ServiceNow Telecommunications Vienna, Austria

A1 Telekom

Customer Service Automation

A1 Telekom needed to eliminate historic operational discontinuities across sales, support, and operations so it could deliver faster, more self-service-oriented B2B customer support on a single standardized platform.

1 Standardized platform consolidating multiple systems
360° Overview of the customers
Centralized view of IT and service data

Reported impact

Business outcome

A1 Telekom established a centralized service platform with a 360-degree customer view, improved incident handling for employees and field engineers, and materially advanced its B2B customer self-service and satisfaction goals.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

A1 Telekom worked with ServiceNow on a digitization roadmap that combined Customer Service Management for customer-facing support workflows with IT Service Management for the internal operational backbone. Customer requests, service orders, and support interactions were moved off a patchwork of tools and partners onto a unified platform with automation, machine-learning guidance, chatbots, and a self-service portal. The company also centralized network, telecommunications, and IT service data so employees could troubleshoot incidents in context instead of switching among several systems. Field engineers use the same service ecosystem through a mobile app with online and offline access. The resulting architecture ties together customer interactions, operational data, and technician workflows so A1 can deliver faster support, clearer SLA oversight, and a more consistent end-to-end service experience.

Data flow

Customers place requests and orders through A1's catalog and self-service support experience, where automation and machine-learning guidance help route work. ServiceNow then combines ticket context with network, telecommunications, and IT service data inside IT Service Management, and field engineers consume the same information through the mobile app when they need to intervene onsite.

Solution architecture

3 components · 2 layers
  1. Serving
    • ServiceNow mobile app Gives field engineers online and offline access to service information while working at customer sites.
  2. Orchestration
    • Customer Service Management Supports automated customer service processes, self-service ordering, and customer support interactions.
    • IT Service Management Centralizes network, telecommunications, and IT service data so employees can analyze and resolve incidents from one platform.

Architecture clues

  • A1 centralized network, telecommunications, and IT service data on one ServiceNow platform.
  • Employees work with knowledge-based flows that suggest the best solution for a customer inquiry or ticket.
  • Field engineers can access service information online and offline from a mobile app while onsite.

Evidence from the source

At the touch of a button, live tech bots give us a 360-degree view of our customers.
ServiceNow Customer Service Management allows for service quality improvements to be delivered through automation, machine learning, chatbots, and a self-service customer portal.
They no longer need to navigate their way around several systems. Instead, all the data necessary to intervene in a fault is in a single platform.
We can now automate a mind-boggling number of process steps.
We now have a very transparent IT ecosystem and can view network data and telecommunications data as well as IT service data very easily.
Transparency Evidence & methodology 75/100 reference value · 77% 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 value75/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence77%
  • 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 specificity8/10
    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

    A1 Telekom customer story

    First-party
    https://www.servicenow.com/customers/a1-telekom.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

  • First archived Mar 24, 2023 · 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.