Microsoft Logistics United States

C.H. Robinson

Document Processing · Supply Chain Optimization

In an increasingly complex supply chain environment, C.H. Robinson—one of the world’s largest logistics platforms—relies on technology to accelerate the seamless delivery of goods that help drive the world’s economy. However, the company faced a challenge unique to its scale: harnessing technology to help employees manage the tens of thousands of requests it receives from shippers via email daily.

Concludes Albrecht, “Incorporating Azure AI in our tech has helped put us on pace to achieve another 15% increase in productivity this year, creating happier customers, from one-truck shippers to some of the largest comp
Incorporating Azure AI in our tech has helped put us on pace to achieve another 15% increase in productivity this year, creating happier customers, from one-truck shippers to some of the largest companies in the world.
Since going live this year, we’ve reduced the time required to provide email price quotes from hours to an average of 32 seconds per quote,” says Meghan Hughes, a Key Account Manager at C.H.
The solutions have been implemented over a 12-month period.

Reported impact

Business outcome

Concludes Albrecht, “Incorporating Azure AI in our tech has helped put us on pace to achieve another 15% increase in productivity this year, creating happier customers, from one-truck shippers to some of the largest companies in the world.

  • Compliance
  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

C.H. Robinson built this solution on Azure AI, Azure SQL Database, Azure Cosmos DB. In an increasingly complex supply chain environment, C.H. Robinson—one of the world’s largest logistics platforms—relies on technology to accelerate the seamless delivery of goods that help drive the world’s economy. Azure AI provides managed AI services used to automate classification, recommendations, or decision support. Azure SQL Database stores transactional and reporting data that the application depends on. Azure Cosmos DB stores operational data that needs globally distributed, low-latency access. The logistics company used Microsoft Azure AI Foundry and Azure OpenAI in Foundry Models to build generative AI tools that automate emailed customer requests, improving employee productivity and customer service. Incorporating Azure AI into C.H. The rollout also connects with knowledge base so users can work inside existing operating processes.

Solution architecture

3 components · 2 layers
  1. Compute
    • Azure AI Provides managed AI services used to automate classification, recommendations, or decision support.
  2. Storage
    • Azure SQL Database Stores transactional and reporting data that the application depends on.
    • Azure Cosmos DB Stores operational data that needs globally distributed, low-latency access.

Architecture clues

  • The implementation relies on Azure AI as a core technical building block.

Evidence from the source

Concludes Albrecht, “Incorporating Azure AI in our tech has helped put us on pace to achieve another 15% increase in productivity this year, creating happier customers, from one-truck shippers to some of the largest comp
In an increasingly complex supply chain environment, C.H. Robinson—one of the world’s largest logistics platforms—relies on technology to accelerate the seamless delivery of goods that help drive the world’s economy.
Incorporating Azure AI in our tech has helped put us on pace to achieve another 15% increase in productivity this year, creating happier customers, from one-truck shippers to some of the largest companies in the world.
The logistics company used Microsoft Azure AI Foundry and Azure OpenAI in Foundry Models to build generative AI tools that automate emailed customer requests, improving employee productivity and customer service.
The solutions have been implemented over a 12-month period.
Transparency Evidence & methodology 95/100 reference value · 87% 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 value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    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
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • 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 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. Microsoft

    C.H. Robinson customer story

    First-party
    https://www.microsoft.com/en/customers/story/19575-ch-robinson-azure-ai-studio
    Source type
    Vendor-published customer story
    Published
    Nov 11, 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 Nov 11, 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.