Microsoft Manufacturing Germany

Siemens Smart Infrastructure

Document Processing

These field service technicians generate more than 1.4 million work-order reports every year for customers. Siemens wanted to harness AI to elevate and standardize report quality to help customers stay informed and efficiently operate buildings, avoid downtime, and meet sustainability goals. It turned to longtime ally Microsoft to develop a generative AI–powered solution that would enhance the customer experience.

Service technicians from the Siemens Smart Infrastructure Buildings Business Unit generate more than 1.4 million work-order reports for customers every year, which is why the company is harnessing generative AI to elevate and standardize report quality.
Siemens, one of the world’s largest technology companies, is pioneering generative AI solutions to enrich customer engagement and employee satisfaction. The Siemens Smart Infrastructure Buildings Business Unit helps organizations optimize facility operations, and its more than 10,000 field service technicians perform preventative maintenance and repairs, offering valuable touchpoints with customers.
These field service technicians generate more than 1.4 million work-order reports every year for customers. Siemens wanted to harness AI to elevate and standardize report quality to help customers stay informed and efficiently operate buildings, avoid downtime, and meet sustainability goals. It turned to longtime ally Microsoft to develop a generative AI–powered solution that would enhance the customer experience.

Reported impact

Business outcome

Siemens expects to see high quality and more-standardized reports from its more than 10,000 field service technicians from the Buildings Business Unit, as well as streamlined workflows, enhanced customer communication, and improved job satisfaction.

  • Customer Experience
  • Productivity Improvement

Solution

Service technicians from the Siemens Smart Infrastructure Buildings Business Unit generate more than 1.4 million work-order reports for customers every year, which is why the company is harnessing generative AI to elevate and standardize report quality. Siemens Smart Infrastructure used Microsoft Copilot for Dynamics 365 to develop and begin testing an application that helps its field technicians from the Buildings Business Unit generate clear, consistent work-order reports for customers. Siemens expects to see high quality and more-standardized reports from its more than 10,000 field service technicians from the Buildings Business Unit, as well as streamlined workflows, enhanced customer communication, and improved job satisfaction.

Solution architecture

2 products mentioned
  • Dynamics 365 Field Service
  • Microsoft Copilot for Dynamics 365

Evidence from the source

Service technicians from the Siemens Smart Infrastructure Buildings Business Unit generate more than 1.4 million work-order reports for customers every year, which is why the company is harnessing generative AI to elevate and standardize report quality.
Siemens Smart Infrastructure used Microsoft Copilot for Dynamics 365 to develop and begin testing an application that helps its field technicians from the Buildings Business Unit generate clear, consistent work-order reports for customers.
Siemens expects to see high quality and more-standardized reports from its more than 10,000 field service technicians from the Buildings Business Unit, as well as streamlined workflows, enhanced customer communication, and improved job satisfaction.
These field service technicians generate more than 1.4 million work-order reports every year for customers. Siemens wanted to harness AI to elevate and standardize report quality to help customers stay informed and efficiently operate buildings, avoid downtime, and meet sustainability goals. It turned to longtime ally Microsoft to develop a generative AI–powered solution that would enhance the customer experience.
Transparency Evidence & methodology 45/100 reference value · 81% 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 value45/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth0/25
    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 scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence81%
  • 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
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity2/10
    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. Microsoft

    Siemens Smart Infrastructure customer story

    First-party
    https://www.microsoft.com/en/customers/story/19736-siemens-ag-germany-dynamics-365-field-service
    Source type
    Vendor-published customer story
    Published
    Dec 3, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Dec 3, 2024 · Microsoft · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.