AI for IT? Not Without Visibility First

Today, artificial intelligence is no longer confined to R&D departments or experimental labs. It’s showing up across enterprise IT stacks, automating help desks, detecting anomalies in network traffic, and optimizing application performance. According to McKinsey, 72% of companies now use AI in at least one function, yet most still rely on outdated, incomplete asset inventories. This rapid adoption reflects both the promise of AI and the pressure IT leaders feel to modernize fast.

But amid the race to embed AI into infrastructure, there’s a foundational flaw that’s often overlooked: visibility. Specifically, the lack of it.

Before AI can be truly useful in IT operations, whether it’s identifying a security threat or auto-scaling resources, it needs a reliable understanding of what it’s working with. And too often, the data AI depends on is built on top of incomplete, inaccurate, or outdated asset inventories. It’s like trying to program a self-driving car without a functioning GPS. The engine might be powerful, but it doesn’t know where it is or what’s on the road.

This is the next bottleneck in enterprise AI.

Why AI Observability Depends on Accurate Asset Data

AI thrives on data, but not just any data. It needs timely, structured, and trustworthy data that reflects current conditions. In an IT context, that starts with understanding what’s in the environment: devices, endpoints, workloads, users, cloud instances, shadow IT, and more.

The problem is, most organizations are flying blind. Asset management tools from a decade ago weren’t designed for today’s hybrid, dynamic environments. And newer solutions often depend on APIs or integrations that don’t reach deep enough. What results is an asset inventory that’s partial at best, misleading at worst.

When AI models are trained or deployed in this kind of blind spot, the consequences compound quickly:

• Security tools miss vulnerable devices because they were never cataloged in the first place.
• Performance insights are skewed by ghost machines or unmanaged endpoints.
• Automation scripts fail when they try to act on resources that no longer exist—or exist in duplicate.

In short, the data that’s supposed to drive smarter decisions ends up introducing more uncertainty. AI can’t create value if it’s acting on a fragmented map of the environment.

Visibility Challenges in a Hybrid, Decentralized World

The visibility challenge isn’t just a result of neglect. It’s a byproduct of how IT has evolved. Today’s environments span physical machines, virtualized workloads, multiple cloud platforms, SaaS apps, remote endpoints, edge devices, and containers. Some assets spin up and disappear in minutes. Others exist in hard-to-reach corners of legacy infrastructure. Responsibility for them may be split between in-house teams, contractors, and third-party providers.

Complicating matters further, enterprises are moving fast. Acquisitions, new tools, and departmental IT decisions all contribute to a sprawling landscape that changes by the day.

Trying to stitch together visibility across all of that is daunting. Many companies resort to spreadsheets, legacy CMDBs, or vendor-specific discovery tools that don’t communicate with each other. The result? Thousands of unknown, unmanaged, or orphaned assets, each a potential point of failure.

And that’s just on the inventory side. There’s also the issue of context. It’s not enough to know that a device exists; you need to know what it does, who uses it, how it connects to other assets, and whether it’s healthy. Without that, AI becomes a blunt instrument—detecting anomalies but not knowing what’s normal, spotting changes but not knowing if they matter.

Making Infrastructure AI-Ready

If AI is to deliver on its promise in IT, whether for observability, automation, or cybersecurity, enterprises need to start with a renewed focus on visibility. That means making asset intelligence foundational, not optional. Here’s what that requires:

Treat asset discovery as a continuous process: Traditional discovery tools work on scheduled scans. That’s not enough anymore. Environments are fluid. Assets can be spun up by developers, move across cloud providers, or shift IPs without notice. Real-time or near-real-time discovery should be the baseline.

Converge data sources to eliminate blind spots: Relying on a single feed, like an agent or a cloud API, won’t give a complete picture. Visibility must combine multiple methods: passive listening, API integrations, log analysis, endpoint telemetry, and network traffic. Each provides a different piece of the puzzle.

Build context, not just counts: Discovery is step one, but enrichment is where real insight begins. That means mapping assets to their business functions, owners, dependencies, and lifecycle stages. AI needs context to distinguish between a critical production server and a test VM.

Eliminate orphaned and unmanaged assets: It’s not uncommon to find environments with hundreds or thousands of assets that no team claims responsibility for. These create both operational and security risk. Bringing them under management, or retiring them entirely, should be a top priority.

Treat visibility as a strategic enabler: Asset intelligence isn’t just about IT hygiene. It’s the foundation for almost everything else: smarter automation, better threat detection, more efficient spending, and yes, trustworthy AI. Without it, every downstream insight is compromised.

The Blind Spot You Can’t Afford

AI in IT isn’t magic. It’s pattern recognition, automation, and reasoning built on data. But when that data is compromised at the source by poor visibility, broken inventories, or contextless assets, AI becomes just another layer of guesswork.

We don’t let pilots fly without instrumentation. Yet that’s what many organizations are asking of their AI systems today, expecting intelligent outputs from an invisible infrastructure. The future of IT will no doubt be more autonomous, predictive, and AI-assisted. But that future is only possible if we start by illuminating the landscape we’re asking AI to navigate. Before we can automate, we must see. Before we predict, we must understand. And before we trust AI to manage our infrastructure, we must make that infrastructure visible.

Anything else is just flying blind.