Share the Wealth: AI Operations in ServiceNow

Updated: Apr 22

Speakers: Mike Desmond


In this week’s Share the Wealth video, Mike Desmond of GlideFast gives an overview and demonstration of AI Operations in ServiceNow.


What is AIOps?

AIOps refers to Artificial Intelligence for IT Operations. AIOps is used for applying machine learning artificial intelligence by applying rules and intelligence to big data sets to derive root cause analysis and attempt to resolve certain issues. Although AIOps is leveraged across the platform, the purpose of this overview is to apply AIOps terminology specifically to ITOM events, correlation, metrics, mediation, and how this can help clients resolve issues.


What is the Problem?

In the business to service to operations process, there is the need to close experience gaps across the service life cycle. This begins with a request from the end user and action needing to be taken by the operations team. The service lifecycle can be outlined as follows: request, prioritize, plan and execute, build, deliver, run, and improve. The purpose of AIOps is to mitigate the gap so that there is transparency between what that piece of hardware is and what that service to the end user is in order to simplify the process and drive value.


Comprehensive AIOps Solution

AIOps Outcomes using ITOM:

  • Historical data

  • Service context

  • Platform analytics

  • Real time analytics

  • Machine learning

  • Learnings from human behavior

  • NLP


Your Strategic Advantage When Service Meets Operations

The Service Management (Service Oriented) model includes CSM/CMDB, specifically problem, incident, service catalog, and change. Operations Management (Infrastructure-Oriented) model includes servers, applications, storage, virtualization, cloud, and network.


The trick is taking predictive analytics and applying AIOps. ServiceNow provides the ability and visibility to act in a reactive state. If an event or alert comes in, you can remediate it almost immediately or, using predictive analytics, you can be proactive with alerts (e.g. there is an X% chance in the next hour that X instance will go down) based on patterns of data collected over the past few weeks from an event management perspective.


End-to-end Solution

For business services deployed on-premises or cloud:

  • Visibility across operations estate and all software.

  • Health of Application services, with AIOps.

  • Optimization of cloud and software spend.


Make Operational Decisions in the Context of your Business Service

Outcomes:

  • Get a continuously-optimized view of services.

  • Detect and remediate service drift (e.g. a known change or unplanned change).

  • Improve root cause analysis.


Deliver High-Performance Business Services

Outcomes:

  • Identify early warning of potential service outages

  • Improve end-user satisfaction with fewer service disruptions.

  • Identify root cause of issues quickly. Reduce event noise by 99%.

  • Get a better operations experience driven by service-level intelligence.


Prioritize by Business Impact

Outcomes:

  • Increase insight to the status of business services through a single dashboard.

  • Drive service uptime.

  • Diagnose and pinpoint issues.


AIOps Outcomes: Examples

  • Materially reduced MTTR

  • Fix widespread performance issues

  • Anomaly detection

  • Automated remediation

  • NLP driven smart searches

  • Reduced noise in network dependencies

Interested in working with experts like Mike?


Reach out to us here. We would love to learn more about your ServiceNow challenges and how we can help your organization build better solutions.

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