infrastructure adaptation to support them.

AI Has Entered the Stack. Is It Ready?

While AI is rapidly getting integrated across every possible digital asset, experts point out that the gap between rapidly advancing AI systems and slower-moving infrastructure foundations is becoming difficult to ignore.

According to the Cisco report, while 90% of organisations have expanded their privacy programmes to meet the growing need of AI adoption, 93% expect to increase investment in privacy and data governance in the next two years. Yet only 12% report having mature AI governance structures in place.

This mismatch is not about risk aversion. It reflects a deeper architectural strain. Most enterprise infrastructure was built to support human-led decisions, and not autonomous systems. That limitation is now being exposed as organisations deploy agentic AI: intelligent software agents that can operate independently of human prompts.

The HCLSoftware study reports that more than 80% of enterprises are either piloting or scaling such agent-based systems. As these agents move into core workflows, they are placing pressure on every layer beneath them—data, observability, integration, and policy.

Enterprises are also discovering that the speed of AI execution is meaningless without the infrastructure to govern and support it.

Data Systems Must Enable Real-Time Context

Even though data remains the critical input to any intelligent system, data infrastructure in most organisations struggles to meet the demands of autonomous AI.

According to Cisco, 65% of enterprises report challenges in accessing high-quality, well-tagged data. Additionally, 70% are concerned about the potential risks of using proprietary or customer data for AI training. The issue is not only about scale. It is about context, control, and clarity.

HCLSoftware highlights that the next generation of AI systems requires data infrastructure that can support real-time orchestration, not just storage and retrieval. The focus is moving from collecting information to connecting it. This implies a transition from centralised data lakes to distributed architectures — data meshes and fabrics that can feed AI models with governed, localised, and timely data.

In this new model, infrastructure is not just a warehouse for information; rather, it is a live context engine.

Observability is the New Infrastructure for Trust

Monitoring systems have always played a supporting role in enterprise IT. But with AI in the loop, observability is being redefined as an active, decision-supporting layer.

New Relic’s AI Impact Report provides clear evidence of this shift. In 2025, accounts using its AI-powered observability features achieved a 25% reduction in mean time to close incidents. In peak periods, the performance gap widened further. These platforms did not simply monitor for alerts—they correlated signals, identified probable root causes, and surfaced actionable incidents faster than human teams could process manually.

This is no longer traditional monitoring. Observability platforms are becoming intelligent systems in their own right—interpreting signals, reducing operational noise, and enabling faster action. In doing so, they return engineering time to innovation. Teams using New Relic AI achieved deployment velocity up to five times higher than their non-AI peers.

This level of signal clarity and operational feedback is no longer a luxury. It is now a prerequisite for deploying AI systems responsibly.

Governance is not an Overlay: It Must Be Embedded

Across all three reports, one message stands out clearly: governance must become an architectural function. It cannot remain a policy overlay applied after systems are in place.

While Cisco’s research underscores this challenge, HCLSoftware’s study argues that governance is evolving from a compliance requirement into a design principle. While most organisations acknowledge the need for AI governance, very few have embedded it meaningfully within their infrastructure. Also, responsible autonomy must be enabled by traceable data flows, auditable decision paths, and explainable system outputs.

New Relic’s AI observability model reflects this principle in action. The platform logs, correlates, and explains changes and their impact, creating a runtime audit trail. In doing so, it brings governance into the feedback loop—not just at the edge but at the core of operations.

In an AI-led environment, monitoring alone is no longer enough. Enterprises must understand, trace, and defend the decisions being made—not just by humans, but by the systems themselves.

Infrastructure Must Support Local Autonomy

As AI systems operate across borders, another layer of complexity emerges: jurisdiction. Hence, data localisation, compliance with regional rules, and sovereignty concerns are reshaping businesses’ infrastructure strategies.

The Cisco study highlights that 85% of global organisations have reported increased costs and complexity due to data localisation requirements. Yet trust in localisation is declining, with belief in the superiority of locally stored data falling from 90% in 2025 to 86% in 2026.

To address this, HCLSoftware introduces the concept of "sovereign intelligence," in which AI systems must operate intelligently across regions while respecting local rules. This is expected to drive a new class of infrastructure design that can blend global scale with regional compliance—a ‘glocal’ approach where architecture is aware of legal, ethical, and operational boundaries.

No wonder, then, that enterprises must now build infrastructure that can act differently based on where it is operating, without compromising performance or transparency.

The three reports also confirm that digital infrastructure is undergoing an irreversible shift, and the systems supporting enterprise AI can no longer be treated as neutral enablers. They must become intelligent, explainable, and responsive in their own right.

Infrastructure today must do more than host services. It must monitor behaviour, orchestrate data, enforce governance, and adapt to change. It must be able to earn trust—not just for what it processes, but for how it supports decisions made by systems that think.

In fact, it is not a trend; it is a foundational change in how enterprises build, govern, and grow.

The image accompanying this story was created using AI. The article was written and reviewed by the author, with limited AI-based research and editing support.

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