Capgemini Public Sector Germany

Federal Ministry of Digitalization and State Modernization (BMDS)

Document Processing · IT Operations Automation

BMDS needed to reduce long, complex planning and approval procedures for infrastructure projects that routinely exceeded legal timelines and burdened case workers with documentation-heavy manual workflows.

more than six months beyond the legally defined limit
shortens procedures that currently take months to days

Reported impact

Business outcome

BMDS gained a reusable AI foundation that accelerates approval procedures, reduces administrative burden, and preserves transparency and legal accountability while preparing the solution for reuse across German public administration.

  • Productivity Improvement
  • Scalability

Solution

BMDS launched its SPARK initiative with Capgemini and six European technology partners to build a modular AI-enabled approval platform for complex infrastructure permitting. The program began with user research into how case workers handle planning and approval files, then moved through prototype development, integrated testing, and phased rollout. The resulting system supports the full process from document preparation and formal review through substantive review, objection review, and AI-assisted drafting of administrative outputs. Capgemini designed the modules to be configurable, reusable, and connected through standard interfaces so they can fit existing public-administration platforms with minimal disruption. Validation and explainability mechanisms were embedded throughout the workflow so legal robustness, user feedback, and technical evolution could progress together, creating a trustworthy foundation for future administrative AI use cases.

Data flow

Planning and approval files move through document preparation, formal review, substantive review, objection review, and AI-assisted drafting, with configurable modules handing work across each step and feeding user validation back into the process.

Solution architecture

0 products mentioned

Architecture clues

  • Prototype-first implementation followed by integrated testing and phased rollout
  • Standard interfaces were used to fit existing administrative platforms
  • The solution is being prepared for open-source reuse across public administration
  • Validation and explainability were embedded throughout the workflow

Evidence from the source

"BMDS aimed to address lengthy and complex planning and approval procedures for critical infrastructure projects, which were often delayed by more than six months beyond the legally defined limit."
"Following the solution’s rollout, the end-to-end system now supports administrative staff throughout the entire approval process, from document preparation and formal review to substantive review, objection review, and AI-assisted drafting of administrative outputs."
"Our technical solution uses so-called agentic AI and shortens procedures that currently take months to days."
Transparency Evidence & methodology 65/100 reference value · 80% 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 value65/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth15/25
    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 confidence80%
  • 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 evidence15/20
    3 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency10/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity4/10
    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. Capgemini

    BMDS fast tracks major infrastructure projects across Germany with open source and agent-based AI - Capgemini India

    First-party
    https://www.capgemini.com/in-en/news/client-stories/bmds-fast-tracks-major-infrastructure-projects-across-germany-with-open-source-and-agent-based-ai/
    Source type
    Vendor-published customer story
    Published
    Jun 9, 2026
    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 Jun 9, 2026 · Capgemini · 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.