What Is AIOps, and Why You Need It Now, More Than Ever
Volatility defines today’s business environment. In order to adapt, your customers, partners, and employees are more dependent than ever on your ability to deliver reliable digital services. Investing in AI-powered capabilities that improve the quality, availability, and performance of these mission-critical services is now all the more important. Technological innovations in AI and Machine Learning (ML) are making such capabilities easier to adopt.
By embracing the role that AI and ML can play in their IT organization, IT leaders have the potential to rapidly extract value from their processes, accelerate the timeframe of critical tasks, and transition to a leaner approach to IT — one focused on innovation, not just keeping up with the day-to-day firefights.
The advantages new AI-powered technologies provide can offer the most benefit when they are allowed to disrupt traditional IT approaches, transforming them for the better. This unification of AI and IT operations is referred to as AIOps.
Gartner defines AIOps as ” AIOps platforms utilize big data, modern machine learning and other advanced analytics technologies to, directly and indirectly, enhance IT operations (monitoring, automation and service desk) functions with proactive, personal and dynamic insight. AIOps platforms enable the concurrent use of multiple data sources, data collection methods, analytical (real-time and deep) technologies, and presentation technologies. “
Forrester defines AIOps as “Software that applies AI/ML or other advanced analytics to business and operations data to make correlations and provide prescriptive and predictive answers in real-time. These insights produce real-time business performance KPIs, allow teams to resolve incidents faster, and help avoid incidents altogether.”
While AI tools and techniques have seen increasing implementation within IT processes, AIOps is a recently developed approach. It builds off of the advances that Agile operations and DevOps brought. In the case of AIOps, removing unneeded steps and gatekeeping comes not merely from organizational process changes, but also from the use of tools that demystify decision-making while automating laborious tasks.
AIOps can have the same holistically positive effect on IT as DevOps and Agile before it. Organizations simply need to identify good candidate use cases and begin transitioning gradually, starting right now. Planting the seeds of change now, during a time when business continuity is more important than ever, will allow your organization-wide transformation.
How AIOps Benefits IT Organizations and the Enterprises They Serve
AIOps has the potential to have a positive impact on the most important measures of IT success. Touted benefits include:
- Reduced incident volume
- Lowering MTTR while also lowering costs
- Achieving better insight into the causes of common IT tickets and business disruptions
- Accelerating decision-making, especially in light of a major incident
- Reducing the time spent on menial tasks, like manual data analysis or remediation of low-level problems
There are many use cases where AIOps can drive tremendous value — potentially delivering payback ROI within weeks or months. E.g., A focus on reducing minor incident volume can not only save organizations up to $2M but also free up staff to focus on preventing major incidents. Other high-value use cases include AI-powered change risk prediction, which can reduce the cost and business disruption related to failed changes
To understand how AIOps can be transformative, let’s look at how AI can improve IT Operations on a process level.
How AIOps Works in the Context of IT Service and Change Management
From a practical perspective, AIOps can be defined as the use of task-specific AI/ML tools to benefit IT organizations and help them achieve their goals.
There are two main categories of AI use in an AIOps organization, broadly speaking:
- Supporting decisions through insights derived from de-siloed data
- Automating simple tasks
Visualizing IT Data to Derive Actionable Insights
The use of AI and analytics allows organizations to aggregate data and analyze it to derive key, actionable insights. Insights derived tend to relate to major organizational pain points.
Common use cases of AI-backed analytics that Numerify customers have implemented include:
- Clustering incidents for more efficient problem diagnosis (root cause) and resolution
- Predicting major incident threats
- Surfacing change-related risks
- Identifying opportunities for service improvements
Visualizing these insights is critical for getting rapid understanding, collective agreement, and stakeholder buy-in. In this way, AI-backed business analytics gives IT the tools it needs to make the status of top priorities more visible, allowing for more clear, data-based, rapid decision making.
Automation in IT
Automation alleviates the effort invested in simple tasks, giving IT the bandwidth to add value. Freeing up resources opens the door to making proactive improvements, expanding the value IT offers customers and enterprises.
Example use cases of automation common among Numerify customers include:
- Automated incident assignment
- Auto-response to certain tickets
- Automation of certain tasks, like identification and approval of low-risk changes
- Identification of major incident or change risk factors
Combining these two major components, AIOps allows for the creation of continual value to organizations. Just like DevOps allows for the deployment of continuous delivery, AIOps can allow for the creation of continuous insights that can lead to proactive improvements, accelerated by the use of automation.
How Is AIOps Transforming IT?
Implementation of AIOps empowers proactive improvements that can allow IT organizations to have a positive impact and reduce common pain points over time, such as recurring incidents or ongoing problems with performance.
In effect, IT organizations can stop “treading water” by fighting the same fires over and over again; instead, they can begin to resolve ongoing problems while preventing future ones from happening.
There are many positive transformative effects that are being observed within organizations that have adopted AIOps.
One strong example is that AIOps offers improved access to insights regarding the entire IT environment — some of which have huge implications for existing processes. In one survey of AIOps users, 45% of respondents said that their IT organization uses AIOps to, “analyze and determine the probable root cause of incidents and to predict future problems.”
Another survey revealed that the most common use cases of AIOps were as follows:
- Intelligent alerting (69% of respondents)
- Root cause analysis (61%)
- Anomaly/threat detection (55%)
- Capacity optimization (54%)
- Incident auto-remediation (53%)
How AI Enhances Human IT Operators
A key component of AIOps is that AI tools work alongside workers, rather than replacing them. This arrangement allows human operators to get more work accomplished in the same amount of time, with less frustration or mystery attached to the status of ongoing operations.
Example 1 — Incident Problem Clustering
AI can help IT teams automatically identify clustered incidents with potentially common root causes.
Before, the same exact issue may have been reported among multiple users and treated like multiple isolated incidents. This required a direct response from multiple low-level IT teams. If a critical app was affected, a higher-level team may have been assigned, further wasting resources.
With AIOps, problems can be identified as a group and addressed all at once. Automated assignment of incidents to a common problem exemplifies the potential for AI to deliver not just insights but efficient fixes, all while reducing the need for physical human touchpoints during the resolution process.
Example 2 — Identifying Change-Related Risks
Automated monitoring and analyses of production changes prevents the need for manual analysis of change risk, as well as any time-intensive scrutiny of possible sources of related incident risk.
Using machine learning, an AIOps solution can automate the assessment of various risk factors and identify the ones with the best risk prediction potential. Highlighting these risk factors can take the form of an expressive KPI, which can quickly reveal the “health score” of a set of proposed changes.
Based on this health score, IT leaders can determine the appropriate remedial actions to respond within a streamlined fashion. Some low-level risks can even be addressed through automated remediation.
Integrating AIOps Within Your Current IT Environment
AIOps can seem like a distant dream for many organizations. It may also seem like a risk given the uncertainty of today’s business environment. The reality is that proven solutions can allow for positive changes today, not years from now, producing real value that allows for leaner, more proactive IT organizations.
The highest priority for teams interested in AIOps to address is the aggregation of data across all silos. Combining metrics from all notable systems of record into actionable KPIs allows for rapid assessment of current organizational health. IT leaders can then identify “low-hanging fruit” to address in order to make quick improvements. Developing a Service Delivery Friction Index, for example, can allow teams to quantify the biggest pain points for IT service customers.
Operationalizing AIOps requires embedding them into your current IT process in intuitive ways. Change risk assessment, for example, can be improved by making prioritized change-related data available for review on a visualization. IT leaders can also establish a risk-scoring system, along with protocols for reacting to specific levels of risk. They can also make decisions on how to prioritize risks when they become so great that dramatic action, like change freezes, is needed. Eventually, automation can be added for low-level risks.
These efforts prepare IT organizations for a faster implementation of AIOps techniques, so they can hit the ground running once their AI-backed systems are available. In this way, AIOps tools enhance the people within your organization. They take the guesswork and manual labor out of identifying lingering/underlying problems just waiting to be solved. They also add intelligence to critical decision-making. These advancements invite the opportunity for further enhancement with automation.
In this way, AIOps helps IT workers accomplish more today with tools for efficiency at their disposal. Now that organizations are being tasked to do more with fewer resources and coordinate across great distances, the advantages AIOps brings empowers them to offer more value to the business services that matter most.
Learn more from Forrester and Numerify about how AIOps can advance your organization by improving IT Change and Service Management in our on-demand webinar: “How AIOps helps IT Change and Service Management be more reliable and nimble“
The New Role of Change Advisory Boards in an Automated World
In a constantly changing world, processes that worked well a short time ago no longer…
8 IT Operations Metrics That Offer Vital Feedback to Developers
DevOps is supposed to represent a seamless partnership between development and operations, accelerating and simplifying…
AIOps and the Capabilities it Should Deliver
Artificial intelligence (AI) is transforming the world around us in countless ways. However, in an…