ServiceNow Software / SaaS North America

UKG

IT Operations Automation

UKG wanted to cut reactive IT work and improve employee support by combining endpoint signals with workflow automation instead of relying on manual routing and incident handling.

70% Fewer unplanned IT incidents
80% First contact resolution rate, up from 45%
85% Of inbound work items automatically routed using process mining

Reported impact

Business outcome

UKG reports 70% fewer unplanned IT incidents, 85% of inbound work auto-routed, and first-contact resolution up to 80% by automating operations with AI.

  • Productivity Improvement
  • Risk Reduction

Solution

UKG wanted to cut reactive IT work and improve employee support by combining endpoint signals with workflow automation instead of relying on manual routing and incident handling. UKG implemented a ServiceNow-centered stack built around IT Service Management, Predictive Intelligence, CMDB, and RaptorDB. Key components included IT Service Management standardizes incidents and requests; Predictive Intelligence classifies and routes work; CMDB stores service and asset context; RaptorDB accelerates workflow analytics. The stack also connected Tanium so the workflows could reuse existing records, documents, and collaboration channels instead of forcing teams back into manual handoffs. Work enters as cases, requests, or automations, is classified or summarized by Predictive Intelligence, and uses context from CMDB, RaptorDB, and Tanium before IT Service Management routes or fulfills the work. The same record of work is then exposed back to agents and end users through the platform.

Data flow

Work enters as cases, requests, or automations, is classified or summarized by Predictive Intelligence, and uses context from CMDB, RaptorDB, and Tanium before IT Service Management routes or fulfills the work. The same record of work is then exposed back to agents and end users through the platform.

Solution architecture

4 components · 4 layers
  1. Compute
    • Predictive Intelligence Classifies, routes, and prioritizes work using machine-learning models.
  2. Storage
    • RaptorDB Provides fast operational analytics and data processing for workflow insights.
  3. Orchestration
    • IT Service Management Standardizes incident, request, and fulfillment workflows on the platform.
  4. Governance
    • CMDB Maintains service and asset context that automation and support teams rely on.

Architecture clues

  • CMDB quality is treated as a prerequisite for automation, service context, or visibility.
  • RaptorDB is used to accelerate operational analytics and reporting on workflow data.

Evidence from the source

ServiceNow AI Agents that helps UKG deflect reactive IT incidents with AI-powered automation
They also envision a self-service model where employees can interact with AI agents and receive an immediate fix, leading to rapid first-call resolution, significant reductions in reactive incidents, and further freeing up IT experts to continue focusing on more strategic work
This seamless platform integration has significantly improved the employee experience and operational efficiency
Through a phased approach, Zaulyczny set his team’s sights on transforming IT operations using AI, from patchworked data and reactive to data-driven and proactive—and, ultimately, predictive
UKG deflects reactive IT incidents with AI-powered automation Combining Tanium endpoint management with ServiceNow workflows, HCM platform provider enhances its employee experiences with autonomous IT operations 70% Fewer unplanned IT incidents 85% Of inbound work items automatically routed using process mining 80% First contact resolution rate, up from 45% 67% Lower incident resolution time UKG simplifies post-merger complexity with ServiceNow and Tanium In 2020, no blueprint existed for doing an industry-changing merger remotely during a pandemic
Transparency Evidence & methodology 100/100 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 value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    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

    UKG customer story

    First-party
    https://www.servicenow.com/customers/ukg.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 Aug 3, 2026 · ServiceNow · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
  • Unverified extracted date Aug 3, 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.