In the rush to adopt AI in IT operations, many organizations focus on feeding copilots as much data as possible. But here’s the problem: data without context is just noise. An IT copilot that can’t distinguish what matters from what doesn’t won’t reduce alert fatigue or accelerate troubleshooting. As we showed in the previous post, Real-World Use Cases for Natural Language Copilots, copilots can transform how engineers interact with their environments, but only when backed by a model that understands context.
In this post, we’ll explore why context is the defining ingredient for an effective IT copilot, how it transforms raw data into actionable insights, and how Selector’s architecture is designed to deliver context at scale.
The Difference Between Data and Context in an IT Copilot
- Data is raw input: SNMP counters, NetFlow records, syslogs, device configs, and time-series metrics.
- Context is the meaning added when those signals are normalized, correlated, and enriched with metadata like device role, topology, and service dependencies.
For example:
A sudden spike in CPU utilization on a router is data. Understanding that the spike coincides with interface flaps in syslogs, a configuration drift detected through CMDB integrations, and downstream packet loss for a critical ERP service — that’s context.
How Context Transforms IT Copilot Responses
A context-aware IT copilot can:
- Prioritize by impact — Selector’s Knowledge Service uses recommender and association models to weigh anomalies against service dependencies, flagging that a WAN outage disrupts ERP, not just a branch order.
- Suppress false positives — By ingesting maintenance window data into the Data Hypervisor, expected anomalies are prevented from triggering alerts.
- Correlate across sources — By combining SNMP data, data from sources like ThousandEyes, and syslog clusters, Selector links packet loss to an interface flap and a recent firmware update.
- Guide Remediation — Through the collaboration service, the copilot suggests CLI commands or automation workflows in Slack/Teams, based on the device type and topology.
This is what separates copilots that summarize logs from copilots that accelerate mean time to resolution (MTTR).
The Challenges of Giving an IT Copilot Context
Delivering this intelligence requires overcoming several technical challenges:
- Siloed Data Sources — Performance metrics in Prometheus, syslogs in Splunk, configs in Git, inventories in NetBox or ServiceNow. Selector’s Collection Service integrates with all of these (300+ integrations) to unify them.
- Unstructured Inputs — Traditional systems require thousands of regex rules for logs. Selector instead applies log mining with Named Entity Recognition (NER) to automatically extract key entities (IP addresses, interfaces, device IDs).
- Dynamic Environments — Hybrid WAN, wireless, and cloud topologies change constantly. Selector’s Digital Twin and topology-aware correlation keep context accurate even as networks evolve.
How a Context-Aware IT Copilot is Built
Selector embeds context into every layer of its architecture, ensuring our Copilot reasons effectively:
- Collection Service — Ingests telemetry, logs, configs, CMDB data, and anomalies via both push and pull mechanisms.
- Data Hypervisor — Normalizes inputs and enriches them with labels and metadata, decoupling raw sources into an abstract, unified data plane.
- Knowledge Service — Performs anomaly detection, ML-driven correlation, and root cause analysis across metrics, events, and logs.
- Collaboration Service — Exposes insights in natural language through Slack, Teams, or APIs, so operators can query and act in real time.
This architecture ensures that when an operator asks Selector’s copilot a question, the answer is not just “what happened”, but also why it happened, how it impacts services, and what action to take next.
Why Context is the Future of IT Copilots
As networks scale and complexity increases, data alone will never be enough. Operators don’t just need to know what happened. They need to understand why it matters and how to fix it quickly.
That’s why the next generation of IT copilots are context-first. Selector’s architecture makes this possible by combining ingestion, normalization, ML-driven correlation, and collaboration in a single platform. And it all culminates in a Copilot that provides precise, actionable guidance instead of vague summaries. In the next post of our How AI Changes Network Operations series, we’ll explore AI That Knows Networking: Selector vs. Generic GPT Integrations, showing why purpose-built AI is the only way to unlock the full potential of copilots in IT operations.
Learn more about how Selector’s AIOps platform can transform your IT operations.
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