Modern IT systems are awash with a constant flow of data providing information on system performance and security. This data is crucial for IT professionals looking to keep IT infrastructures running smoothly.
The snag is, with so much information available, it’s almost impossible to decipher what’s routine and what potentially could pose a threat. Trying to figure out the digital wheat from the chaff requires more than just visibility. It requires increasingly sophisticated systems that can interpret, prioritize and act—not simply collect information.
Unfortunately, most observability tools today don’t work this way. They generate alerts, log events and surface anomalies. And while technology is improving all the time, they don’t always understand what’s happening or how to respond.
Take a typical global enterprise running a hybrid architecture. It may have critical applications that are spread across multiple cloud providers, while also relying on on-premise legacy systems. Together, these systems are shepherded by dozens of monitoring tools generating thousands of alerts every day.
Some are false positives. Others are minor policy breaches. But lurking somewhere in the noise is a genuine security incident. And by the time it’s spotted… well, it could be too late.
Chief Technology Officer, SolarWinds.
Observability is becoming more intelligent
What’s missing isn’t just more tools or greater visibility. It requires a new, highly sophisticated level of observability—one that works more like a human brain with the ability to filter out noise, recognize what matters and trigger the right response at the right time. What’s needed is something intelligent that can ’think’ for itself.
Part of the reason why this is needed is because IT teams have tended to invest in separate tools that often have little contextual awareness. That means it’s up to the human members of IT teams to bridge the gaps, deciding whether an alert is serious, identifying the root cause and initiating the right response. In fast-moving environments, these human assessments can take time which, in turn, adds to the risk.
An intelligent observability system, on the other hand, would do more than simply monitor for known issues. It would detect anomalies in real time using context-aware monitoring, then assess the severity and likely impact based on both the technical and business relevance and risk.
Rather than treating every signal the same, it would prioritize based on urgency and risk, helping teams focus on what truly matters.
Crucially, it would also support automation, enabling routine fixes or containment measures to be initiated. And instead of splitting insight across multiple disconnected views, it would bring together data from on-premises and cloud environments in a single, cohesive picture.
This kind of system doesn’t just monitor IT systems and networks. It has total oversight and is ready to act when needed.
So, how close are we?
The good news is that progress is being made. AI-driven observability is moving from aspiration to implementation. Anomaly detection based on behavioral baselines is becoming more accessible, helping teams distinguish real issues from false alarms. Alert correlation and intelligent escalation paths are improving, reducing alert fatigue and bringing the right signals to the right people at the right time.
Some observability platforms, including those being developed at SolarWinds, are already combining monitoring, analysis and response into more cohesive workflows. Integration across hybrid environments remains a challenge, but the building blocks for intelligent observability are now in place.
What’s still missing, though, is the kind of full-system intelligence that can replicate the nuance of human decision-making. Most observability tools still rely on thresholds, templates, or predefined rules. True context-awareness—the ability to understand why something is happening and what to do next—is still emerging. But the direction of travel is clear.
Why this matters now
According to a recent SolarWinds AI and Observability report focused on the public sector, three-quarters of respondents said hybrid environments were hard to manage. Top concerns included data protection, integration complexity and a lack of visibility across systems.
Managing this complexity is made harder by the reality that observability tools are often siloed—one for cloud, another for on-prem, with separate platforms for detection, logging and remediation.
Security only adds to the unpredictability. In the same report, more than half of IT professionals said insider mistakes were contributing to serious threats, while 59% highlighted increasingly sophisticated attacks from external actors. The rise of generative AI means those external threats are becoming more scalable and targeted, increasing the strain on overstretched IT teams.
Which is why the key is not to add even more tools but to reduce complexity, improve visibility and act with intelligence and speed. An observability system that functions more like a brain does exactly that, because IT systems need to do more than observe. They need to understand.
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