AIOps: how companies can harness AI to reshape IT operations (www.techradar.com)

AIOps is being positioned as a transformative approach for IT operations that combines human expertise with AI, machine learning and advanced analytics to automate monitoring, troubleshooting and predictive maintenance across ITOps, NetOps and DevOps. Rather than a single product announcement, this piece argues AIOps is a strategic shift: platforms ingest vast telemetry streams, correlate events in real time, surface actionable insights, and automate remediation—reducing mean time to resolution (MTTR), improving application performance and strengthening security posture. Industry uptake is already high, with more than 84% of organizations using or planning AIOps. Technically, an AIOps stack relies on three core elements: advanced analytics to convert raw telemetry into signals, ML to detect subtle patterns and evolve automation, and predictive analytics plus real-time event correlation to act proactively during incidents or attacks. Its effectiveness hinges on high-quality, contextual data—faulty or fragmented inputs can produce conflicting automations or miss attacks (the “garbage in, garbage out” risk). The recommendation: adopt a phased rollout with a strong data governance strategy so AIOps can safely automate routine tasks, accelerate incident response, and free engineers for strategic work while avoiding dangerous false positives or blind spots.