"AIOps combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination."
Through algorithmic analysis of IT data and Observability telemetry, AIOps helps IT Ops, DevOps, and SRE teams work smarter and faster, so they can detect digital-service issues earlier and resolve them quickly, before business operations and customers are impacted.
With AIOps, Ops teams are able to tame the immense complexity and quantity of data generated by their modern IT environments, and thus prevent outages, maintain uptime and attain continuous service assurance.
With IT at the heart of digital transformation efforts, AIOps lets organizations operate at the speed that modern business requires, and deliver a stellar user experience.
An AI Platform for Today — and the Future
This isn't your grandparents data center any more, you can’t manage today’s dynamic, constantly changing IT environments with yesterday’s tools. Microservices and containerization make it imperative to modernize.
The evolution of IT infrastructures — moving from static and predictable physical systems to software-defined resources that change and reconfigure on the fly — demands equally dynamic technology and processes for their management.
The complexity of managing the operations of modern IT environments exists at three levels:
At the core is the complexity of systems that are modular, distributed and dynamic, and whose components are ephemeral.
TThe second layer is the data these systems generate about their internal operations — logs, metrics, traces, event records and more. This data is complex because of its high volume, specificity, variety, redundancy.
The third outer layer is the complexity of the tools used to monitor and manage the data, and the systems. There are more and more tools, with increasingly narrow functionality, that don’t always interoperate, and thus create operational and data silos.
As IT infrastructures evolve, old rules-based systems fall short, because they rely on a pre-determined, static representation of a mostly homogeneous, self-contained IT environment.
AIOps uses machine learning and data science to give IT operations teams a real-time understanding of any issues — including new, unforeseen problems for which rules haven’t been crafted yet — that affect the availability and performance of digital services.
How Does AIOps Work?
Not all AIOps tools are created equal. To get the most value, an organization should deploy it as an independent platform that ingests data from all IT monitoring sources, and acts as a central system of engagement.
Such a platform must be powered by five types of algorithms that fully automate and streamline five key dimensions of IT operations monitoring:
Taking the massive amount of highly redundant and noisy IT data generated by a modern IT environment and selecting the data elements that indicate there’s a problem, which often means filtering out up to 99% of this data.
Correlating and finding relationships between the selected, meaningful data elements, and grouping them, for further advanced analytics.
Also called root cause analysis, identifying root causes of problems and recurring issues, so that you can take action on what has been discovered.
Notifying appropriate operators and teams, and facilitating collaboration among them, in particular when individuals are geographically dispersed, as well as preserving data on incidents that can accelerate future diagnosis of similar problems.
Automating response and remediation as much as possible, to make solutions more precise and quick.
AIOps is the Nucleus of Digital Operations
In a real world setting, the AIOps platform ingests heterogeneous data from many different sources about all components of the IT environment — networks, applications, infrastructure, cloud instances, storage and more.
- Using algorithms, it removes noise and duplication, and selects only the truly relevant data. This algorithmic filtering massively reduces the number of alerts Ops teams must deal with, and eliminates duplication of work caused by redundant tickets routed to different teams.
- It then groups and correlates this relevant information using various criteria, like text, time and topology. Next, it discovers patterns in the data, and infers which data items signify causes, and which signify events.
- The platform communicates the result of that analysis to a virtual collaborative environment where everyone involved in solving an incident has access to all the relevant data. These virtual teams can be assembled on the fly, enabling different specialists to “swarm” around an issue that spans technological or organizational boundaries.
They can then quickly decide upon fixes, and choose automated responses for fast and precise resolution of the incident. For example, existing ticketing and incident management systems can take advantage of AIOps capabilities, integrating directly into existing processes. AIOps also improves automation of incident response, by enabling workflows to be triggered with or without human intervention.
The AIOps platform stores the causes and solutions for every fixed incident, and uses that knowledge to help Ops teams diagnose causes and prescribe solutions for future issues.
Moogsoft is the AIOps Platform
For Collaborative, Agile & Proactive Incident Resolution Workflow
How Does AI Help Human Operators?
The pace and volume of change demands automation of routine tasks, so that Ops pros can focus on solving critical , unpredictable, and high-value issues, instead of getting bogged down by the overwhelming amount of mostly irrelevant IT data. AIOps combines automation of tactical activities with strategic oversight by expert users, instead of wasting the time and expertise of skilled Ops pros on “keeping the lights on”.
The “AI” in AIOps does not mean that human operators will be replaced by automated systems. Instead, humans and the AIOps platform operate together, with the AI and ML algorithms augmenting human capabilities and enabling Ops pros to focus on what is meaningful.
Equally important, now that remote work is the new normal, AIOps has emerged as a lifeline for Ops pros who now find themselves having to maintain the uptime and stability of critical digital services while teleworking.
By facilitating remote collaboration, streamlining incident management, and accelerating detection and resolution, AIOps has become the foundation for collaborative operations environment.
DevOps and SRE teams, who have long embraced automation, see AIOps as a highly robust and change tolerant extension of the feedback loop.
How to Integrate AIOps with your Current Tools
An AIOps platform integrates with existing tools and processes, bringing together information, insights, and capabilities that were previously locked in disconnected islands. IT teams use multiple monitoring tools for different purposes. Each one is valuable to a specific team or function, but access to each tool and to its insights and data is limited. Instead of engaging in tool rationalization initiatives to shoehorn individual needs into one-size-fits-all solutions, AIOps ties them all together and delivers seamless shared visibility across all tools, teams, and domains.
In the same way, AIOps improves and enables ITSM by ensuring that only real, actionable incidents are created, and by avoiding duplication. There is no need to discard the experience embedded in each organization’s ITIL-based processes.
Finally, AIOps brings automation into the fold as well, integrating orchestration and run books, and making them directly available to operators as partial or full automation. IT organizations have typically developed large libraries of automated solutions over the years, but need to ensure that they are triggered only by the correct conditions. AIOps ensures that this is the case, minimizing risk and maximizing value of existing investments in automation.
What are the Benefits of AIOps?
The main benefit of adopting AIOps is that it gives Ops teams the speed and agility they need to ensure the uptime of critical services and the delivery of an optimal digital customer experience. It’s been hard for Ops pros to accomplish this, due to brittle rules-based processes, the creation of silos due to specialization, and above all, too much repetitive manual activity. Here are more details about the benefits of AIOps:
- AIOps removes noise and distractions enabling busy IT specialists to focus on what’s important and not be distracted by irrelevant alerts. This speeds up the detection and resolution of service-impacting issues, and prevents outages that hurt sales and the customer experience.
- By correlating information across multiple data sources AIOps eliminates silos and provides a holistic, contextualized vision across the entire IT environment – infrastructure, network, applications, storage — on premises and in the cloud.
- By facilitating frictionless, cross-team collaboration between different specialists and service owners, AIOps accelerates diagnosis and resolution times, minimizing disruption to end users.
- Advanced machine learning captures useful information in the background and makes it available in context to further improve the handling of future incidents.
- Through knowledge recycling and root cause identification, the workflows for solving recurring incidents can be automated, moving Ops teams closer towards a ticketless and self-healing environment.
What You Need to Know About AI & Machine Learning
The AI in AIOps is not a general intelligence. Instead, a set of specialized algorithms are narrowly focused on specific tasks. Different algorithms can pick out significant alerts from a noisy event stream, identify correlations between alerts from different sources, assemble the correct team of human specialists to diagnose and resolve a situation, propose probable root causes and possible solutions based on past experiences, and learn from feedback in order to improve continuously over time.
Clustering and correlation is the most complex and crucial step, requiring multiple different approaches. A combination of historical pattern-matching and real-time identification helps IT Ops teams to identify both recurring and net-new issues. Raw monitoring events may be enriched by reference to an external data source, where available; this enrichment helps to deliver better predictive correlation, as well as service impact information.
AIOps Market Momentum
Adoption of AIOps is growing strongly worldwide, as global enterprises use it successfully to attain continuous service assurance. Here’s a sampling of research findings about the momentum of AIOps.
- According to research from Digital Enterprise Journal, there has been an 83% increase in the number of organizations deploying or looking to deploy AIOps capabilities since 2018.
- MarketsandMarkets estimates the global AIOps platform market size to grow from $2.55 billion in 2018 to $11.02 billion by 2023, at a Compound Annual Growth Rate (CAGR) of 34.0% during the forecast period.
- Almost half of all DevOps pros who responded to a 451 Research survey done in 2020 said they currently use AIOps.
- Companies surveyed by Enterprise Management Associates ranked AIOps as the most successful IT analytics investment, with 81% indicating that the value they get from AIOps exceeds its cost, including 42% who said it does so “dramatically.”
- Enterprise Management Associates also found that AIOps is the IT analytics option that’s preferred by larger enterprises, and that supports a broader range of use cases. It ranked at the top for having broader support for third-party toolset integrations, and stronger support for integrated automation, including AI bots.
- In its “2019 Strategic Roadmap for IT Operations Monitoring,” Gartner includes this recommendation for leaders focused on infrastructure, operations and cloud management: “Augment root cause analysis and IT Ops staff performance by using AIOps platforms to uncover insights from broad IT Ops datasets.”
- In its “Market Guide for AIOps Platforms,” Gartner forecasts that “by 2023, 40% of DevOps teams will augment application and infrastructure monitoring tools with AIOps platform capabilities” and also states that:
- "There is no future of IT operations that does not include AIOps"
- “AIOps platforms enhance I&O leaders’ decision making by contextualizing large volumes of varied and volatile data. I&O leaders should use AIOps platforms for refining performance analysis across the application life cycle, as well as for augmenting IT service management and automation.”
- “Enterprises that adopt AIOps platforms use them as a force multiplier for monitoring tools correlating across application performance monitoring (APM), IT infrastructure monitoring (ITIM), network performance monitoring and diagnostics tools, and digital experience monitoring.”
Who Is Using AIOps and for What?
AIOps is being used globally by organizations of all types, industries and sizes, and for a variety of scenarios.
Enterprises with Large, Complex Environments
AIOps adopters include companies with extensive IT environments, and spanning multiple technology types, which are facing issues of complexity and scale. When those are compounded by a business model that is heavily dependent on IT, AIOps can make a huge difference to the success of the company. Though these organizations may be in many different industries, they share a common scale, and a rapid and accelerating rate of change, as the need for business agility in turn creates more and more demand for IT agility.
AIOps is also being embraced by small and medium size enterprises (SMEs), particularly those that were born in the cloud, and that need to develop and release software continuously and quickly. AIOps allows the SRE teams in these SMEs to continually sharpen their digital services, while preventing glitches, malfunctions and outages.
DevOps Teams in Organizations of All Sizes
Companies with a DevOps model can struggle to maintain alignment between the different roles involved. Direct integration of Dev and Ops systems into an overall AIOps model smooths away much of the potential friction. AIOps gives Dev teams a better understanding of the state of the environment, and grants Ops teams full visibility of when and how developers are making changes and deployments into production. This holistic view ensures that CI/CD cycles run uninterrupted, and that apps are created and delivered quickly and seamlessly.
In addition, DevOps pipelines generate massive amounts of data. To maintain the stability and speed of application delivery, DevOps leaders must analyze it quickly and continuously. While DevOps teams have automated most of their functions, many still have a manual decision-making process, which creates bottlenecks and leads to ill-informed actions. AIOps, with its ability to analyze data and recommend actions, is the key to make precise data-driven decisions and automate actions for rapid application delivery.
As Gartner states in its “Augment Decision Making in DevOps Using AI Techniques” report: “AI-driven approaches leverage the continuous data streams to enable pattern recognition, anomaly detection, and prediction and causality.” Gartner forecasts that, “by 2022, DevOps teams that leverage AIOps platforms to deploy, monitor and support applications will increase delivery cadence by 20%.”
Organizations with Hybrid Cloud and On Prem Environments
Moving workloads to a public cloud platform has well-known benefits, but there are also good reasons to keep certain applications and infrastructure on premises. For this reason, many organizations find themselves with hybrid environments, and this brings its own set of IT operations challenges. By delivering a holistic, comprehensive view across all infrastructure types, and helping operators to understand relationships that change too quickly to be documented, AIOps helps Ops teams maintain control over these environments and provide service assurance.
Businesses Undergoing Digital Transformation
Digital transformation is the digitization of business processes in order to make the organization more efficient, agile and competitive. At the heart of digital transformation initiatives is IT, which needs to operate at the speed that the business requires if it is not to become a bottleneck, preventing achievement of the wider goals. By automating IT operations and preventing glitches that disrupt these digitized processes, AIOps helps IT deliver the level of technology support that successful digital transformation projects require.
Where Does AIOps Fit into the Modern IT Environment?
When looking at AIOps for the first time, it is not immediately obvious how it fits into existing tool categories. This is because AIOps does not replace existing monitoring, log management, service desk, or orchestration tools. Instead, it sits at the intersection of these domains, integrating information across all of them and providing useful output to ensure a synchronized picture is available.
These tools are valuable in their own right, but it's hard to access the right piece of information at the right time. Hard-coded integration logic struggles to keep pace with the rate of change of modern IT environments. AIOps provides a much more flexible approach to assembling all of these different partial views into a single comprehensive understanding of what is actually important for IT Ops teams to know about.
As such, an AIOps platform plays the role of organizing and integrating what an organization’s domain-specific IT monitoring and management tools do, intelligently integrating the stack’s functionalities. The AIOps platform acts as the brain that brings together these tools, and becomes a coordinating, central layer.
The Economic Value of AIOps
When evaluating the financial benefits of an AIOps platform, it’s essential to look beyond its ability to reduce costs. Don’t ignore the benefits side of the equation — both direct benefits and the technology’s future impact on enhancing flexibility and reducing risk.
AIOps’ impact can be often directly traced to business benefits. For example, AIOps helps prevent disruptions of critical digital services, and accelerates detection and resolution. In that way, AIOps optimizes revenue generation, because when apps malfunction, sales are lost.
It also plays a direct part in customer experience, satisfaction and retention, as well as in brand reputation protection, all of which are directly related to business performance and profitability.
Let’s look at a real world example.
A large financial services institution cut its MTTR by a whopping 85%, and slashed its Level 1/2 tickets by 75%, its Level 3 tickets by 15%, and its Level 4 tickets by 50%. The financial benefits to the business beyond simple cost reduction: Tens of millions of dollars.
This was achieved via a multi-pronged strategy encompassing several key use cases, including:
- A dramatic improvement in the clustering of alerts around incidents. The company went from a limited, inefficient process, to an AIOps-driven ingestion and correlation process that consolidated alerts into contextually-rich incidents and a massive ticket reduction.
- An integration with the ITSM / CMDB system. This drastically simplified and accelerated ticketing, leading to faster, more effective routing, prioritizing, handling and resolution of incidents.
- Automated knowledge capture and recycling. With the knowledge capture and recycling process totally automated, operators are notified of resolved past incidents that are similar to current ones, and provided all resolution documentation, accelerating MTTR.