The term «AIOps» is frequently mentioned in the constantly evolving IT space. According to Future Market Insights, the AIOps market is already valued at over $6.7 billion and is projected to reach an impressive $80.2 billion by 2032. This substantial growth highlights its significance in the industry.
If you aim to stay competitive or even outpace your competitors in attracting customers through streamlined IT processes, AIOps should be your go-to technology. So, what exactly is AIOps all about?
To shed light on this emerging concept, this guide delves deep into AIOps, exploring how it operates. By the end of this comprehensive guide, you'll have a thorough understanding of AIOps and its potential impact on your organization.
What is AIOps?
AIOps, short for «Artificial Intelligence for IT Operations», refers to the application of AI-powered capabilities aimed at enhancing IT operations and performance. This approach harnesses various AI techniques, including automation scripts, machine learning models, big data analytics algorithms, and natural language processing systems, among others, to streamline IT processes.
AIOps platforms collect data from a variety of sources, including application logs, event data, configuration data, incidents, performance metrics, and network traffic. This data can be both structured, such as databases, or unstructured, such as social media posts and documents. AIOps platforms then use machine learning algorithms to analyze this data and identify patterns and anomalies. These patterns and anomalies can be used to predict and prevent problems, as well as to troubleshoot problems that have already occurred.
The term was coined by Gartner in 2016. Today, it has grown to become a key consideration for businesses in the US and across the globe. IBM’s survey of 500 organizations in the US shows that 25% of the surveyed businesses already use it, and a further 43% are exploring its usage. On a global scale, based on 7,502 businesses that participated, 35% implement it to facilitate IT operations.
How AIOps works
As stated earlier, AIOps uses automation scripts, big data analytics, and machine learning models to improve IT management workflows. There are four stages in the comprehensive application of these elements.
- Data Collection
- Automated Threat Detection and Prioritization
- Automated Response and Recovery
- Continuous Learning and Improvement
1. Data Collection
AIOps collates structured and unstructured data from the organization’s different IT systems onto a single platform. It allows you to automatically and continuously aggregate large volumes of datasets like application logs, application traffic data, latency data, and event data, among others. The source of this data includes distributed IT solutions like performance monitoring tools, event management tools, and even social media platforms.
Aggregated data plays a crucial role in enhancing the observability of application and IT systems through visualization. However, it is unfortunate that many organizations tend to halt their efforts at this point. While streaming, storing, and visualizing IT data and events is a significant step, it is not sufficient for achieving a truly effective AIOps implementation.
2. Automated threat detection and prioritization
AIOps is an approach that leverages artificial intelligence and machine learning to enhance and automate various IT operations tasks, including monitoring, event analysis, incident management, and more. When it comes to security, AIOps can be utilized to improve threat detection and response processes.
AI algorithms analyze the collected data to establish patterns of normal behavior within the IT environment. When deviations from these patterns occur, they may indicate potential security threats. Threats could include malware infections, suspicious network activities, or unauthorized access attempts.
The identified threats are then prioritized based on their severity and potential impact on the organization. High-priority threats that pose significant risks to the IT environment receive immediate attention and action.
3. Automated Response and Recovery
The response and recovery stage is where AIOps automatically moves to remediate negative IT incidents immediately when they occur.
This is a key stage in AIOps implementation that ensures proactive 24/7 security and control over the IT infrastructure without 24/7 human supervision.
4. Continuous learning and improvement
This stage is essential for ensuring that the AIOps platform is always providing the best possible results. It includes gathering new data, updating the AI models, improving the algorithms, and reevaluating the goals.
Gathering new data is important because it allows the AI models to be trained on the latest information. This helps to improve the accuracy of the models and allows them to identify new patterns and anomalies.
Updating ensures that the models are always up-to-date with the latest data. This helps to prevent the models from becoming outdated and inaccurate.
Improvement involves testing new algorithms and fine-tuning existing algorithms. This helps to improve the performance of the AIOps platform and allows it to identify problems more quickly.
Reevaluating the goals is the final step in continuous learning and improvement. This involves reviewing the goals of the AIOps platform and determining whether they are still being met. If the goals are not being met, then the platform may need to be improved or modified.
Common AIOps use cases
Which are the specific ways in which AIOps can be used to improve IT operations? These are the most common:
1. Performance monitoring
Is your organization adopting an enterprise-level support infrastructure with a vast ecosystem of components such as servers, virtual machines, deployed applications, among others?
With the imminent complexity of such an ecosystem, AIOps can help you efficiently monitor the performance of each of these different infrastructure components.
For example, you can aggregate and continuously analyze data on resource/component usage, application traffic, latency, and availability. This way, you’ll always be able to easily determine whether you meet service-level objectives (SLOs).
Efficient monitoring coupled with automated action on identified issues can lead to revenue increase.
For example, let’s say a retail company uses AIOps to monitor its e-commerce website. The AIOps solution identifies a pattern of customer abandonment at the checkout page. The company then uses this information to improve the checkout process, which results in a 10% increase in sales. The AIOps solution identifies a pattern of customer abandonment at the checkout page.
2. IT adoption and migration
AIOps proves useful at reducing human error and other risks associated with adopting or migrating to new IT environments and operations.
For instance, if you wish to adopt or improve DevOps workflows, AIOps can help eliminate the tedious processes around continuous integration and continuous delivery (CI/CD). The outcome is fast development.
With the adoption of complex infrastructures like hybrid cloud environments, AIOps can help with the following:
- Automatic tracking and classification of data
- Analysis of mixed requirements
- Separation of requirements
- Monitoring environment components
- Optimizing cloud resources
- Respond to threats while.
All these will eliminate a significant amount of human errors.
3. Internal and external anomaly detection
Detecting threats in dynamic user-based environments can be a daunting task. Internal administrators using IT management systems, as well as external customers using your software application, have unique access behaviors. This means one rule for threat detection cannot be comprehensive.
But thanks to the ML capabilities that accompany AIOps, unique baselines may be created for each user. With AI, you can continuously collate historical data, identify atypical datasets faster, and maintain more accurate proactive supervision over IT systems.
4. Root cause analysis
An unplanned downtime event is difficult to deal with, given you don’t know what caused it in the first place.
You can easily treat the symptoms, like getting your systems back up. But if you don’t know what caused the downtime, there would only be a recurrence of it. And sometimes, getting your systems back up may not even be possible without knowing the core problem.
AIOps makes it possible to identify the root cause of incidents and performance issues. Through continuous logging and AI-powered incident data analysis, you’ll easily track performance data and correlate activities leading up to a negative event.
Sometimes, you may realize the downtime wasn’t caused by a cyberattack but, rather, the inadequacy of some components in the system — for example cloud server resources. Without the need for human intervention, issues can be remediated much faster.
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5. Business impact analysis
Business impact analysis is all about identifying the effect of IT events on the organization’s business objectives.
With SLAs affecting the satisfaction or trust customers have in your services, AIOps makes it easy to monitor the impact of each IT event. You prioritize events or components with the most negative or positive impact, automate the appropriate operations workflows on them, and ensure all SLOs are promptly met.
Benefits of AIOps
We all understand how traditional/manual IT operations are laden with a lot of inefficiencies.
To give you an idea, Forbes shows that, in 2016 — the year of AIOps' inception, data scientists spent 19% of their time collecting data and a further whopping 60% of their time cleaning and organizing data. That's 79% of IT time spent on tedious repeatable tasks.
AIOps streamlines IT operations by replacing primary and repetitive tasks with automated workflows. By leveraging AI, your IT personnel are freed up to focus on more business-oriented tasks, resulting in faster and more accurate IT operations management.
More specifically, AIOps comes with the following advantages.
1. AIOps iIncreases observability over the IT infrastructure
Through automated data ingestion and visualization, AIOps allows you to unify data from multiple individual systems.
Real-time centralized monitoring gives you supreme observability, as you understand the overall health of the IT infrastructure. This includes the effects of dependencies, applicability of security functions, and the strength of governance policies.
2. AIOps improves collaboration
The intuitive visualization and real-time data processing greatly facilitate collaborative workflows among different IT teams.
With the help of AI-powered insights, decision-making becomes faster, eliminating delays where manual efforts fall short.
According to a survey conducted by Snaplogic, 49% of respondents consider the accelerated insight-generation and decision-making capabilities of AI as its most significant advantages.
3. AIOps reduces MTTD and MTTR
Thanks to ML models, anomalies are swiftly and accurately detected, resulting in a reduced mean-time-to-detect (MTTD) – the metric used to measure how quickly IT issues are spotted.
Moreover, automated scripts play a crucial role in decreasing the mean-time-to-respond (MTTR) in the face of performance-related issues, downtime events, identified threats, or security breaches. This proactive approach can lead to a remarkable 75 percent reduction in the time required to debug applications.
4. AIOps improves IT efficiency and reduces operational cost
AIOps increases productivity as the workload on IT teams is reduced significantly. They have more time to be creative and come up with innovative ideas for more efficient AI implementation and service improvements.
Productive and efficient workflows mean you enjoy savings on maintenance and repair. IBM says54% of AI-powered firms acknowledge this cost-saving advantage.
In the digital age where organizations are driven by massive technology, reducing IT costs is a priority. This article on how to reduce IT costs gives comprehensive insights on this subject.
Who needs AIOps?
The benefits of AIOps aren’t limited to just one class of firms or businesses. Whether for only data ingestion and secure storage, or informed product and business improvements, you need some level of AIOps adoption if you intend to run a digital, IT-powered organization today.
These are the types of business that AIOps is best suited for;
1. Large enterprises with complex IT needs
What unites these industries is that they are all large enterprises dealing with vast volumes of critical user information, substantial revenue, and tempting financial incentives for attackers.
If your organization operates within any of these industries or shares similar business risks, implementing AIOps becomes essential to bolster your operational management. AIOps offers the capability to maintain maximum uptime and safeguard user data through accelerated data analysis and incident response.
This is especially crucial considering that you likely manage a highly complex IT infrastructure, which requires diligent efforts from IT personnel to ensure the security of user data.
2. Small and Medium-sized Businesses (SMBs)
One significant reason for this is the lack of flexibility in various aspects of their business operations. This includes the ability to adapt products to meet customer needs, adjust services to the competitive environment, and align IT systems with product requirements.
While cloud-based IT management is a step in the right direction, combining it with the automated ingestion and analysis of both structured and unstructured data through AIOps can significantly mitigate key business risks for small enterprises.
AIOps offers several benefits to these businesses:
- The ability to swiftly identify trends and keep up with competitors.
- Improving product features and performance to enhance customer satisfaction.
Leveraging AIOps will greatly reduce the chances of falling into the 45% to 51% failure segment, potentially eliminating this risk altogether.
3. DevOps-powered teams
DevOps is a methodology that focuses on delivering products and features as fast as possible. This involves quick Continuous Integration (CI) for building and Continuous Delivery (CD) for testing and delivery. This results in intensive workflows for application management.
If your organization utilizes or plans to adopt DevOps for software development, AIOps can help you execute high-velocity workflows with increased accuracy.
You don’t just automate the building and testing of code. You also utilize AI to accomplish the following:
- Monitor support systems
- Identify conflicts between applications and infrastructure
- Propose solutions to conflicts
- Optimize the IT infrastructure to accommodate new features or CI/CD needs.
Please check this guide for more practical insights on DevOps.
Also Read: SRE vs. DevOps
AIOps: Sample success stories
Wondering if the emerging technology is truly effective? Here are a few specific examples of organizations achieving improved IT efficiency through AIOps.
1. Confiz enhanced a Fortune 500 retailer's IT efficiency with AIOps
Confiz is an IT service provider specializing in digital transformation and tackling complex IT challenges. One of its customers, a Fortune 500 retail client, was facing server issues across over 1,000 pharmacies in the US. The client urgently required a solution to predict and prevent downtime events while expediting responses to performance concerns.
AIOps emerged as the ideal solution, and Confiz promptly adopted the Autoregressive Integrated Moving Average (ARIMA) model. This sophisticated ML model effectively analyzed critical data points such as memory and CPU usage, system parameters, and queue metrics within pharmacies.
The ML model handled predictive and root-cause data analysis. It also sent out insightful alerts to facilitate response to downtime events.
The implementation of business intelligence visualization and the ARIMA model yielded these results:
- 80% increase in prediction accuracy.
- Hundreds of hours of IT productivity saved.
- Faster response times to downtime events.
- Mass server outages eliminated.
- Improved end-user experience.
Confiz's AIOps solution proved to be a game-changer for the retail client's IT operations, ensuring a smoother, more efficient IT landscape.
2. Taiwan’s NCHC used AIOps to reduce MTTD by 55%
The NCHC was under tremendous pressure to hasten research against COVID-19. Due to this, it created a central network exchange that worked with several public networks. The system was intended to be used for cross-discipline information sharing.
However, the different public networks had their different arrays of monitoring tools and data sets. This complicated research and IT management. The complex IT environment also made incident prevention and response difficult for the NCHC team.
To manage this complexity and maintain utmost uptime for its central network exchange, the NCHC adopted IBM’s Watson AIOps solution. The IBM solution was trained with real-world operations data and network logs. It was then deployed as a central aggregator for the entire IT infrastructure.
IBM’s Watson AIOps solution helped to ingest both structured and unstructured data, visualize all network device statuses, group events, and send alerts to a central interface.
With this, the NCHC reduced its MTTD by 55%. Uptime was tremendously improved as the NCHC team identified outages 25 hours faster.
Challenges of AIOPs
The primary limitation of AIOps lies in the risk of IT teams employing an inadequate strategy when integrating it. This situation arises when there is insufficient preparation and understanding before adopting complex AI and ML models.
Other challenges include:
- Data Quality: The available data for training machine learning models may be of poor quality, containing contradictions and not in a format suitable for effective analysis.
- Data Availability: There may be a lack of quality data due to missing datasets caused by inconsistent data collection practices.
- Interoperability: Integrating AIOps technology with existing IT tools may face challenges if compatibility issues arise.
Difficulties around culture change can also present a challenge when adopting AIOps. For example, employees might worry that automation will replace their roles, leading to job insecurity. Such concerns can lead to resistance and a lack of cooperation in implementing AIOps.
There is also the issue of distrust in AI-powered systems. IBM has identified that the majority of businesses have not made efforts to enhance the trustworthiness of AI. To be precise, 74 percent of businesses have yet to address bias reduction, 68% have not focused on tracking performance variations and model drift, and 61% are unable to provide explanations for AI-powered decisions.
What does the future hold for AIOps?
One of the key advantages of AIOps lies in its ability to proactively identify and resolve potential issues before they escalate into major problems.
Through advanced anomaly detection and predictive analysis, AIOps can anticipate system disruptions, preventing costly downtime and ensuring uninterrupted service availability. This newfound resilience will enable organizations to scale their operations confidently, knowing that their IT infrastructure can handle increased demand and complexity.
Additionally, the seamless integration of AIOps with other emerging technologies, such as DevOps and cloud computing, will create a powerful synergy that amplifies the benefits across the organization. Through continuous monitoring, automated performance tuning, and agile deployment capabilities, AIOps will foster a culture of continuous improvement. This will accelerate product development cycles and reduce time-to-market for new innovations.