How AIOps Impacts Business Performance 

IT Ops has rapidly and exponentially evolved. Today, the amount of data involved in application infrastructures is overwhelming, yet some organizations still process and report on it manually, using static reports designed decades ago. The need for constant availability and predictive insights has led to the emergence of AIOps.

According to Gartner, approximately 50% of enterprises will actively use AIOps technologies with application performance monitoring (APM) to provide insight into business/mission-critical applications and IT operations. Today, less than 10% of enterprises do so.

Why the increase? In part, because while application performance monitoring can help diagnose application problems, it typically offers little insight into how to use that information to benefit the business. This is where AIOps plays an important role.

IT teams agree that AIOps solutions drive productivity gains by handling mundane and tedious tasks, reducing friction for real-time and cross-domain incident collaboration, and ensuring speed and consistency of digital service delivery. In terms of business benefits, AIOps tools can be particularly effective for Intelligent Alerting, Root Cause Analysis and Threat Detection.

• Intelligent Alerting: Deliver contextual alert notifications that let DevOps teams understand event history, streamline incident collaboration, and meet service-level requirements for problem resolution.
• Root Cause Analysis: Ensure better service uptime and reliability, with rapid problem diagnosis that combines impact visibility and service context to determine the probable cause and effect for operational issues.
• Threat Detection: Leverage machine-learning algorithms to quickly identify outliers through pattern recognition, enabling IT teams to extract signals from noise and identify events that deviate from regular system behavior.

When it comes to the “build vs. buy” decision, Gartner data shows 55% of IT decision-makers choose to implement commercially available solutions from a recognized AIOps tool vendor. Evaluating and choosing the right vendor often comes down to ranking four features that clients say are critical: inference models, incident visualization, data-agnostic ingestion, and an integrations ecosystem.

Inference Models

Inference models are no surprise, given that IT teams are looking at vendors to provide pre-built optimization techniques for alert management. AIOps can deliver immediate value, with analysis of vast IT event datasets for historical and real-time incident analysis.

Data-Agnostic Ingestion

Given the volume, variety and velocity of data generated by today’s IT services, AIOps solutions ensure prompt incident response by consolidating, normalizing and presenting the right operational insights for informed action.

Incident Visualization

Given the complexity and interdependencies of modern digital services, AIOps tools that can clearly highlight alert insights, relevant performance metrics, and event timelines in a single place are a huge boon for incident responders.

Integrations Ecosystem

AIOps tools can manage the entire incident lifecycle by integrating relevant events, metrics and logs from different IT operations tools. IT teams can access the right performance insights across multiple data sources without resorting to swivel-chair monitoring.

The operational benefits of using AIOps come from the productivity gains of eliminating low-value, repetitive tasks across the incident lifecycle, rapid issue remediation with faster root-cause analysis; better infrastructure performance through reduced incident and ticket volumes; and significantly reducing the human time spent on first response, alert prioritization and root-cause analysis.

Each of these has clear business benefits, yet some enterprises are still slow to adopt AIOps tools. Why? IT leaders cite data accuracy, skills gaps, Implementation Cycles for AIOps and loss of control as their most significant sources of apprehension while implementing AIOps tools.

• Data Accuracy. Enterprises will need more time to build trust in the relevance and reliability of AIOps recommendations. IT teams will combine data-driven insights with human judgment to draw the right conclusions for performance optimization.
• Skills Gaps. IT teams will need to gain expertise in machine-learning techniques and combine them with incident analysis skills to support AIOps deployments.
• Implementation Cycles for AIOps. AIOps implementation timelines are a function of organizational maturity, staff exposure to machine learning, and the right investments in big data analytics for handling large-scale event volumes. A full 65% of organizations that have implemented AIOps tools have taken more than three months to build accurate models, train machine intelligence with the right data, and invest in staff training to deploy an enterprise-ready AIOps solution.
• Loss of Control. Incident management teams are afraid to cede complete control to self-driving autonomous systems that deliver actionable insights for problem diagnosis, troubleshooting and recovery.

Clearly, AIOps can impact business performance, as indicated by the dramatic adoption predicted by Gartner, with the leading use cases being intelligent alert notifications, root cause analysis, and anomaly detection. Yet some enterprises still have concerns as they delve into this new territory. While enterprises will bet on AIOps tools for predictive issue analysis, vendors should offer solutions to address growing concerns around data accuracy, loss of control and long implementation cycles. On the client/enterprise side, management should ensure they have the right mix of talent to speed adoption, with expertise in data science, machine learning, and industry awareness.

We foresee five additional steps to help companies launch an AIOps initiative:

1. Choose initial test cases wisely. Transformation initiatives should start with pilot and industrialization.
2. Experiment freely. Although AIOps platforms are often products of substantial cost and complexity, a large variety of machine-learning software is available to enable evolution.
3. Look beyond IT. Data management is a huge component of AIOps, and teams are often already skilled.
4. Standardize where possible, modernize where practical. Prepare an environment to support an eventual AIOps implementation by adopting a consistent automation architecture, infrastructure as code, and immutable infrastructure patterns.
5. Visualize full adoption. There are many variables and available products will evolve, as will the AIOps “state of the art,” and the infrastructure and applications for which a business is responsible.

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