Artificial intelligence (AI) is transforming the world around us in countless ways. However, in an enterprise setting modern AI solutions don’t yet replace existing workflows as much as intelligently enhance them. Using AI, teams are able to augment their decisions with data-driven insights and automation of simple, repetitive tasks. This allows them to do more work faster and better than ever before.
This scenario exemplifies the goal of a technology industry term called “AIOps.” With this in mind, a formal definition of AIOps could be: “The use of artificial intelligence, machine learning, and similar technologies to enhance operations, especially in the context of businesses that create technology products.”
What Are the Benefits of AIOps?
The benefits of an AIOps approach can include:
- Greater velocity for product changes and value creation
- Less resource consumption and greater efficiency in incident resolution
- Process improvement opportunities, as revealed through analytics
- Cost savings, both through reduced overhead and additional created value
- Fewer incidents, and faster resolution to ongoing problems
- Greater customer and business user satisfaction
But with all these promises, business leaders may be left wondering: how can an IT organization reliably achieve the benefits promised by AIOps? One of the most vital approaches is to customize your AIOps strategy, investments, and implementation to your individual needs and environment. At the same time, there are a number of elements every AIOps solution should have in order to enhance IT operations’ productivity and augment the capabilities of existing IT teams.
Essential Components to an AIOps Solution
AI and Machine Learning (ML) Enabled Data Analysis across all relevant data
AI and ML models are only as good as the data sets used to build and train them. Also, like humans, AI/ML models act on information given to them. Because of this dependence upon data, the most important component of any AIOps solution is an analytics platform capable of bringing in data from all relevant organizational silos.
Many current IT Ops solutions offer their own respective reporting functions, but this is not analytics. In order to achieve accurate and truly meaningful insights, IT organizations have to be able to let all the relevant data talk to each other. Then, while looking at the data pool in aggregate, AI/ML models can apply analytics techniques to generate meaningful insights.
As an example, during a service disruption, an IT operations team may be looking at diagnostics for an individual application. These diagnostics will reveal, among other things, the stability of the current production environment along with the availability of resources currently assigned. The system may even reveal the status of certain infrastructural components vital to the platform’s functioning as a whole.
What the diagnostics likely won’t reveal is the root cause behind the disruption. That’s because the diagnostics system can only observe things that happen within its respective silo. It may also lack the analytic capability to connect the dots between various observed production factors.
To reveal deeper insights, IT teams can examine all data relevant to operations: not just applications performance monitoring but also configuration management data, data related to the enterprise infrastructure, and IT service management (ITSM) data tracking current problem tickets. With this information available, IT can discover contextually how a backlog of IT service issues has led to the outage, or that the system used to promote code was contributing instability to the production environment.
An AIOps solution can further act on this information through enhanced analytics functionalities, but it needs access to aggregated data as a baseline requirement. Some AIOps engines automatically reveal problem root causes, while others can actually predict disruptive risks posed by changes before they are ever promoted. But before these capabilities can be realized, the solution must be implemented atop a system of data analytics that draws from every relevant system of record in the organization.
Precise Functional Control to continuously tune models
Algorithms don’t always do what we want them to! This can be a fact we simply have to deal with when, say, rolling our eyes at suggested movies on a streaming platform. But when AI/ML models aren’t behaving like they should for IT operations, they should have the ability to recalibrate or retrain the models to achieve the desired functionality.
Having experience with data science and machine learning can add several more degrees of control over these functions, but you shouldn’t have to have a PhD to fix an automated feature that’s not performing as expected. Thankfully, some AIOps solutions give users the ability to review results from several models and promote the ones that offer the best performance. Models should also be continually retrained and improved upon in light of the freshest data, ensuring they’re always aligned to the context of the operations team’s current goals and environment.
Not having the ability to temper your results could create organizational chaos if AI/ML models fail to perform as expected. In some cases, the models can create disruptions and slow processes when originally they were intended to make the function they control more agile and reliable. Because of this risk, IT organizations should be sure that any AIOps solution they invest in gives them a high level of control and customization when it comes to AI/ML model performance.
Self-Service Analytics to drive a proactive culture of improvement
Many AIOps functionalities aim to give IT operations teams access to critical insights. They may be made aware of emerging threats based on trend analysis, or they may be able to identify recurring problems that are ripe for a permanent fix using custom KPIs.
Insights like these are much more effective when they can be accessed by everyone. One of the biggest barriers to proactive problem solving and innovation in many IT operations environments is that deriving insights and answering questions requires laborious manual analysis. An AIOps platform can circumvent this hurdle by offering self service business analytics reporting and the ability to generate customized visualizations on-the-fly.
One Numerify customer saw measurable gains when they made the analytics platform available through a single browser-based domain. Self-service reporting features allowed teams to monitor their own performance metrics and respond accordingly when certain performance areas fell short. The client also implemented the use of actual names rather than generic roles, allowing teams to rapidly view the most important metrics to their particular domain. Using names also drove behavioral changes because, as the client put it, “people don’t like seeing their names on a bad list.”
Dashboards should also offer ways to interact with the data to answer questions through a few clicks rather than the cumbersome generation of new reports. Filters, sorting, drill down, slices, dices, and the ability to assign metadata on the fly all allow for intuitive exploration that can reveal solutions to problems that have been nagging the organization and dogging productivity.
Remember, AIOps Can Be Implemented Gradually, with Increasing Levels of Automation as You Advance
One aspect of AIOps strategy that many executives seem to forget is that you don’t have to make huge changes in order to achieve the desired goals of transformation. Start with a data-focused culture, and then expand with AI/ML engines capable of resolving common hurdles to agility and reliability.
IT Ops teams can leverage AI to develop a system of scoring production risks, for example. Then, they can modify their processes to rapidly act on this data when weighing which changes to promote and which changes to first test and possibly remediate. This reduces the need for a change advisory board (CAB) meeting for every major change, and it also shortens the level of analysis needed to decide upon changes. Eventually, IT leaders can add RPA to accelerate workflows, and they can automate remediation of low-level change risks using ML-based solutions that fix common sources of instability.
When IT leaders can implement AI-back analytics solutions into their organization gradually in this way, it becomes a source of transformative culture change — not just a process change.
Prioritizing these basic elements of an AIOps solution ensures that IT operations teams can get the capabilities they need to achieve the transformation they deserve. Procuring a solution that can’t provide these basic elements, on the other hand, limits the ability for IT operations to fully benefit from AIOps investments.
Learn more about the transformative potential of AIOps in our recent webinar: “How AIOps helps IT Change and Service Management be more reliable and nimble“