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ServiceNow Commercial and Professional Services Global

LTM

IT Operations Automation

LTM’s growth to 87,000 employees across 47 countries created siloed HR and IT workflows plus fragmented AI governance that could not scale with the business.

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21% Increase in HR team productivity
72.3% Improvement in mean time to restore unplanned outages
80% Projected improvement in AI asset management efficiency with AI Control Tower

Reported impact

Business outcome

LTM cut IT tickets by 30%, improved HR productivity by 21%, improved MTTR for unplanned outages by 72.3%, saved 400 hours per month on email workflows, and expects AI asset management efficiency to improve by 80%.

  • Compliance
  • Productivity Improvement
  • Revenue Growth

Solution

LTM’s growth to 87,000 employees across 47 countries created siloed HR and IT workflows plus fragmented AI governance that could not scale with the business. LTM implemented a ServiceNow-centered stack built around AI Control Tower, Now Assist, IT Service Management, and HR Service Delivery. Key components included AI Control Tower governs AI inventory and ROI; Now Assist adds generative search, summarization, and drafting; IT Service Management standardizes incidents and requests; HR Service Delivery manages HR cases and onboarding. Work enters as cases, requests, or automations, is classified or summarized by Now Assist before AI Control Tower, IT Service Management, HR Service Delivery, and App Engine routes or fulfills the work. The same record of work is then exposed back to agents and end users through the platform. Running employee support and operational workflows on one data model reduced manual handoffs and made expansion easier.

Data flow

Work enters as cases, requests, or automations, is classified or summarized by Now Assist before AI Control Tower, IT Service Management, HR Service Delivery, and App Engine routes or fulfills the work. The same record of work is then exposed back to agents and end users through the platform.

Solution architecture

5 components · 3 layers
  1. Compute
    • Now Assist Adds generative-AI capabilities such as summarization, drafting, search, and guided responses inside workflows.
  2. Orchestration
    • IT Service Management Standardizes incident, request, and fulfillment workflows on the platform.
    • HR Service Delivery Moves HR cases, onboarding, and employee support workflows onto the same platform.
    • App Engine Supports custom workflow and application development for business processes.
  3. Governance
    • AI Control Tower Provides centralized AI governance, inventory, and ROI tracking across use cases.

Architecture clues

  • AI Control Tower centralizes AI inventory, lifecycle status, and ROI tracking.

Evidence from the source

Analysts lacked a unified view of incidents and often had to manually connect information to understand and prioritize threats
Automated playbooks replaced manual interventions for activities such as firewall changes
Rajesh Kumar CIO, LTM Limited LTM built a new kind of workforce to double its revenue without doubling its headcount LTM is a USD5 billion IT services business with 87,000 employees across 47 countries
Why LTM hired AI agents to double global revenue Employee IDs, managers, and reviews for every AI agent
With 87,000 employees working across 47 countries, processes had become siloed, disconnected, and inconsistent
Transparency Evidence & methodology 95/100 reference value · 79% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. ServiceNow

    LTM customer story

    First-party
    https://www.servicenow.com/customers/ltm.html
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • Sitemap last modified Sep 29, 2026 · ServiceNow · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
  • Unverified extracted date Sep 29, 2026 · ServiceNow · Unknown Legacy extracted date without source-page confirmation.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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