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ServiceNow Public Sector Germany

Hamburg Port Authority

IT Operations Automation

Hamburg Port Authority needed to make everyday service workflows genuinely useful to employees before it could scale platform adoption and add AI into port operations.

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88% Increase in user engagement, month on month
90% Of employees use the ServiceNow AI Platform every month
€6.9M Expected operational savings over the next three years

Reported impact

Business outcome

Hamburg Port Authority achieved 90% monthly platform adoption, expects €6.9 million in operational savings over three years, and increased user engagement and knowledge usage sharply.

  • Cost Reduction
  • Productivity Improvement
  • Scalability

Solution

Hamburg Port Authority needed to make everyday service workflows genuinely useful to employees before it could scale platform adoption and add AI into port operations. Hamburg Port Authority implemented a ServiceNow-centered stack built around ServiceNow AI Platform, App Engine, Now Assist, and RaptorDB. Key components included ServiceNow AI Platform provides the common workflow and AI layer; App Engine builds custom workflow apps; Now Assist adds generative search, summarization, and drafting; RaptorDB accelerates workflow analytics. The stack also connected SharePoint 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 Predictive Intelligence, and uses context from RaptorDB and SharePoint before ServiceNow AI Platform and App Engine routes or fulfills the work.

Data flow

Work enters as cases, requests, or automations, is classified or summarized by Now Assist and Predictive Intelligence, and uses context from RaptorDB and SharePoint before ServiceNow AI Platform and App Engine routes or fulfills the work. The same record of work is then exposed back to agents and end users through the platform.

Solution architecture

5 components · 3 layers
  1. Compute
    • Now Assist Adds generative-AI capabilities such as summarization, drafting, search, and guided responses inside workflows.
    • Predictive Intelligence Classifies, routes, and prioritizes work using machine-learning models.
  2. Storage
    • RaptorDB Provides fast operational analytics and data processing for workflow insights.
  3. Orchestration
    • ServiceNow AI Platform Acts as the common workflow and AI platform that unifies service data, automation, and user experiences.
    • App Engine Supports custom workflow and application development for business processes.

Architecture clues

  • 90% Of employees use the ServiceNow AI Platform every month
  • Knowledge content is reused as a source for self-service or AI-assisted answers.
  • RaptorDB is used to accelerate operational analytics and reporting on workflow data.

Evidence from the source

Contact Us Recommended Stories View All Stories Autostrade Per L’Italia SpA keeps traffic flowing with ServiceNow Video CTA Know more ROSSMANN accelerates store from 9 minutes to 5 seconds with agentic AI Booking.com’s AI agents get it right 97% of the time ServiceNow AI Platform puts customers first in Bertelsmann’s GBS transformation
HPA now has the infrastructure that makes AI-scale analytics on live operational data possible: real-time queries running against eight years of compounding workflow history, without extraction, without lag, without a separate analytics environment
Many incidents resolved by HPA's team automatically become a knowledge article for the next person who encounters the same problem
What comes next With agentic AI capabilities in development, the target is for 55% of IT requests to be handled without human intervention—a credible ambition for an organization whose AI layer is already operating inside consolidated, high-quality data
What they got wasn't a better search engine—it was AI that could use eight years of accumulated operational context to provide answers, recommend actions, and improve IT’s productivity
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.

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 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.
02

Traceability

Sources & provenance

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

  1. ServiceNow

    Hamburg Port Authority customer story

    First-party
    https://www.servicenow.com/customers/hamburg-port-authority.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 Aug 27, 2026 · ServiceNow · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
  • Unverified extracted date Aug 27, 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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