From complexity to clarity: AIOps across cloud deployments using IBM products

From complexity to clarity: AIOps across cloud deployments using IBM products

Artificial intelligence for IT operations (AIOps) enables better, more informed decision-making capabilities through contextualizing and consolidating large volumes of data. As complex data is received from varied sources between hybrid cloud, mainframe, and cloud ecosystems, it can be difficult to navigate and connect diverse data segments. Clarity comes by converging data from each source while gaining the necessary insight to enhance accuracy and speed during both problem resolution and mean time to repair (MTTR).

Digital transformation has accelerated the adoption of hybrid and multi-cloud environments, but it has also introduced new challenges: fragmented visibility across platforms, resource inefficiencies and overprovisioning, alert fatigue and manual troubleshooting, and reactive problem-solving that leads to downtime. Capgemini’s AIOps solution, powered by IBM products, addresses these challenges by enabling predictive insights, intelligent automation, and unified observability.

Capgemini’s AIOps framework is built on three pillars—observability, orchestration, and automation—and is tailored to single, hybrid, and multi-cloud environments. Key capabilities include human-in-the-loop automation for ITSM, AI-powered chatbots and incident remediation, enhanced CMDB and knowledge management, and predictive analytics and anomaly detection. Capgemini’s deep expertise in mainframe and cloud integration ensures seamless deployment across diverse infrastructures.

Capgemini’s AIOps solution is amplified by IBM’s suite of tools: IBM Cloud Pak for AIOps (CP4AIOps) for real-time anomaly detection and automated workflows, IBM Turbonomic for dynamic resource allocation and cloud cost optimization, IBM Instana for application performance monitoring and predictive insights, IBM SevOne NPM for network observability and performance analytics, and IBM Concert for generative AI-powered data harmonization and remediation. Together, these tools create a powerful architecture for proactive, intelligent IT operations.

A leading U.S. bank reduced cloud costs by 30–40% and improved application performance by implementing IBM Turbonomic to right-size EC2 instances, optimize RDS, and reduce memory usage. A UK bank increased platform uptime and reduced incident troubleshooting time by 7–10 minutes using Capgemini’s AI/ML-powered AIOps solution, which included auto-healing and predictive incident management.

Capgemini’s AIOps deployment follows a three-phase approach: Observe (data ingestion via IBM Concert, Instana, and SevOne; strategy definition and KPI alignment), Engage (AI model training with CP4AIOps; automation via Red Hat Ansible; dashboard configuration in Instana), and Act (automated workflows in CP4AIOps; resource optimization with Turbonomic; remediation through IBM Concert).