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Driving next-level customer experiences with flawless Predictive IT Operations


Srinivasan Shanmugam
Nov 20, 2023

Driving continuous service and next-level customer experiences with flawless Predictive IT Operations adoption

In utilizing Intelligent Automation (IA), predictive SLA management enables a proactive approach to SLA management that addresses issues early on – and prevents critical IT applications or services from failing. It’s a method for managing SLAs that can help you increase customer satisfaction, cut operational expenses, and improve service quality.

In the second piece of this two-part blog series, Srinivasan Shanmugam and Sachidanand Padgaonkar will be breaking down the adoption stages of the Intelligent Automation to discover how businesses can leverage AIOps and other continuous improvement solutions to improve end-user experience and drive higher growth.

Leveraging AIOps, monitoring, and RPA solutions for superior SLA performance

Businesses can maintain healthy SLA management by adopting a proactive and predictive approach made possible by Capgemini’s Intelligent Automation. Instead of waiting for issues to occur and then reacting, businesses can leverage Intelligent Automation to identify and resolve potential issues before they impact SLA performance.

For instance, coupled with AIOps, Capgemini’s automated monitoring solution, Application Performance Management (APM), can detect service degradation or potential outages – triggering proactive notifications and alerts to initiate corrective actions. The overall architecture of Predictive IT Operations can be seen in the above figure.

Predictive IT Operations adoption phases

When designing and implementing successful Predictive IT Operations initiatives, we’ve identified three different phases of adoption:

  1. Monitoring of runtime data (data collection and integration)
  2. Prediction of failures (data analysis and correction through AIOps)
  3. Self-action composition (proactive corrective management)

The first stage is to ensure that all relevant ADM services data is collected via monitoring tools and stored in a centralized repository known as metric database. This combines both historical data and real-time performance metrics. IT support teams can provide a detailed map of the service landscape by combining these data sources.

After the data is collected, AIOps solutions (i.e., the predictors), can perform advanced analytics on the metric database via a checkpoint database (with business rules) and correlation techniques to uncover patterns and detect anomalies. By applying machine learning algorithms, it can identify possible SLA violations and provide notifications, tickets, or suggestions for preventive measures. AIOps can not only predict potential SLA breaches but is also able to proactively manage incidents through self-action solutions before they impact service quality.

The real-time action adapter requires the information contained in the ITSM database (the application details needed for self-action) and SLA database to decide if process instances are likely to violate SLAs – and which actions can be triggered in these cases. The SLA, an action orchestrator, and checkpoint databases are all inputs in our approach. For high-priority incidents, the EAF that is analyzing data patterns and applying root-cause analysis techniques can help IT support teams identify the underlying causes of issues and suggest remedial actions to fix issues before any service failures.

By analyzing the historical data and performance trends of computing resources (i.e., the utilization of CPU, memory, technical daemon, etc.), AIOps solutions can forecast future resource requirements and recommend suitable scaling measures. For instance, if there is a large spike in web traffic during a promotional campaign, it could recommend scaling up web servers to accommodate the anticipated load – or notify the IT support team ahead of time – so they could take the appropriate steps. These steps could include optimizing disc space or adding more storage as part of capacity planning to ensure continuous service, a seamless user experience, and avoid SLA breaches.

Accurate capacity planning and optimization is vital to ensure service performance and availability.  Businesses can optimize their IT infrastructure by identifying underutilized resources, predicting workload increases, and matching capacity needs thanks to SLA management through predictive IT operations that utilize AIOps.

Customer satisfaction and retention with Capgemini’s continuous improvement approach to AIOps

SLA management via Predictive IT Operations with AIOps is an iterative process and part of Capgemini’s continuous improvement approach to AIOps. By continuously monitoring service performance, analyzing data, and honing predictive models, we help IT teams can enhance their ability to meet SLA requirements and improve service quality over time through standardizing processes and minimizing human error. Consistently fulfilling SLAs will instill trust within customers and encourage heightened loyalty, along with helping your business stay ahead of the curve and provide next-level customer experiences.

If businesses want to harness the potential of Predictive IT Operations to manage SLAs using AIOps proactively, they should invest in a robust AIOps solution that can collect, analyze, and make sense of large volumes of IT data, which can provide comprehensive visibility into ADM services. Furthermore, to ensure an integrated approach to proactive SLA management, boost business growth, and obtain a competitive advantage in the market, businesses must promote a culture of collaboration between IT and business teams.

As businesses continue on their unique digital transformation journeys, Predictive IT Operations for SLA management powered by AIOps will play a vital role in helping them stay ahead of the curve and delivering exceptional IT services. So why hold off? It’s time to advance your own SLA management and realize the full potential of Capgemini’s Intelligent Automation solution (a part of ADMnext).

To learn more about how we can help your business with Predictive IT Operations adoption or share your Intelligent Automation and AIOps experiences and feedback, contact us below.

Meet the authors

Srinivasan Shanmugam

Director, ADMnext
With more than 25 years of industry experience, I lead our ADM Automation and Smart Analytics offerings (with a focus on incident mining). I work with clients to develop outcome-driven, intelligent-automation strategies and solutions that generate maximum business value. In managing Smart Analytics – our service offering clients unified views and actionable insights – to help them make informed decisions that enhance their overall IT operational performance.

Sachin Padgaonkar

Vice President, ADM Solutions
I lead the portfolio ADM solutions team across all markets and specialize in ADM solutions for multi-tower deals. With +27 years of experience, I work with clients to develop value propositions around ADMnext to help them succeed on their growth journeys. I craft unparalleled offerings that serve to position Capgemini as business and technology leader for all of our clients by delivering unique business value. I’m also a seasoned SAP practitioner with experience across sales, delivery, and solutioning.