AI has speed, information, and automation in spades. Unfortunately, it lacks context, judgment, and accountability.
Artificial intelligence can scan and index 100+GB/s of logs in real time while responding to thousands of queries at once. It can continuously analyze network traffic patterns, spot bottlenecks and inefficiencies, and adjust resources to keep systems running smoothly. Even so, AI won’t fix your network for you. When organizations face network challenges, what they often need most is something only humans can provide: visibility, discipline, and less wishful thinking.
AI has clear strengths and clear limits. Problems arise when leadership treats AI as a decision-making authority rather than as a tool that surfaces signals. Understanding what AI does well only matters if its blind spots are acknowledged and managed.
AI is only as good as the data it is fed
AI can vastly outperform humans at busy work, predictive maintenance, and pattern recognition. These strengths can lead to faster incident response and quicker root cause analysis, shaving valuable time off an intrusion or network failure. But AI is only as good as the data it learns from, and new problems emerge constantly. AI can address known issues, but it is always pattern-matching against existing data sets. What happens when bugs or bottlenecks arise in software that did not exist when the model was trained? Or when human or vendor errors occur that AI network management has no way of knowing about?
Correlation vs. causation
AI is highly effective at spotting correlation. Problem solving, however, requires more than identifying a pattern. It requires understanding why it exists. AI network monitoring may correctly identify topology changes across multiple devices, but it cannot determine their causes. That context comes from human engineers. AI can flag routing changes, but it cannot know whether they were caused by a new hire making a mistake, a design flaw creating instability, or a deliberate attack. Was it a hacker, and if so, have similar threats affected other organizations, perhaps without being publicly reported? AI excels at detecting changes in digital patterns. Legitimate maintenance and remediation require understanding those signals within the messy reality of physical systems and human behavior.
Clients demand accountability
AI cannot build trust, take responsibility for a bad decision, or provide meaningful reassurance during a crisis. If companies were primarily AI-driven and dealing only with other AI-run organizations, that might be different. In reality, most businesses still work with people. Successful operations depend on human relationships and an understanding of imperfect, shifting needs. Clients want to know who they are doing business with and who will step up when something goes wrong.
Reading the room requires human connection. AI lacks instinct or the network engineer’s intuition developed over years of real-world experience. The credentials, judgment, and assurance of a human engineer remain a company’s greatest asset when resolving complex network issues.
Escalation and decision-making require human know-how
Escalation is a critical part of resolving network issues. Human engineers bridge the gap between what AI systems surface and the realities of organizational, regulatory, and interpersonal constraints. Network management requires understanding all of these systems and making sound judgment calls. Addressing discrepancies in accounting software may need to wait until after tax season. Restarting services may not be appropriate during a critical pitch meeting. Engineers act as interpreters, determining whether alerts are truly necessary and how best to apply recommended fixes.
Consider the following scenario: AI network monitoring can detect threats, reducing the average time spent per incident by 86%, according to Fortinet. That can reduce response time from weeks to roughly an hour. But that hour still demands human judgment. What kind of threat is it? Is the access a remote employee logging in for the first time, or a genuine breach? Does the incident require regulatory reporting? How much data was accessed, and how sensitive was it? Which clients must be notified, and when should leadership be involved?
AI is a powerful tool for staying competitive, but it is not enough on its own to fix or maintain a network. Accountability and visibility matter just as much. Organizations that rely on automation without investing in people risk confusing speed with control. Human expertise remains essential for long-term resilience.
Sources: Engineering at Meta, Fortinet, Mesh-AI
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