In-house Manufacturing Global Company newsroom

Airbus

Document Processing · Enterprise Knowledge Search

Airbus needs faster, easier access to complex technical and manufacturing documentation across engineering, manufacturing, after-sales service, and other operations while meeting the aerospace sector's regulatory and safety constraints.

600 use cases have been identified across Airbus in less than a year

Reported impact

Business outcome

Airbus reports that GenAI is helping employees manage complex and technical documents more effectively, with experiments aimed at faster answers for after-sales service teams and quicker retrieval of manufacturing instructions, while the broader programme targets efficiency, savings, and quality improvements.

  • Customer Experience
  • Productivity Improvement

Solution

Airbus is rolling out an internal generative AI programme to make technical knowledge easier to use across the company. A company-wide working group launched in 2023 to bring generative AI, large language models, and base models into Airbus under defined policies and safeguards. One concrete deployment is an AI assistant for Standard Operating Instructions, the manufacturing instructions used to build aircraft. The assistant works as a chatbot that lets factory staff query technical data in natural language instead of manually paging through documents on a tablet. Airbus is also running an after-sales service experiment to help support teams answer questions faster. In this implementation, internal document collections and technical instructions are the source material, large language models perform question understanding and answer generation, and chatbot-style interfaces serve the results back to employees in production and support workflows.

Data flow

Airbus feeds manufacturing instructions and other technical documentation into internal GenAI workflows, where large language models and base models interpret natural-language questions. The chatbot-based assistant returns targeted answers, such as the correct tool or procedure, to manufacturing staff and other employees through operational interfaces such as tablets.

Solution architecture

4 components · 2 layers
  1. Compute
    • large language models Interpret employee questions and generate responses from Airbus technical documentation.
    • base models Provide the foundational models Airbus is bringing into the company under controlled governance for GenAI use cases.
  2. Serving
    • AI assistant for Standard Operating Instructions Provides natural-language access to manufacturing instructions and related technical data for aircraft assembly workers.
    • chatbot Serves the user interface employees use to query documentation and retrieve answers quickly.

Architecture clues

  • Airbus highlights regulatory constraints specific to the aerospace industry
  • Company-wide GenAI working group launched in 2023
  • Deployment is governed with policies and safeguards for responsible use
  • Manufacturing users can query documentation in natural language instead of navigating many pages on a tablet

Evidence from the source

"600 use cases have been identified across Airbus in less than a year."
"Another full-scale experiment involves testing an AI assistant for Standard Operating Instructions, the manufacturing instructions used to build the aircraft."
"One experiment, for example, aims to demonstrate how technology can help after-sales service teams to provide answers more quickly."
"The chatbot facilitates access to technical data."
"This group aims to bring the power of generative AI, large language models and base models to Airbus, while developing the policies and safeguards to do so responsibly"
"it enables us to check in an instant, using natural language, which torque spanner to use for a specific operation"
Transparency Evidence & methodology 75/100 reference value · 92% 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 confidence92%
  • Source authority25/25
    The deployment is described by the adopting organization or a government source.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 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 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. Airbus

    Airbus customer story

    First-party
    https://www.airbus.com/en/newsroom/stories/2024-05-how-airbus-uses-generative-artificial-intelligence-to-reinvent-itself
    Source type
    Customer-published story
    Published
    May 28, 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 May 28, 2024 · Airbus · 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.