AI and AIOps – a perspective for IT Services Industry

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AI and AIOps – a perspective for IT Services Industry

This write-up is to discuss about AIOps from the perspective of IT Services Industry and how possibly one need to shift towards bringing the benefits/efficiencies of AIOps at the Service Delivery / Service Consumption level.

There are definitive steps taken currently in the AIOps community to shift left from ITOPs to DevOps.

One can see that AIOps practices are making their headway into even pre-production activities with definite focus on predictive remedies, in order to build and deploy robust services.

This blog is looking at how AIOps is helping in the area of ITSM/DevOps areas and also brainstorming on how one could start integrating these practices / solutions into Service Delivery / Service Consumption areas.

Ever since Gartner coined the term AIOps, way back in 2016-17, the market has grown significantly and is expected to grow very fast in the coming years (one estimates that it will grow in the range of 20%-25% CAGR and may cross $40bn in next few years).

This phenomenal growth is attributed to

        Larger Digital transformation across IT Estates

        Varied and disparate platform / sources where the estates reside including cloud agnostic solutions

        Ever growing data across the estate (Engagement, Observational data)

        Larger, faster releases and deployments

The overall goal is to capture all data generated across the IT Estate, store, analyse, provide insights, and provide fixes thru appropriate automation. In this two aspects that play critical role are Big Data and Analytics thru Machine learning.

The following diagram is representative of how AIOps is playing role at various levels.

AIOps – In Operations

·        AIOps solutions are very strong in the area of ITOM, ITSM

·        Typical Solutions that are available currently

o   Domain-centric (domains like Application Monitoring, Log Monitoring, Network monitoring) (Examples of some products/Product companies Dynatrace, Datadog, ScienceLogic, S1 Platform, Zenoss, IPsoft etc.)

o   Domain-agnostic solutions available (works across disparate services and working across domains in IT environment) (Examples of some products/Product companies Big Panda, ServiceNow, BMC,  Elasticsearch, IBM Cloud Pak, CISCO App Dynamics,  Moogsoft, DataDog, Zenoss, Splunk etc.)

·    Personas: These are largely IT Operations personas, Service Delivery Personas – such as System Engineer, Site Reliability Engineers, Operations Engineer, Security Professionals, and Service Desk etc.

·    Process involves – Predict Service Failures, determine appropriate root causes and propose remediation and in some cases fix the issues before they affect the services.

·  Typical features involve Predictive Analytics, Predictive maintenance, Solution Recommendations, Creating knowledge articles, Intelligent Autoresponders, Persona based Analytics etc.

·    Some benefits are :  Proactively identify potential issues before they occur, remove noise from actual alerts that need attention, Improved IT Productivity, Improved  Utilization, Better visibility across IT estate, Optimize the spend across the estate, Better CSAT, Better relation with the business (from cost center to partner)

AIOps – in DevOps

·        As stated earlier, there is a trend to shift left – bringing AIOps practices / solutions to pre-production activities while Applications and Services Solutions are built, tested and deployed. This shift was imminent, given that Dev works very closely with OPS and has large impact in what gets designed, developed and deployed.

·        Persona – Developers, DevOps engineers, SRE Engineers

·    Some features includes – data ingestion for gaining insights while code is getting  developed and tested, proactively identifying anomalies in CI/CD pipelines, auto-remediation for such workflows (such as deployment of Pods and containers in multi cloud environment) using SLA for faster deployments and so on

·        Examples of Product/Product companies – Harness, Dynatrace, OverOps etc

·      Some Benefits are – better control on CI/CD stack, efficient use of pre-production estates, better resilient software solutions, robust design , Better productivity of DevOps community, and finally better integration with Ops

AI in Service Delivery/Consumption

·      Question is how to bring AI (and AIOps practice & discipline) into Service Delivery/Service Consumption areas and integrate the practices across Service Applications and underlying IT infrastructure to provide a complete integrated experience to the end user company / LOB owner

·        Few examples of  Service Delivery/Service Consumption could be

o   Selecting & Onboarding new resources onto Organization Platform

o   Talent Hunt with appropriate competencies

o   Allocation of competent human resource to a program

o   Allocation and management of Workplace / facilities to increase occupancy

o   Developing Market Strategy based on market / customer interests and Sales inputs

o   How to build a predictive Customer Service

o   Design and apply predictive maintenance needs in manufacturing setup

o   Detection of suspicious behaviour and persistent vulnerabilities that result in security threats across the ecosystems (& this is not restricted to IT Systems security threats but extended to threats to IPR, Knowledge Assets etc.)

·        Currently individual solutions do exist in the form of AI/ML solutions or robotic processes (including bots) in many areas including Customer Service, Healthcare, Finance, Stocks, Auto Industry, even fitness applications areas

·        In many case these, however, are not  integrated solutions or products within a given service delivery platform

·        While some of them can be integrated including digital workplace solutions (for example ServiceNow IT Service Management and ServiceNow HR Service Delivery), it will be imminent to bring the power, predictability and resilience of AIOps into Service Delivery functions too. This tight integration and convergence will help provide flawless, efficient Services to the end users.

·        It is evident that business has to spend time, money in meticulously planning for such tight end to end integrations in order to yield maximum benefit of automation at both the ends

·        Also, probably it is time now, to bring a certain standardisation in the mechanisms of doing such integrations

·        Some Examples of Product Vendors/Products :

o   Integrated HR Service Management : ServiceNow HR Service Delivery, DoveTail Employee Engagement  Suite, Oracle HR Help Cloud Service, SAP SuccessFactors Employee Central Service Center,

o   Workspace Management : ServiceNow Workplace Service Delivery,



·        Gartner Market Guide for AIOps Platforms

·        ServiceNow Workplace Service Delivery, HR Service Delivery

·        Mordor Intelligence – Market Snapshot

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