AI netOps for Resilient Global Networks

Overview

A leading global chemical manufacturer undertook a large-scale network transformation to improve visibility, resilience, and governance across its distributed enterprise operations. Tech Mahindra implemented an AI-enabled co-managed network model powered by its netOps.ai framework, unifying network, security, automation, and service management platforms into a single observability layer to strengthen operational efficiency and transparency.

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Industry Challenge

The global manufacturer, headquartered in Germany and with an expanding network worldwide, relied on fragmented monitoring tools and manual operational processes. Limited end-to-end visibility, rising incident volumes, and inconsistent operational governance across regions slowed response times and increased operational complexity. These challenges highlighted the need for a standardized, resilient, and co-managed network operating model.

Our Approach and Solution

Business Technology Alignment

Aligned operational objectives with an AI-driven NetOps model to shift from tool-centric monitoring to signal-driven, outcome-focused network operations.

Unified Network Architecture

Implemented a resilient global backbone and WAN overlay architecture, enabling consistent connectivity across regions, including a dedicated and isolated China environment for compliance.

AI-Enabled Observability

Integrated network, security, automation, and service management platforms into a single observability layer, enabling event correlation, noise reduction, and faster incident triage.

Service Governance and Automation

Enabled centralized service governance through ServiceNow SIAM and deployed automation catalogs to improve coordination, operational efficiency, and shared accountability.

Controlled Execution at Scale

Executed a phased migration approach, transitioning over 600 locations to the new network fabric within a defined timeline while ensuring operational continuity.

Benefits

  • Reduced unplanned network downtime by 32%, improving operational continuity
  • Achieved real-time visibility across global facilities through unified observability
  • Increased production throughput by 14% increase, supported by improved network reliability
  • Improved workforce productivity with automation deployed from day one
  • Strengthened governance and resilience through a co-managed operating model