Enterprise Service Management at Scale — Major Canadian Telecom
25,000+ employees
30% resolution time reduction
The Challenge
- Fragmented ITSM landscape with three platforms in parallel — BMC Remedy, ServiceNow, and Jira — creating operational silos
- No unified Enterprise Service Management (ESM) strategy across the organization
- Slow ticket resolution due to manual routing, duplicate tickets across platforms, and no cross-platform visibility
- $2B+ capital portfolio with no unified project management tooling or governance framework
- Release management inefficiencies causing delays in product delivery and feature deployment
The Approach
Our Approach
This was a multi-year enterprise transformation for one of Canada's major telecommunications companies. The challenge was not just technical — it was organizational: three ITSM platforms running in parallel (BMC Remedy, ServiceNow, and Jira), each with its own processes, teams, and vendor contracts, and a $2B+ capital portfolio with no unified governance.
Phase 1: Platform Strategy & Roadmap
We began with a comprehensive assessment of the existing ITSM landscape: platform capabilities, team dependencies, ticket volumes, and vendor contract terms. The output was a multi-year platform strategy that defined:
- ServiceNow as the unified ITSM/ESM platform — consolidating incident, request, change, and problem management
- Jira/Confluence as the engineering and project management platform — standardizing development workflows and documentation
- BMC Remedy on a managed sunset path — migrating workloads to ServiceNow over a defined timeline
The strategy was not just a recommendation — we took governed ownership of both the ServiceNow and Atlassian platforms, managing vendor relationships and enterprise agreements at the contract level.
Phase 2: ServiceNow Consolidation & AI/ML Routing
The ServiceNow consolidation focused on eliminating the operational silos created by parallel platforms:
- Unified ticket intake — single portal for all service requests regardless of the resolving team
- AI/ML ticket routing — machine learning model trained on historical ticket data to classify, prioritize, and assign tickets automatically
- Cross-platform visibility — dashboards providing leadership with end-to-end service delivery metrics for the first time
The AI/ML routing system started with rule-based classification and was progressively enhanced with ML models as production data accumulated. After three months, the ML routing achieved accuracy comparable to experienced human dispatchers.
Phase 3: Jira/Confluence Enterprise Rollout & Portfolio Governance
The Jira/Confluence rollout was executed at enterprise scale — 6,000+ users across engineering, product management, and project management teams. Each department received configurations tailored to their workflow (Scrum for engineering, Kanban for operations, waterfall for capital projects) while sharing a common governance framework.
The $2B+ capital portfolio was brought under a unified project management solution with consistent reporting, resource allocation, and milestone tracking. Release management processes were standardized across teams, delivering a 20% improvement in release cycle efficiency and reducing ticket resolution time by 30% across the organization.
System Architecture
Technology Stack
Key Outcomes
Resolution Time Reduction
Ticket resolution time reduced by 30% through unified routing, AI/ML classification, and elimination of cross-platform handoffs
Users on Jira/Confluence
Enterprise-wide rollout of Jira and Confluence across engineering, product, and project management teams
Portfolio Under Unified PM
Capital portfolio brought under a single project management solution with consistent reporting and governance
Release Efficiency Improvement
Release management cycle time improved by 20% through standardized processes and automated workflows
What Made It Different
- Governed enterprise platform ownership — not just advisory, but hands-on management of ServiceNow and Atlassian vendor relationships at the contract level
- AI/ML-powered ticket routing that classifies and assigns tickets across the unified platform, eliminating manual triage
- Multi-year roadmap execution spanning platform migration, process standardization, and organizational change management simultaneously
- Jira/Confluence enterprise rollout at 6,000+ user scale with department-specific configurations preserving team autonomy
Lessons & Transferable Patterns
- Platform consolidation in large telecoms requires a multi-year roadmap — attempting to migrate all platforms simultaneously creates organizational resistance
- AI/ML ticket routing accuracy improves dramatically after 3 months of production data — start with rule-based routing and layer ML on top
- Vendor management at the contract level is as critical as the technical implementation — negotiating enterprise agreements unlocks features and pricing that project-level procurement cannot
- Jira/Confluence adoption at 6,000+ users requires dedicated enablement resources — self-service documentation alone is insufficient for non-technical teams
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