7 Ways how AIOps transforms incident management

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Why are AIOps (Artificial Intelligence for IT Operations) necessary for incident management at the current time?

In November 2023, Blue Yonder, a supply chain tech provider experienced a ransomware attack, which crippled Starbucks’ employee payroll and scheduling systems, and disrupted inventory for UK supermarkets like Sainsbury’s and Morrisons. 

In 2024, a faulty software update from CrowdStrike affected millions of Windows users and impacted critical industries.

It is clear that traditional incident management strategies are struggling to keep up with the diverse IT risks. 

Instead of reacting to security risks and disasters, you need systems to predict, prevent, and automate responses so incidents don’t cripple operations. Let’s uncover how AIOps can resolve such incidents and secure mission critical systems.

How predictive analytics and automation are amping up incident management

Are predictive insights absolutely necessary for effective incident management? 

Judging by the severity and ingenuity of cyberattacks and evolving risks, yes. And that’s one of the use cases of AIOps.

In a way, AIOps changes how enterprises handle incident management. It combines machine learning and big data analytics to identify anomalies, correlate events, and automate remediation. 

This lets you focus on real threats rather than chasing false alarms.

So why is this necessary? 

• Event correlation: AIOps analyzes millions of logs and alerts in real-time to detect patterns across systems. 
• Anomaly detection: Instead of waiting for failures, AI flags unusual behavior before it escalates.
• Automated remediation: AIOps detects problems and fixes them. 

In fact, IBM says that AI can reduce IT alert noise by 50% and eliminate 80% of the time spent fixing false-positive incidents. This means you can spend less time sorting through endless notifications!

Accelerate incident management with real-time AI detection & resolution

No one likes to wake up at night to manage a crisis, especially if it’s not a genuine alert.

Take Sony PlayStation, for example. Their IT teams were drowning in alerts, constantly escalating issues, and waking engineers at 2 AM. The company integrated BigPanda’s AIOps and cut down alert noise, allowing their Network Operations Center to spot real issues faster and reduce downtime.

Like Sony, many organizations are adopting AIOps to enhance incident management. 

Companies like Dell also offer AIOps to cut through IT complexity. Their APEX AIOps platform delivers AI-driven observability and incident management, helping IT teams resolve infrastructure issues 10X faster.

7 Strategic benefits for AIOps-driven incident management 

Now, let’s take a deeper look at how AIOps makes IT operations proactive and more cyber resilient.

1.  Improved decision-making and resource optimization

AI-driven decision intelligence enables faster, data-backed decisions, helping you to allocate IT resources more effectively.

Gartner predicts that decision intelligence will become mainstream within five years, enabling businesses to optimize IT resources and incident management through predictive insights.

2. Proactive issue resolution and enhanced reliability

If you can identify issues and resolve them before a severe impact, then you can prevent costly disruptions. That’s why you need AI-driven solutions.

Back in 2022, Forrester predicted rapid growth in AI-driven software. By 2025, AI-infused software is expected to grow at a 22% CAGR. 

This means you can leverage real-time monitoring and AI-driven insights at a lower cost, detect issues faster, and take proactive action. 

We can also say that resolving issues before they escalate will become a standard practice shortly.

3. Stronger security posture

Automated threat detection reduces security risks while being compliant with evolving regulations.

We’ve seen how cybercriminals leveraged AI-driven attacks since the launch of GPT. Now, hackers also use generative AI tools to optimize their attacks.

Traditional cybersecurity methods for business resilience are not adequate. However, Advanced AI-driven solutions can detect ransomware in less than 60 seconds, using machine learning to analyze data patterns and identify anomalies in real-time.