ServiceNow Software / SaaS Global

ServiceNow

IT Operations Automation · Real-time Analytics

ServiceNow had hundreds of complex internal processes where bottlenecks and rework were hard to spot with traditional reporting alone.

1 Product to optimize 900 processes
28% faster
74% Of HR cases discovered for recategorization
84% Faster change management processes

Reported impact

Business outcome

ServiceNow cut change-management processes by 84%, discovered 74% of HR cases that needed recategorization, and uses one optimization product across about 900 processes.

  • Productivity Improvement
  • Speed to Market

Solution

ServiceNow had hundreds of complex internal processes where bottlenecks and rework were hard to spot with traditional reporting alone. ServiceNow implemented a ServiceNow-centered stack built around Process Mining, Workflow Data Fabric, RaptorDB, and Now Assist. Key components included Process Mining finds bottlenecks and rework in process data; Workflow Data Fabric connects operational data and automation context; RaptorDB accelerates workflow analytics; Now Assist adds generative search, summarization, and drafting. Work enters as cases, requests, or automations, is classified or summarized by Process Mining and Now Assist, and uses context from RaptorDB before Workflow Data Fabric routes or fulfills the work. The same record of work is then exposed back to agents and end users through the platform. Running process analytics and continuous improvement 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 Process Mining and Now Assist, and uses context from RaptorDB before Workflow Data Fabric 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 · 3 layers
  1. Compute
    • Process Mining Analyzes workflow event data to uncover bottlenecks, loops, and rework.
    • Now Assist Adds generative-AI capabilities such as summarization, drafting, search, and guided responses inside workflows.
  2. Storage
    • RaptorDB Provides fast operational analytics and data processing for workflow insights.
  3. Orchestration
    • Workflow Data Fabric Connects operational data across systems and powers automation with shared context.

Architecture clues

  • RaptorDB is used to accelerate operational analytics and reporting on workflow data.
  • Workflow Data Fabric is used as the shared context layer across multiple systems and workflows.

Evidence from the source

But before we went all in on using Process Optimization throughout the enterprise, we had to see if it added measurable value
Incident management: Any time an incident is generated across ServiceNow IT services, whether it’s user created (reactive) or created through monitoring (proactive) 2
It was difficult to know where to start and what to fix to drive more productivity and enable better employee and customer experiences
Using process mining to uncover and repair inefficiencies 84% Faster change management processes 74% Of HR cases discovered for recategorization 1 Product to optimize 900 processes Discovering process inefficiencies We rely on process mining to find friction points that slow down daily ServiceNow processes, then we systematically fix these issues to be more productive
Vijay Kotu SVP, Analytics These were our results using ServiceNow Process Optimization for two weeks: Incident management Inefficiencies: - High CPU alerts consuming IT operations team bandwidth (automation opportunity) IT operations team manually triaging the micro-segmentation heartbeat failure INC Too many escalations from L1/L2 to L3 teams Potential impact if improved: 28% faster 5% more productive Asset management Inefficacies: 22% of requests taking additional step, adding an average of three days to the resolution 30% of requests waiting for four days for approval; Only 2% of requests getting rejected by managers 22% of requests took 10 days for license allocation after approval 31% faster 12% more productive Problem management Delay in problem assessment by the problem coordinator Delay in closing the root cause analysis task by the task assignee Delay in starting the remediation task by the task assignee 40% faster 22% more productive Request management Delay in server access request fulfillment, requiring considerable manual effort High volume for active directory group lifecycle management Manual intervention required to fulfill server reboot request 25% faster 10% more productive HR case management 74% of HR cases are recategorized 60% of the HR cases take longer than six days to resolve Case resolution is delayed, resulting in a lower employee NPS score 46% faster 34% more productive Our in-platform Process Optimization product identified very specific opportunities for improvement within each workflow
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

    ServiceNow customer story

    First-party
    https://www.servicenow.com/customers/now-on-now-process-optimization.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

  • First archived Jun 3, 2024 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Jul 30, 2026 · ServiceNow · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.