Rapid ServiceNow ITOM Implementation for Enhanced IT Asset Discovery & Infrastructure Visibility

A professional analyzing system data on a tablet and laptop inside a modern server facility, representing ServiceNow ITOM Implementation and real-time operational monitoring.

Introduction

In modern IT environments, organizations often struggle with fragmented visibility, inaccurate asset data, and manual inventory tracking. These gaps hinder operational efficiency, delay incident resolution, and increase compliance risks. This case study highlights how a fast, 6-week ServiceNow ITOM implementation helped a mid-size IT services company strengthen IT asset discovery, improve CMDB accuracy, and gain real-time IT infrastructure visibility with ServiceNow.

Client Overview

The client is a mid-size IT services provider based in India, supporting application development, managed services, and infrastructure operations for multiple domestic and international customers. The organization manages a hybrid IT environment consisting of on-premise data center assets, co-located servers, and a growing footprint of virtualized workloads hosted across AWS and Azure.

Their infrastructure included :-

  • 700+ physical servers across Windows, Linux, and UNIX
  • 1,200+ virtual machines supporting customer environments and internal applications
  • 250+ network devices, including switches, routers, firewalls, and load balancers
  • Numerous peripheral hardware assets, such as backup devices, storage arrays, and hypervisor clusters
  • Distributed hardware across three Indian locations (Chennai, Bengaluru, Pune)

Although ServiceNow ITSM was already in use for Incident, Change, and Request Management, the client lacked unified, automated ServiceNow Discovery across on‑premises and cloud environments, consistent hardware asset management, and a reliable ServiceNow CMDB that could keep up with the scale and dynamics of this hybrid estate.

Challenges & Pain Points

Scale-driven inventory sprawl

With 700+ physical servers and 1,200+ VMs spread across on‑prem, co-lo, and cloud, the client relied heavily on manually maintained spreadsheets per data center or team, leading to frequent mismatches in counts and details.

Incomplete hybrid visibility

While some on‑premise subnets were partially documented, there was no automated discovery of AWS and Azure workloads, leaving many cloud-hosted VMs and services completely absent from the CMDB.

Fragmented CMDB for infrastructure CIs

Existing CMDB entries for servers, network devices, and storage were a mix of legacy imports, ad‑hoc manual creations, and outdated records, often with missing IPs, serial numbers, or ownership details.

Weak relationship mapping at scale

For hundreds of servers and network devices, relationships to applications, databases, and customer environments were either missing or incorrect, making it impossible to visualize multi-tier architectures across regions and clouds.

Difficulty identifying unused or misaligned assets

With such a large server and VM footprint, the team could not systematically detect orphaned VMs, underutilized servers, or network devices that were still powered on but no longer supporting active services.

Limited cloud tagging and alignment with CMDB

AWS and Azure resource tags were inconsistent and not reconciled with CMDB CI attributes, causing disconnects between cloud cost centers, owners, and the configuration data seen in ServiceNow.

Delayed incident resolution across regions

When incidents affected customer environments hosted in different locations or clouds, support teams struggled to quickly pinpoint the exact combination of physical servers, VMs, and network devices involved.

Increased operating and hosting costs

Overprovisioned compute, idle VMs in cloud subscriptions, and legacy on‑prem servers that were never formally decommissioned all contributed to avoidable spend on hardware, licenses, and cloud resources.

Complex audits across locations & cloud

Auditors requested consolidated views of all hardware and infrastructure CIs across Chennai, Bengaluru, Pune, and cloud workloads, but the client could only provide fragmented exports from multiple spreadsheets.

Limited capacity planning for new customer onboarding

With unreliable data on current utilization and available capacity, sales and delivery teams had difficulty estimating infrastructure needs for new managed-services customers or large application deployments.

Heightened risk in a hybrid landscape

Configuration drift between on‑prem and cloud environments, unmanaged shadow IT in the cloud, and lack of end‑to‑end traceability from customer service to underlying infrastructure increased operational and security risk.

Solution We Implemented

Our team executed a targeted, rapid ServiceNow ITOM implementation that unified discovery across on‑prem data centers, co-located environments, and cloud workloads in AWS and Azure, while uplifting Hardware Asset Management and CMDB data quality. The 6‑week program was structured to deliver quick value on visibility and audit readiness without disrupting live customer environments.

1. Unified Discovery Architecture for Hybrid Infrastructure

  • Discovery strategy and scoping
    • Segmented discovery scope into three logical zones: on‑prem data centers (Chennai, Bengaluru, Pune), co-located facilities, and cloud subscriptions (AWS and Azure).
    • Prioritized subnets and VPC/VNet ranges hosting customer-facing workloads and shared platforms to maximize impact from the first discovery cycles.
    • Defined credential strategies for each layer: domain/WMI for Windows, SSH for Linux/UNIX, SNMP/CLI for network devices, vCenter for hypervisors, and API/role-based access for AWS and Azure.
  • MID Server deployment and placement
    • Deployed dedicated MID Servers in the primary data center to handle on‑prem, co-lo, and outbound cloud API communication, ensuring low-latency access to 700+ physical servers and 250+ network devices.
    • Configured network routes, firewall rules, and proxy settings so MID Servers could securely reach AWS and Azure endpoints for cloud discovery, while keeping traffic contained within approved segments.
    • Tuned concurrency, ECC queue handling, and logging to efficiently manage large discovery jobs without saturating links between locations.
  • On‑prem and network device discovery
    • Enabled agentless discovery schedules to scan the core subnets in Chennai, Bengaluru, and Pune, as well as co-lo ranges, identifying all reachable Windows, Linux, and UNIX servers.
    • Activated patterns for network gear (switches, routers, firewalls, load balancers) and configured SNMP communities and CLI credentials to classify and fingerprint the 250+ devices.
    • Used credential-less and identity-based discovery techniques for subnets where traditional credentials were not available, tagging results for later enrichment.
  • Cloud discovery (AWS and Azure)
    • Integrated ServiceNow with AWS and Azure using cloud-native discovery, leveraging IAM roles/service principals and tag-based discovery to pull EC2/VM, storage, and network objects into the CMDB.
    • Aligned AWS account and Azure subscription metadata with CI attributes (such as account ID, subscription name, region) so operations teams could easily filter and report by cloud provider and region
    • Ensured cloud discovery schedules ran with a higher frequency for dynamic workloads, capturing the creation and termination of VMs to reduce “invisible” cloud drift.

2. CMDB Normalization and Data Quality for 700+ Servers and 1,200+ VMs

  • Consolidation of multiple inventories
    • Imported key attributes from existing spreadsheets maintained by server, network, and operations teams and reconciled them against freshly discovered CIs.
    • Identified mismatches in hostnames, IPs, and asset tags and used rule-based reconciliation to decide which records to keep, merge, or retire.
    • Archived stale and unused CI records that had no recent discovery activity, no active incidents/changes, and no valid asset link, while maintaining audit logs.
  • Standardized CI modeling using CSDM
    • Applied a consistent class model for :-
      • Physical servers (Windows, Linux, UNIX) as cmdb_ci_server and platform-specific subclasses.
      • Virtual machines (on-prem and cloud) as cmdb_ci_vm_instance, with differentiating attributes for vCenter, AWS, and Azure.
      • Network devices as cmdb_ci_netgear, including switches, routers, firewalls, and load balancers.
      • Storage arrays and backup devices as appropriate storage CI classes.
    • Implemented mandatory fields and controlled value lists for environment (Prod/UAT/Dev), location (Chennai/Bengaluru/Pune/co-lo/cloud region), customer association, and lifecycle state.
  • Relationship and service context enrichment
    • Leveraged discovery results to build relationships between :-
      • VMs and ESXi hosts/hypervisor clusters.
      • Physical servers and their top-of-rack switches.
      • Cloud VMs and their corresponding VPC/VNet, subnet, and security group objects.
    • Connected key customer-facing application services to their underlying infrastructure, enabling infrastructure and operations teams to see which of the 1,200+ VMs and 700+ servers were critical for specific customers.

3. Hardware Asset Management Integration at Scale

  • CI–Asset mapping and reconciliation
    • Linked discovered CIs with hardware asset records using serial number, asset tag, and manufacturer/model, reducing duplicate or orphaned entries across 700+ physical servers and peripheral devices.
    • Set reconciliation precedence so that technical attributes (IP, OS version, CPU/RAM) were driven by discovery, while financial and lifecycle attributes came from hardware asset records.
    • Flagged CIs with no matching asset record and assets with no matching CI to drive clean-up actions and improve both financial and technical completeness.
  • Lifecycle alignment and governance
    • Aligned CMDB lifecycle states and HAM statuses to ensure consistent behavior across on‑prem and cloud resources (for example, a decommissioned physical server automatically moving its CI to “retired”).
    • Introduced approval workflows for provisioning and decommissioning that automatically created or updated CIs and hardware assets, reducing manual data entry and missed updates.
    • Defined governance rules to handle co-lo and cloud resources, ensuring that hardware, virtual, and cloud assets all followed a common lifecycle and ownership model.

4. Visibility, Dashboards, and Operational Use Cases

  • Infrastructure and coverage dashboards
    • Built dashboard views showing discovery coverage across
      • Locations (Chennai, Bengaluru, Pune, co-lo).
      • Technology domains (servers, VMs, network, storage).
      • Cloud providers (AWS vs. Azure).
    • Exposed KPIs such as percentage of CIs with complete ownership and location data, number of duplicates removed, and the ratio of discovered vs. manually created CIs.
  • Service-aware operational views
    • Enhanced Incident, Change, and Problem views to show the full CI context, including upstream/downstream relationships and whether a CI was on-prem, co-lo, or cloud.
    • Enabled on-call teams to quickly filter affected CIs during major incidents by customer, location, or cloud provider, improving triage and response.
    • Provided pre-built reports for auditors showing traceability from hardware asset to CI to related changes and incidents, across all three Indian locations and cloud platforms.

5. Knowledge Transfer, Operating Model, and Future ITOM Expansion

  • Operational playbook and roles
    • Delivered a structured playbook describing how to :-
      • Onboard new IP ranges, VPCs/VNets, and cloud accounts into Discovery.
      • Maintain credentials, monitor MID Server health, and troubleshoot failed probes or API calls.
      • Review CMDB health dashboards weekly and drive remediation actions.
    • Defined ownership for each domain (server, network, cloud, HAM, CMDB), including who approves new CI classes, who validates cloud tags, and who owns asset lifecycle updates.
  • Readiness for advanced ITOM capabilities
    • Designed the CMDB and relationships to be compatible with later adoption of Service Mapping and Event Management, so that the same 700+ servers, 1,200+ VMs, and 250+ network devices could feed richer service models and event correlation.
    • Recommended next steps such as integrating monitoring tools for event aggregation, enabling cloud cost and utilization reporting on top of the normalized CMDB, and gradually expanding discovery into additional cloud regions and customer environments.

Results

  • 88% improvement in CMDB accuracy across 700+ physical servers, 1,200+ VMs, and 250+ network devices, giving teams trustworthy asset data to act on.
  • 95% discovery coverage of prioritized data center subnets, co-lo ranges, and AWS/Azure cloud workloads, enabling near real-time visibility of critical infrastructure.
  • 25% reduction in mean time to resolve (MTTR) hardware-related incidents due to clearer infrastructure dependency mapping and faster CI context for support teams.
  • 60% decrease in audit exceptions related to asset lifecycle and configuration, thanks to automated discovery replacing manual spreadsheets and improved hardware asset governance.
  • Identification and elimination of 25+ unused or duplicate servers and VMs, reducing hosting and cloud costs, power, and maintenance overhead.
  • Automated CI reconciliation between Discovery and Hardware Asset Management improved financial and operational accountability for infrastructure assets.
  • Dashboards for CMDB health, discovery coverage, and cloud-to-ground visibility empowered proactive operational reviews and better capacity planning.
  • Established operating playbook and role-based governance for maintaining Discovery schedules, credentials, and CMDB health, building organizational readiness for advanced ITOM capabilities like Event Management and Service Mapping.

Conclusion

Through a focused and efficient ServiceNow ITOM implementation, the client transformed their asset visibility and operational efficiency. Automated IT asset discovery, improved ServiceNow CMDB quality, and stronger hardware asset management in ServiceNow laid a solid foundation for scalable IT operations.
If your organization is facing similar challenges with manual asset tracking or limited infrastructure visibility, LMTEQ can help accelerate your IT transformation with proven ServiceNow expertise.

Ready to modernize your IT operations with ServiceNow?
Talk to our experts today.

As an AI-powered transformation partner, we help businesses unlock potential with ServiceNow to achieve faster, high-quality outcomes.

Quick Links

Services

Get In Touch

Facebook X-twitter Linkedin Instagram

© 2025 LMTEQ All rights reserved.

In collaboration with KMTEQ & Finklr

×