Transform your service experience with Predictive AIOps


By Gaurav Uniyal, Associate Vice President and Service Management Head, Americas and APAC, Infosys 

Service Experience Transformation is top priority for CXOs to accelerate complex business initiatives, drive user adoption and enable IT stability and productivity improvement programs. With increasing complexity of business use cases, today’s modern IT setup has evolved into a mesh of agile processes, hybrid technologies and data science frameworks to support business priorities.     

To enable IT Operations modernization, a relatively new but fast expanding discipline Predictive AIOps is being pursued by technology leaders, site reliability engineering teams, IT operations teams, as well as business leaders. 

Predictive AIOps combines AI and related technologies such as NLP (Natural Language processing), machine learning, along with data science frameworks and modern processes to provide visibility into historic performance data, provide predictive insights, make proactive recommendations, and support self-healing through automation and AI techniques.  By doing so, AIOps significantly reduces operational downtime, minimizes business impact, and prevents future disruptions.

How Predictive AIOps works  

Today business services require 100% uptime and extreme performance as any degradation has a direct impact on business outcomes and customer retention. 

Examples of such services could be an app to book a taxi, or to order food or an app to check emails– these are very basic and common examples of services that are required by consumers anywhere, anytime and even the smallest downtime, outage or even service quality degradation can lead to widespread business and commercial implications. 

This is where Predictive AIOps helps in minimizing services disruption by proactively identifying potential failures based on historical performance data, guide technology teams through proactive insights to act ahead of failures, and drive remediation through automated playbooks to rectify the cause. 

Additionally, Predictive AIOps provide significant benefits to IT through early warning signals, consolidation of alerts and events from multiple sources to provide meaningful insights and helps in reducing Mean-Time-to-Repair (MTTR). This helps in improving IT operations with optimized cost of service delivery- which is of paramount importance to CXO and cost center owners. 

Following are the key tenets of a comprehensive Predictive AIOps solution.

  • Data Collection and Aggregation – First step towards adopting Predictive AIOps is to identify and collect meaningful data and aggregate information to derive meaningful insights. Observability data helps in detecting potential IT failures, determining business impact and provide patterns for automated problem solving.  
  • Data Analytics and Insights – Machine learning works on application and infrastructure logs, structured and unstructured data and patterns to provide meaningful insights to the Technology teams to prevent critical outages.
  • Integrated Architecture and Collaboration Platform: True Predictive AIOps can only happen once all service management components, data sources, monitoring tools are integrated and working in tandem. AIOps platform should ultimately reduce reliance on data scientists, thereby letting the AI predict incidents based on historical behavior. 
  • Observation and Engagement: Bringing real-time most up-to-date data across all technology stack and ecosystem in a centralized platform and letting analytics provide right insights and forced automation can save precious time. 
  • Automation and Orchestration: Automated remediations helps in significantly accurate resolutions and that too in a time bound manner – resulting in zero touch operations, improving response and resolution times. 

Predictive AIOps makes life easy for Service Providers and Service Consumers

Predictive AIOps applies to a variety of use cases to deliver benefits in streaming and modernizing IT operations.   

  • Reduction in Mean-time-to-repair (MTTR): Predictive AIOps helps in several use cases ranging from order processing, application stability, frequent data-center outages, application upgrades, etc. Predictive AIOps helps in collecting and parsing logs and metrics real-time, correlate anomalies to provide the right insights to Operations teams, and reduce time to resolve problems based on historical patterns and guided insights.  
  • Reactive to Predictive Maintenance: AIOps doesn’t just analyze historical data, it continues to learn in real time. Historic patters, Applications and Infrastructure data, and AI algorithms helps in identifying and proactively preventing potential business disruptions. 
  • Reduction in Business impact: Ability to identify business impact due to outages through services-apps-infrastructure dependencies and proactive resolution to avoid or minimize business impact. 
  • Optimized human effort with Automation and Self-Healing: Automation of IT operations processes and reduction in human effort through events aggregation, guided insights for problem solving and self-healing through AI techniques.

IT expansion will continue breaking all barriers. The need of the hour are disciplines and solutions like Predictive AIOps that could offer enhanced visibility and observability, while facilitating better collaboration and actionability to enhance IT operations. These solutions will also help to eliminate the common barriers of adoption, as the load on humans for analysis, root cause, resolution can ultimately be replaced through Machine Learning and AI; human efforts can be redirected for value added service delivery and other business demanding modernization programs. 


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