The value of AIOps--as noted by Gartner, includes the ability to:
Reduce noise (false alarms)
Determine causality, identifying the probable cause of incidents
Capture anomalies
Detect trends that may result in outages before their impact is felt
Drive the automation of low-risk to medium-risk recurring tasks
Improve user effectiveness and automation using chatbots and virtual support assistants (VSAs)
Triage problems
Gartner groups AIOps solutions into three buckets:
Domain-agnostic AIOps — Vendors going to market with a general-purpose AIOps platform. These products tend to rely mostly on monitoring tools to perform data capture and cater to the broadest use cases.
Domain-centric AIOps — Vendors that have the key components, but with a restricted set of use cases. They essentially do the same thing they did before but now they’re replacing rules, heuristics and fingerprints with math (algorithms). These vendors are focused on one domain (for example, network, endpoint systems or APM). However, there have been some efforts by domain-centric solutions to hybridize these categories and evolve to ingesting data from sources other than their own instrumentation tools and including this data in their analysis.
Do-it-yourself (DIY) — Some open-source projects enable users to assemble their own AIOps platforms by offering tools for data ingest, a big data platform, ML and a visualization layer. End users can mix and match the components from multiple providers.
Illustrative suppliers of domain-agnostic AIOps include firms such as:
Some enterprises actively build AIOps platforms by putting together all the required layers starting with streaming to acquire data (using Prometheus, for example), followed by aggregation (in InfluxData’s InfluxDB, for example) and a visualization tool (such as Grafana or Elastic Kibana), Gartner says.
Some advanced adopters of do-it-yourself AIOps platforms have built solutions that analyze the confidence level of their deployments in order to gauge risk, predict customer churn, and detect and automatically resolve problems before they have business impact. However, these deployments are in the minority, due to the skills needed to support them, maintenance requirements and support, Gartner adds.
Domain-centric AIOps suppliers are further delineated by Gartner into those in the information technology service management (ITSM) space, others in the DevOps space and some in the Network Performance Monitoring and Diagnostics (NPMD) or application performance monitoring (APM) segments of the market.
Use of AI in IT operations has been driven by the adoption of digital transformation and the resultant need to address the following:
Rapid growth in data volumes generated by the IT systems, networks and applications
Increasing data variety with the need to analyze events, metrics, traces (transactions), wire data, network flow data, streaming telemetry data, customer sentiment and more
The increasing velocity at which data is generated, as well as the increasing rate of change within IT architectures and challenges in maintaining observability and improving engagement due to the adoption of cloud-native and ephemeral architectures
The need to intelligently and adaptively automate recurring tasks and predict change success and SLA failure
Use of AIOps platforms to augment IT functions such as event correlation and analysis, anomaly detection, root cause analysis and natural language processing is growing rapidly. However, use of AIOps for functions such as ITSM and DevOps is progressing at a slower pace.
AIOps platform offerings have split into two approaches: domain-agnostic and domain-centric solutions.
Enterprises that adopt AIOps platforms use them as a force multiplier for monitoring tools correlating across application performance monitoring, IT infrastructure monitoring, network performance monitoring and diagnostics tools, and digital experience monitoring.
AIOps platform maturity, IT skills and operations maturity are the chief inhibitors to rapid time to value. Other emerging challenges for advanced deployments include data quality and lack of data science skills.
