Reported impact
Business outcome
Fonterra improved MTTR for high-priority incidents by 92%, cut event incident tickets by 90%, and used better service context to speed fulfillment and resilience work.
- Productivity Improvement
- Risk Reduction
ServiceNowConsumer GoodsIT Operations AutomationAI and Machine LearningData AnalyticsData PlatformProductivity ImprovementRisk ReductionUndated ServiceNowSN#1f8476Consumer GoodsNew ZealandFonterra needed a stronger CMDB and more reliable service processes so it could reduce incident noise, accelerate problem management, and build resilient digital operations.90% Reduction in event incident ticketsModified · Jul 30, 20265IT Operations AutomationCMDB · Employee Center · IT Service Management0 2026-07-30 5
Fonterra
IT Operations Automation
Fonterra needed a stronger CMDB and more reliable service processes so it could reduce incident noise, accelerate problem management, and build resilient digital operations.
90% Reduction in event incident tickets
92% Improvement in MTTR for high-priority incidents
Solution
Fonterra needed a stronger CMDB and more reliable service processes so it could reduce incident noise, accelerate problem management, and build resilient digital operations. Fonterra implemented a ServiceNow-centered stack built around ServiceNow AI Platform, IT Service Management, CMDB, and Predictive Intelligence. Key components included ServiceNow AI Platform provides the common workflow and AI layer; IT Service Management standardizes incidents and requests; CMDB stores service and asset context; Predictive Intelligence classifies and routes work. The stack also connected Knowledge Base so the workflows could reuse existing records, documents, and collaboration channels instead of forcing teams back into manual handoffs. Requests enter through Employee Center, are classified or summarized by Predictive Intelligence, and uses context from CMDB and Knowledge Base before ServiceNow AI Platform and IT Service Management routes or fulfills the work.
Data flow
Requests enter through Employee Center, are classified or summarized by Predictive Intelligence, and uses context from CMDB and Knowledge Base before ServiceNow AI Platform and 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
- Compute
- Predictive Intelligence Classifies, routes, and prioritizes work using machine-learning models.
- Serving
- Employee Center Serves a single portal for requests, knowledge, and support interactions.
- Orchestration
- ServiceNow AI Platform Acts as the common workflow and AI platform that unifies service data, automation, and user experiences.
- IT Service Management Standardizes incident, request, and fulfillment workflows on the platform.
- 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.
Evidence from the source
Using AI capabilities within the ServiceNow AI Platform, we have reduced the average mean time to resolution for high-priority incidents by 92%
With evolving business needs and multiple sources of CI data, it is an enormous challenge to deliver consistent data and ongoing maintenance
that helps Fonterra drive efficiency and business value with the AI platform
“Using the AI capabilities within the ServiceNow AI Platform, we have reduced the average mean time to resolution for high-priority incidents by 92%,” Victoria adds
“With ServiceNow, our Problem Manager can start the review process in less than 48 hours, an 80% reduction in time
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 value100/100
- Problem clarity20/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth25/25Rewards 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/fonterra.htmlServiceNowFirst-party
Fonterra 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.
Related
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