ServiceNow Healthcare San Diego, California

American Specialty Health

Enterprise Knowledge Search · IT Operations Automation

American Specialty Health wanted to modernize employee IT service and operations so staff spent less time manually fixing internal issues and more time helping the business, but legacy systems and manual processes made support, incident handling, and service desk work too slow.

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43% reduction in Level 1 support tickets

49% reduction in Level 2 tickets

6 weeks to deploy AI-powered virtual agent chatbot

Reported impact

Business outcome

ASH deployed its AI-powered virtual agent in six weeks, cut Level 1 support tickets by 43% and Level 2 tickets by 49%, reduced resolution times, and freed service desk staff to focus on higher-value technical work and AI upskilling.

Solution

American Specialty Health standardized employee support on ServiceNow AI Platform with IT Service Management and IT Operations Management replacing legacy systems and manual processes. ITSM and ITOM consolidate service and operations data on one platform, while Service Operations Workspace gives teams a single pane of glass for incidents, collaboration, performance monitoring, and continuity. ASH then layered AI Agents, CMDB context, and Now Assist capabilities onto that base. Centralized CMDB relationships let AI Agents assemble reports and incident context from how systems interact, and the AI-powered virtual agent uses Now Assist to understand intent, take action, answer common questions, open tickets, and reset passwords across email, chat, and the service portal. AI search and auto-generated summaries reduce administrative effort for service desk staff and make escalations faster and better informed.

Data flow

Employee requests arrive by email, chat, or the service portal, where the Now Assist-powered virtual agent answers common questions, opens tickets, or completes simple tasks. Complex issues flow into ITSM and ITOM, where Service Operations Workspace organizes the work, CMDB relationships add system context, AI Agents assemble reports, and AI Search plus autogenerated summaries help agents and engineers resolve and document incidents faster.

Solution architecture

8 components · 4 layers

Compute
- AI Agents Pull together reports and operational context so IT teams can handle incidents more easily.
- Now Assist Powers the virtual agent so it can understand intent, take action, and accelerate support resolution.
Serving
- Service Operations Workspace Gives teams one workspace to collaborate on incidents and monitor service performance.
- AI Search Finds knowledge article answers quickly for service desk agents working tickets.
Orchestration
- ServiceNow AI Platform Acts as ASH's shared workflow backbone for employee support and operations.
- IT Service Management Runs incident, request, and service desk workflows on the unified platform.
- IT Operations Management Extends the platform into operations monitoring and service reliability work.
Governance
- CMDB Provides the system relationship context that AI and support teams use during incident handling.

Architecture clues

CMDB data is used to model how systems interact before AI agents generate reports and incident context.
Employees can request support through email, chat, and the service portal.
Service Operations Workspace gives teams a single pane of glass for incidents and performance monitoring.

Evidence from the source

ASH deployed ITSM and ITOM to replace legacy systems and manual processes with automation and streamlined experiences.

ASH reduced Level 1 support tickets by 43% and Level 2 tickets by 49%.

This sped up development time, allowing them to deploy the AI-powered virtual agent in just six weeks.

With ServiceNow's Now Assist platform, the virtual agent can tackle complex, multi-step/variable processes, understand intent, and take action.

With centralized data in the CMDB providing understanding into how systems interact, AI agents can pull together reports that allow IT teams to handle incidents more easily.

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.

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.

Traceability

Sources & provenance

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

ServiceNow
American Specialty Health customer story

First-party
https://www.servicenow.com/customers/american-specialty-health.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 Jul 30, 2026 · ServiceNow · Publisher metadata Publisher-reported modification timestamp; not a publication date.
Open date source
Unverified extracted date Jul 30, 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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