AIOps

What is AIOps?

Struggling to keep up with IT cost optimization, cybersecurity goals, efficient app deployment, or quality hiring?

So are ITPro Today’s survey respondents. Almost half (43%) of them decided to make greater use of artificial intelligence for IT operations (AIOps) to overcome these challenges.

But there’s plenty to understand before investing in an AIOps solution. What does AIOps actually mean?
AIOps stands for artificial intelligence for IT operations. It involves harnessing AI capabilities to automate and optimize operational IT workflows.

The core technologies of AI for IT operations

Let’s dive into the mechanics of AIOps by understanding its core technologies:

• Big data: Large volumes of structured and unstructured data from a variety of sources.
• Machine learning: The practice of training software with data and algorithms to do human-like activities using artificial intelligence.
• Natural language processing: Enabling software to understand and use natural language in written or voice communication.
• Automation: Using technology to take action with minimal human intervention.

What is AIOps like in daily IT operations?

Transitioning from traditional IT operations to AIOps offers several benefits, including:

• Proactive issue detection: AIOps continuously monitors systems, detecting anomalies and potential issues before they affect users.

• Automated problem resolution: Routine problems are automatically resolved by AIOps, reducing the need for manual intervention and streamline the resolution process.

• Enhanced incident management: AIOps prioritizes incidents based on their impact, helping IT teams focus on critical issues first and streamline the resolution process.

• Data-driven insights: By analyzing vast amounts of data, AIOps provides actionable insights and trends, enabling IT teams to make informed decisions and improve system performance.

• Resource optimization: AIOps predicts demand and adjusts resource allocation accordingly, ensuring efficient use of IT infrastructure.

• Improved collaboration: AIOps integrates with existing IT tools and workflows, enhancing collaboration among team members and boosting overall productivity.

• Continuous learning: AIOps systems learn from past incidents and continuously improve their detection and response strategies, leading to better performance and reliability.

• User experience enhancement: By maintaining optimal system performance and quickly addressing issues, AIOps ensures a seamless and reliable user experience.

Benefits of AIOps

Perhaps a bigger question than “What is AIOps?” is “What can AIOps do for me?”

The answer: A lot.

The main advantage of AIOps lies in its ability to quickly identify, resolve, and mitigate slowdowns and outages.

Some of the benefits of AIOps include:

Reach a faster mean time to resolution (MTTR)

Using AI in IT operations lets you leave manual data analysis behind. The software does it for you faster and more thoroughly, pinpoints the root causes, and suggests accurate solutions.

Enhance observability and collaboration

While IT automation is important, generating visibility, transparency, and communication across ITOps, DevOps, governance and security functions empowers decision-making agility and better issue responses.

Reduce operational costs

Tools empowered by AI for IT operations automatically identify issues and engineer response scripts, saving your team days of work. Therefore, you can leverage your human resources investment for more strategic, innovative tasks.

In doing so, you elevate the employee experience, and your employee retention rates accordingly.

Transition to predictive and proactive management

Any AI IT Ops solution you choose needs to have predictive analytics, that continuously learns to identify priorities, abnormalities, and significant alerts. This way, you can tackle IT issues before they escalate into slowdowns or outages.

Now that we understand the key benefits, let’s dig into another important question:

How does AIOps work?

The implementation of AI in IT operations: How does AIOps work?

It’s time to drill into what AI Ops is like in organizations, and what your chosen tools must do for your AI operations to succeed.

Performance monitoring

The process begins with data collection from various sources such as logs, metrics, events, and alerts across the IT environment. This data is then ingested and aggregated into a central repository, providing a comprehensive view of the system. AI operations bridge this gap, as its tools monitor cloud infrastructure, virtualization, and storage systems.

They use event correlation capabilities to consolidate and aggregate data. They report on usage, availability, and response times, among others, thus enhancing information accessibility.

Observability

Observability tools and practices ingest, aggregate, and analyze the continuous stream of performance data across applications and hardware devices. This enables effective monitoring, troubleshooting, and debugging, which better serves customer expectations, service level agreements (SLAs), and business requirements.

Signal segmentation and anomaly detection

AIOps tools scan complex IT operations data, including extensive historical data. They then distinguish anomalies and other substantial signals from the surrounding noise and routine data. These help predict potentially problematic events, such as data breaches.

Root cause identification, analysis, and resolution

AI for IT operations helps you automatically cross-reference abnormal events with other event data across various environments, and reports it. 

It also suggests appropriate remedies, or even autonomously resolves problems – without human intervention. Often, even before humans are even aware of the issues.

Predictive analytics

Predictive analytics plays a crucial role in AIOps, enabling IT operations to become more proactive and efficient. By leveraging historical data and advanced machine learning algorithms, predictive analytics can forecast potential issues and performance trends before they impact the IT environment.

The process begins with data collection from various sources, including logs, metrics, events, and alerts. This historical and real-time data provides a comprehensive foundation for analysis. Machine learning models are then trained on this data to identify patterns and trends that precede certain types of incidents or performance degradations.

Once trained, these models can predict future events, such as potential system failures, capacity bottlenecks, or security threats. For instance, predictive analytics can identify patterns that suggest a server is likely to fail within a certain timeframe based on its current performance metrics and historical behavior. This allows IT teams to address the issue proactively, such as by scheduling maintenance or reallocating resources, thus avoiding unexpected downtime.

Predictive analytics also assists in capacity planning and resource optimization. By forecasting future demand based on current usage trends, AIOps can recommend adjustments to resource allocation, ensuring that the IT infrastructure is always adequately prepared to handle the expected load. This helps in optimizing costs and improving overall system performance.

In the realm of security, predictive analytics can identify potential threats by recognizing anomalous patterns that are indicative of malicious activity. This early detection allows IT teams to implement preventive measures, such as updating security protocols or patching vulnerabilities, thereby enhancing the overall security posture.

Proactive response

What is AI Ops without a proactive response?

When you feed predictive algorithms with application performance metrics, they identify patterns and trends aligned with different IT issues. Next, they forecast IT problems before they happen (or escalate), triggering relevant human or automated processes, and fast-tracking resolutions.

Continuous learning for future enhancement

Continuous learning is a cornerstone of AIOps, driving ongoing improvements in IT operations by enabling systems to learn from past experiences and adapt to new challenges. This dynamic capability ensures that AIOps platforms remain effective in the face of evolving IT environments and emerging threats.

Continuous learning also plays a vital role in predictive maintenance. By analyzing historical data and recognizing patterns that precede failures, AIOps can predict when components are likely to fail and recommend preemptive maintenance. This proactive approach reduces downtime and extends the lifespan of IT infrastructure.

The low hanging competitive advantage that efficient AI operations provide

In 2023, Accenture found that “90% of business leaders are applying AI [for]… operational resilience.” However, many are still trying to determine what is AIOps’ future, or the AIOps’ meaning for their organizations. 

According to Accenture, only 9% “achieved [operational] maturity on all fronts. Those that did average 1.4X higher operating margins over peers, while driving 42% faster innovation, 34% better sustainability, and 30% higher satisfaction scores.”

Here at Atera, we’ve made sure our AI-powered IT platform helps you achieve all that. Our constantly updated AI features include a comprehensive unification of remote monitoring and management, helpdesk ticketing system, billing, and reporting. We provide lots of automation, quick onboarding, and ease of use.

Champion a new way to manage IT with Atera’s AI-powered capabilities, driving your entire organization upwards. By leveraging AI, Atera delivers superior support to both employees and customers, effectively eliminating IT frustrations and ensuring higher levels of satisfaction on both sides of the ticket. The result is a faster, more consistent IT service experience that boosts overall organizational performance.

Atera’s AI extends your IT team, empowering technicians to elevate their skills. Transform your Tier-1 techs into Tier-2 experts and free up senior technicians to focus on strategic projects. With Action AI, you can take on any IT challenge seamlessly, enhancing your team’s capabilities without the need to increase headcount. This leads to higher productivity and enables your team to handle more complex issues with confidence.

Efficiency is turbocharged with Atera’s AI by reducing manual triage and accelerating ticket resolution through automated insights and recommendations. This diminishes mundane and repetitive tasks, driving faster SLAs and higher accuracy in troubleshooting. As a result, employees and customers experience quicker resolutions and reduced frustration, significantly enhancing operational efficiency.

Try Atera for free to see how AIOps can supercharge your organizational goals!