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Capgemini Logistics United States

Penske Transportation Solutions

Application Refactoring

Penske needed to help its software engineering teams deliver quality code faster, reduce repetitive work, and modernize older applications without overloading lead developers.

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10–12 percent developer productivity improvement with GitHub Copilot pilot
Reduced costs by thousands of dollars for new digital solutions
potential for 18 to 20 percent productivity gains

Reported impact

Business outcome

Penske improved developer productivity, delivered features earlier, reduced exploratory-stage costs, and created a broader rollout path for AI-assisted software engineering.

  • Cost Reduction
  • Productivity Improvement
  • Speed to Market

Solution

Penske Transportation Solutions used GitHub Copilot in a structured software-engineering pilot designed with Capgemini to improve delivery speed, code quality, and developer leverage. Capgemini helped define the pilot roadmap, measurement framework, and training program for onshore and offshore teams, then supported rollout across three custom software projects. Copilot was introduced as an AI assistant for writing code, reducing repetitive boilerplate and glue-code work, helping junior developers work more independently, and supporting the modernization of older code bases. Penske treated the effort as both a tooling and change-management program, so developers were trained to use Copilot as a productivity and learning aid rather than just an autocomplete feature. After early success, the company began rolling the tool out more broadly across software engineering, where teams continue to find new uses in refactoring, upgrades, and faster feature delivery.

Data flow

Developers use GitHub Copilot inside their software-engineering workflow to generate and refine code, reduce repetitive implementation work, and accelerate modernization and delivery across Penske’s custom applications.

Solution architecture

1 components · 1 layer
  1. Compute
    • GitHub Copilot Assists developers with code generation, boilerplate reduction, modernization tasks, and faster delivery across custom software projects.

Architecture clues

  • Pilot covered three custom software projects
  • The rollout is expanding beyond the initial pilot into wider software engineering use
  • Training and change management were treated as part of the implementation

Evidence from the source

"After using GitHub Copilot for three custom software projects, Penske saw a 10-12 percent increase in productivity"
"As Gen AI capabilities were added to GitHub Copilot, junior developers are now able to work more independently."
"Based on our experience with just software engineering, we are projecting the potential for 18 to 20 percent productivity gains"
"Working with Capgemini, Penske conducted a pilot with GitHub Copilot to provide software engineers with an AI assistant that helps them write better quality code faster and focus on higher value work."
Transparency Evidence & methodology 95/100 reference value · 93% confidence · 2 sources

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 value95/100
  • 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
    2 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence93%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 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 transparency10/10
    2 source references 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. Capgemini

    Penske Transportation Solutions boosts custom software engineering with AI - Capgemini India

    First-party
    https://www.capgemini.com/in-en/news/client-stories/penske-transportation-solutions-boosts-custom-software-engineering-with-ai/
    Source type
    Vendor-published customer story
    Published
    Nov 17, 2025
    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
  2. Penske

    Who We Are - Penske

    First-party
    https://penske.jobs/who-we-are/

    We’re a winning team of 43,000 associates who help move lives and businesses forward.

    Source type
    Official company website
    Supports
    • Company size

Date evidence

  • Published on source page Nov 17, 2025 · Capgemini · First-party
    Open date source
Not separately sourced Industry, Region. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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