Reported impact
Business outcome
EY estimates 160,000 hours and US$5.7 million in annual value from AI agents, with 70% of 103,000 resolution notes accepted unchanged in the first month.
- Cost Reduction
- Productivity Improvement
ServiceNowCommercial and Professional ServicesIT Operations AutomationAI and Machine LearningERP / CRMGenerative AI / LLMCost ReductionProductivity ImprovementUndated ServiceNowSN#1f8476Commercial and Professional ServicesGlobalEY needed its global service desk to handle more than a million tickets a year without forcing agents across 30 service desks to spend time on repetitive note-taking and manual context gathering.160,000 Hours saved annually across 30 service desksModified · Jul 30, 20265IT Operations AutomationAI Agent · Now Assist · ServiceNow AI Platform0 2026-07-30 5
EY
IT Operations Automation
EY needed its global service desk to handle more than a million tickets a year without forcing agents across 30 service desks to spend time on repetitive note-taking and manual context gathering.
160,000 Hours saved annually across 30 service desks
75% Reduction in service ticket volumes
Solution
EY needed its global service desk to handle more than a million tickets a year without forcing agents across 30 service desks to spend time on repetitive note-taking and manual context gathering. EY implemented a ServiceNow-centered stack built around ServiceNow AI Platform, Now Assist, and AI Agent. Key components included ServiceNow AI Platform provides the common workflow and AI layer; Now Assist adds generative search, summarization, and drafting; AI Agent automates note generation and support assistance. Work enters as cases, requests, or automations, is classified or summarized by Now Assist and AI Agent before ServiceNow AI Platform 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. EY estimates 160,000 hours and US$5.
Data flow
Work enters as cases, requests, or automations, is classified or summarized by Now Assist and AI Agent before ServiceNow AI Platform routes or fulfills the work. The same record of work is then exposed back to agents and end users through the platform.
Solution architecture
- Compute
- Now Assist Adds generative-AI capabilities such as summarization, drafting, search, and guided responses inside workflows.
- AI Agent Automates service-desk note generation, summarization, and support assistance.
- Orchestration
- ServiceNow AI Platform Acts as the common workflow and AI platform that unifies service data, automation, and user experiences.
Architecture clues
- 160,000 Hours saved annually across 30 service desks
Evidence from the source
Out of the estimated 103,000 resolution notes generated in the month after implementing the AI agent, 70% of the resolution notes were accepted without changes
That’s why human intelligence needs to be embedded throughout AI processes.” With the ServiceNow AI Platform, we can adapt quickly, respond to people faster and measure what is happening around the world
This saves five minutes for every ticket, allowing service desk agents to quickly move on to the next challenge while providing standardized, detailed reporting that IT can use to spot patterns and problems
Using Now Assist’s generative AI (GenAI) capabilities, it creates resolution notes for the service desk agent to approve before closing the ticket
“The ServiceNow AI Platform enables us to adapt quickly, respond to people faster and measure what is happening around the world,” says Eble
Transparency Evidence & methodology
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 value95/100
- Problem clarity20/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth20/25Rewards named components, data flow, integrations, and architecture detail.
- Outcome specificity20/20Measures whether the reported benefit is concrete rather than a generic claim.
- Quantified impact25/252 metrics describe measurable impact.
- Deployment scale10/10Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
- Source authority20/25The main evidence is an official vendor-published customer story.
- Claim traceability21/2510 of 12 key fields link to a named source.
- Direct evidence20/205 direct source quotes retained.
- Date provenance0/10The publication date has no documented provenance.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity10/10Rewards 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.
- https://www.servicenow.com/customers/ey-ai.htmlServiceNowFirst-party
EY 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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