Complete guide to AIOps_ Automate IT Operations with AI.pdf

Complete guide to AIOps: Automate IT
Operations with AI
In the fast & digitally evolving world today, staying ahead of the curve is
essential, and that's where modern DevOps meets the game-changing realm
of AIOps! Imagine a world where automation and artificial intelligence work
hand in hand to supercharge your operations—it's not just a dream!
With AIOps adoption skyrocketing, a recent study shows that 60% of
organizations have embraced these innovative tools, and the AI market in IT is
expected to hit a whopping 1339.1 billion by 2030. Buckle up as we explore
how the modernization of DevOps, infused with AIOps, can pave the way for a
brighter, more efficient future for IT businesses!
In this blog you’ll delve into the overview of AIOps, how AIOps is important for
businesses, types, differences in DevOps & AIOps, benefits of integrating
AIOps in DevOps, challenges with AIs modernisation along with few great
success stories slaying at their business through AIOps adoption and a
checklist to check whether you AIOps is performing at its best or not! Ready to
take charge? Let’s go.
AIOps Overview
Artificial Intelligence is a revolutionary force for innovation in this era. The
term AIOps was first coined by Gartner in 2016. You can easily de-abbreviate it
as ‘Artificial Intelligence for IT Operations’.
Though not just AI, this approach is also about utilizing all the superpowers of
new-age technologies such as big data analytics, Natural Language
Processing (NLP), and Machine Learning (ML) to automate identifying &
resolving common IT operation issues.
Based on the Global CIO Point of View, nearly 90% of CIOs are already using
machine learning in some capacity. Whereas, 87% of CIOs reported that

machine learning provided “substantial value” or “transformative value” to the
accuracy of decision-making. Indeed, this technology is here to stay for long!
Three stages of AIOps processing
The construct of AIOps works by collecting large volumes of data from
different systems and identifying the root cause of the issue for an accurate
diagnosis & solution.
By utilizing big data analytics, machine learning & other technologies to their
maximum potential, the working of AIOps involves 3 major stages as follows:
1. Observe: It is the initial phase of AIOps. In this, AI has to identify and
report IT incidents as they occur or have happened in the past. Some of
its core processes are:
● Historical analysis

● Performance analysis
● Finding core issues
● Finding the overloaded devices
● Service faults detection
● Analyze various events, logs, and metrics
2. Engage: This step is about checking on potential IT incidents and
reporting them in advance. Some of its core stages include:
● Anomaly detection
● Changes in impacts
● Capacity management
● Predicting faults, overloads, or other failures in advance.
3. Act: Since all the issues have been churned out, it's time for AIs to
automatically fix them or share their reports with humans for final
resolution. Its major stages include:
● Root cause analysis
● Automated or assisted maintenance
● Automated or assisted optimization of the network.
● Offering technical support.
Apart from AIOps, it is also denoted as - IT operations analytics (ITOA),
advanced operational analytics, AI for ITOM, IT data analytics, and Cognitive
Operations in the DevOps industry.
Also read- Ultimate to DevOps– principles, how it works, and real-life
examples
How AIOps is important for IT businesses?

So far, you’ve understood the AIOps definition & its core functionality. But how
implementing AIOps can transform your IT business? Here’s your answer:
1. Reduced operational cost
The AIOps technologies easily fetch actionable insights out of large volumes
of data that required big IT teams before. This optimization of resources with
a single system and lean team of data experts significantly impacts the entire
operational costs. That too without compromising the functionality of IT
operations. Isn’t that a great heads-up?
2. Faster MTTR (Mean Time to Resolution):
Artificial intelligence can easily function beyond the operational noise and
provide faster analysis & resolution of issues as compared to manual speed,
which eventually results in reduced and faster MTTR.
3. Reduces downtime:

AIOps system, not only assesses large volumes of data but also gives you a
heads up for issues before they even impact operations. This significantly
affects your IT system performance & helps you to be prepared for disruptions
in advance. Resolving all such problems helps you avoid frequent downtimes.
4. Support for cloud migration:
AIOps platform offers an in-depth solution for managing public, private, or
hybrid cloud environments. With this approach, your organization can shift
workloads from traditional systems to cloud infrastructure without the
complexities of intricate data transfers across the network. It enhances
visibility, allowing your IT teams to efficiently manage data across various
storage solutions, networks, and applications.
5. Enhanced customer experience
When AIOps resolves the majority of IT operations in seconds and identifies
potential bottlenecks through extensive big data analysis, your company will
naturally deliver improved service quality, becoming a signature of your brand
and customer experience.
6. Proactive maintenance
AIOps technologies aids in forecasting potential issues and implementing
preventive measures proactively. This means you can say goodbye to the
unexpected maintenance costs that typically arise at the end of each quarter!
Difference between AIOps & DevOps

DevOps is a software practice that bridges the gap between development and
support workflows. It helps organizations apply changes and quickly address
users' concerns by sharing information between software and operations
teams.
On the other hand, AIOps is an approach for using AI technologies to support
existing IT processes. DevOps teams use AIOps tools to assess coding quality
and reduce software delivery time continuously.
Whereas DevOps focuses on accelerating and refining software development
and deployment, AIOps uses AI to optimize the performance of enterprise IT
environments, ensuring systems run smoothly and efficiently. AIOps platforms
use ML and big data analytics to analyze vast amounts of operational data to
help IT teams detect and address issues proactively.
When used in tandem, AIOps, and DevOps services can help businesses
create a complementary, and detailed approach to managing the entire
software lifecycle.

What are the types of AIOps?
AIOps presents new opportunities for your organization to enhance operations
and lower costs. However, to make informed decisions, it's essential to
identify your specific needs to determine which AIOps solutions will best meet
them. Here are the two primary types of AIOps you should be familiar with:
1. Domain-centric AIOps are AI-driven tools customized to operate within a
defined scope. For instance, operational teams utilize domain-centric
AIOps platforms to oversee the performance of networks, applications,
and cloud services.
2. Domain-agnostic AIOps are solutions that allow IT teams to expand
predictive analytics and AI automation beyond network and
organizational boundaries. These platforms aggregate event data from
various sources and correlate it to generate valuable business insights.
Benefits of integrating AIOps in DevOps
AIOps & DevOps both are an integral part of revolutionizing the modern-day
businesses. But the significant impact AIOps implementation in DevOps
brings, are as follows:
● Increased speed and collaboration: The integration of automation
enables Site Reliability Engineers (SREs) and developers to enhance
their collaboration and efficiency. It results in significant savings in time,
money, and resources for organizations.
● Efficient development to production workflow: AIOps system facilitates
a smooth transition from development to production for DevOps teams

by automatically responding to changes within production
environments.
● Machine learning advantages: Machine learning enhances AIOps by-
Identifying anomalies, anticipating performance issues, diagnosing root
causes and providing insights for DevOps teams to prioritize workflow
improvements and make informed decisions.
● Consistent release-ready code: AIOps creates a framework where code
is always prepared for deployment, ensuring readiness at all times.
Case studies showing successful AIOps adoption
1. ServiceNow IT operation’s growth
ServiceNow has experienced rapid growth, which posed challenges for their IT
operations team in consistently delivering outstanding experiences for both
support teams and users. As their workforce expanded, ServiceNow found it
increasingly difficult to manage the surge in requests.

They primarily focused on addressing immediate issues and responding to
rising demands, struggling to onboard new talent. This led to dissatisfaction
due to the high number of outages they faced. To tackle these challenges,
they sought a scalable and cost-effective IT operations model and turned to
AIOps.
The results have been impressive. Across the company, they have saved $1.5
million annually, improved software reclamation by 10%, and reduced events
to alerts by 96%. Their employees are delighted, as reflected in their employee
satisfaction score of over 99% and nearly doubled their productivity.
2. Airtel's AI-driven operations
Airtel has made significant strides in reducing customer frustration to nearly
zero levels through AIOps uses in operations. By implementing an AIOps (AI
for IT ops) framework and advanced automation, Airtel has increased both
efficiency and speed.
AI-driven automated operations introduced a unique Customer Frustration
Index to Airtel’s existing business support systems (BSS) led to the creation of
allowing real-time measurement of experience improvements.
In terms of order processing they reached to 90% reduction in order fallouts,
with 92% of order fallouts resolved using automated and self-healing
capabilities. For bill delivery, 99% of bills were delivered within 24 hours, and
100% of bills were paid via self-service online or mobile apps accurately.
These transformations illustrate the powerful impact that AIOps system can
have on IT operations and customer satisfaction. Want your systems to dope
the charts too? Check our modern custom DevOps solutions.
AIOps Challenges
Surely with every new tech, there are some unexplored hazards that can limit
your development growth. Out of these, the three main challenges with AIOps
are skills gaps, security, and scalability. Let’s take a brief look at each one.

Challenge 1: Skills gap
Many IT departments are still in the early stages of adopting AIOps. It’s
essential to invest in training and development programs to close the skills
gap within your IT teams. Additionally, consider partnering with external
training organizations or recruiting AIOps specialists to enhance your team's
expertise.
Challenge 2: Security
The integration of AIOps tools can potentially create new security risks for
your systems. It is crucial to prioritize cybersecurity measures and verify that
AIOps tools meet industry standards and regulations. Implement strong
encryption and access controls to protect sensitive information.
Challenge 3: Scalability
As your organization expands its use of AIOps-based systems, scalability may
become a concern. Ensure that you select AIOps tools capable of growing
alongside your organization. Regularly evaluate your infrastructure
requirements and upgrade your tools as needed to maintain scalability.
AIOps Checklist: Assess yours now!
Make sure your AIOps is functioning at its peak with the following checklist:

1. Data collection & aggregation
Diverse data sources: Gathers information from a variety of sources, including
networks, applications, databases, tools, and cloud environments.
Various data formats: Collects data in multiple formats such as metrics,
events, incidents, changes, topology, log files, configuration data, KPIs,
streaming data, and unstructured data.
2. Data management & storage
Centralized access: Organize data in a central location for easier access and
analysis.

Efficient management: Implement indexing and expiration processes to
manage data effectively.
3. Data analysis & ML
Machine learning utilization: Use machine learning for detecting patterns,
identifying anomalies, and conducting predictive analytics.
Alert significance: Differentiate meaningful alerts from irrelevant noise.
Correlate and contextualize data for precise problem identification.
4. AIOps as a strategic overlay
Integration with existing tools: Combines AIOps with current monitoring tools
and investments to provide a cohesive view of IT operations.
5. Automation and orchestration
Automated workflows: Transforms knowledge into automated response and
remediation processes.
6. Continuous learning
Historical data utilization: Uses past data to enhance problem management
and resolution over time.
Conclusion
AIOps has significant potential to transform IT operations. With the increasing
prevalence of predictive analytics, automated root cause analysis, and other
advanced features, AIOps is set to change how IT professionals manage and
enhance complex infrastructures. Adopting these innovations and keeping up
with the latest AIOps trends will be essential for maintaining competitiveness
and achieving success in the ever-evolving IT operations landscape.
If you want to maximize the benefits of AI technologies like AIOps or DevOps,
Peerbits can be your ideal partner for success. We assist you in automating

the tracking, monitoring, and analysis of your infrastructure using best
practices from DevOps and other innovative solutions, allowing you to address
issues before they escalate. Staying updated is the key to today’s success and
Peerbits can help you do that with ease.

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