Reported impact
Business outcome
Brenntag expects to reduce global service tickets by 5%, lower MTTR by 30%, and cut the time users spend searching and submitting requests by 33%.
- Customer Experience
- Productivity Improvement
ServiceNowManufacturingIT Operations AutomationAI and Machine LearningDevOpsGenerative AI / LLMCustomer ExperienceProductivity ImprovementUndated ServiceNowSN#1f8476ManufacturingGlobalBrenntag’s global IT and finance operations across 72 countries were slowed by manual tasks, fragmented systems, and too much time spent searching for help or resolving routine requests.30% Expected decrease in mean time to resolve (MTTR)Archived · Aug 10, 20265IT Operations AutomationIT Service Management · Now Assist · ServiceNow AI Platform0 2026-08-10 5
Brenntag
IT Operations Automation
Brenntag’s global IT and finance operations across 72 countries were slowed by manual tasks, fragmented systems, and too much time spent searching for help or resolving routine requests.
30% Expected decrease in mean time to resolve (MTTR)
33% Expected reduction in time users spend searching and submitting
5% Expected reduction in global service tickets
Self-service and automation to cut ticket volumes 5%
Solution
Brenntag’s global IT and finance operations across 72 countries were slowed by manual tasks, fragmented systems, and too much time spent searching for help or resolving routine requests. Brenntag implemented a ServiceNow-centered stack built around ServiceNow AI Platform, IT Service Management, and Now Assist. Key components included ServiceNow AI Platform provides the common workflow and AI layer; IT Service Management standardizes incidents and requests; Now Assist adds generative search, summarization, and drafting. The stack also connected SharePoint and Microsoft Teams 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 Now Assist, and uses context from SharePoint and Microsoft Teams before ServiceNow AI Platform and IT Service Management routes or fulfills the work.
Data flow
Work enters as cases, requests, or automations, is classified or summarized by Now Assist, and uses context from SharePoint and Microsoft Teams 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
- Now Assist Adds generative-AI capabilities such as summarization, drafting, search, and guided responses inside workflows.
- 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.
Architecture clues
- Knowledge content is reused as a source for self-service or AI-assisted answers.
Evidence from the source
Disconnected systems and inconsistent processes impact efficiency With disconnected systems and inconsistent processes across 72 countries, capturing and comparing service performance data was a struggle for Brenntag
Fragmented systems, poor visibility, and frustrated users can impact productivity and business performance
Global knowledge sharing will cut resolution time in half With everything running on ServiceNow, Brenntag has a single source of truth for all IT and financial services
Now with Agentic AI and Now Assist we can further optimize cost-per-volume and augment how we work, with greater productivity and coverage
“Using ServiceNow and AI workflows, we’ve reduced manual tasks for our IT teams, freeing them up for more interesting work,” confirms Michelle Wu, Chief Digital Data and Technology Officer at Brenntag
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 impact25/254 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/brenntag.htmlServiceNowFirst-party
Brenntag 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 Aug 10, 2026 · 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.
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