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Microsoft Healthcare Japan

Kawasaki Heavy Industries

IT Operations Automation · Predictive Maintenance · System Migration

The challenge was not simply building more capable robots.

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Nurse walking distance cut from 20 km to 10 km Develop once, deploy & update across environments Building a platform for the next era of robotics For decades, industrial robotics has been defined by specialized engineering.
With Azure OpenAI in Foundry Models, we can make operational knowledge available right when it’s needed, which helps reduce the day-to-day burden on staff.” From platform strategy to operating model The shift to an operating model became visible when Kawasaki publicly introduced its Digital Platform strategy as part of its broader DX transformation and Group Vision 2030.
With Azure OpenAI in Foundry Models, we can make operational knowledge available right when it’s needed, which helps reduce the day-to-day burden on staff.” In one case, where robots reduce routine workload and allow staff to focus on patient care, service robots have cut the distance nurses walk from 20 kilometers a day to 10 kilometers.
With Kawasaki’s KARAKURI initiative, users without robotics expertise—including healthcare professionals—can build and operate robot applications using natural language instructions.

Reported impact

Business outcome

With Kawasaki’s KARAKURI initiative, users without robotics expertise—including healthcare professionals—can build and operate robot applications using natural language instructions.

  • Customer Experience
  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

As labor shortages and aging populations in Japan drive demand for robotics in healthcare and services, organizations need systems that support workers. The challenge was not simply building more capable robots. Building on Azure, Kawasaki is currently developing a robotics as a service platform that decouples software from hardware, unifying AI, data, and operations to enable scalable deployment of intelligent robotics systems and lay the foundation for physical AI. With Kawasaki’s KARAKURI initiative, users without robotics expertise—including healthcare professionals—can build and operate robot applications using natural language instructions.

Solution architecture

5 components · 4 layers
  1. Compute
    • Azure OpenAI Service Runs the generative AI experience that answers questions, summarizes content, or drafts outputs for users.
    • Azure Provides the secure, scalable cloud foundation for the solution.
  2. Storage
    • Azure Cosmos DB Stores globally distributed application data for digital experiences.
  3. Orchestration
    • Azure Kubernetes Service Runs and scales the containerized application platform in Azure.
  4. Uncategorized
    • IoT Operations Supports the core application workflow described in the customer story.

Architecture clues

  • Building on Azure, Kawasaki is currently developing a robotics as a service platform that decouples software from hardware, unifying AI, data, and operations to enable scalable deployment of intelligent robotics systems and lay the foundation for physical AI.
  • For Kawasaki, scaling meant more than expansion; it required a new operating model.
  • Jun Yamaguchi, Director, Kawasaki Physical AI Center at Kawasaki Heavy Industries, explains, “Azure Kubernetes Service gave us a consistent platform for deploying robotics applications across environments.
  • Kawasaki set out to develop a cloud-based robotics as a service platform capable of supporting repeatable, production-scale deployment of intelligent robotics systems.
  • Organizations were looking for new ways to automate repetitive work while enabling people to focus on higher-value tasks.
  • The model delivered powerful automation capabilities but made it difficult to scale innovation across industries and use cases.
  • The objective was clear: transform robotics from a series of engineering projects into a scalable business capability.
  • To achieve this, Kawasaki built its platform on Microsoft Azure , incorporating: Azure Kubernetes Service (AKS) Azure OpenAI in Foundry Models Azure Cosmos DB Azure IoT Operations Azure Digital Twins Together, these services provide the foundation for developing, deploying, operating, and continuously improving intelligent robotics applications.

Evidence from the source

Building on Azure, Kawasaki is currently developing a robotics as a service platform that decouples software from hardware, unifying AI, data, and operations to enable scalable deployment of intelligent robotics systems and lay the foundation for physical AI.
The challenge was not simply building more capable robots.
To achieve this, Kawasaki built its platform on Microsoft Azure , incorporating: Azure Kubernetes Service (AKS) Azure OpenAI in Foundry Models Azure Cosmos DB Azure IoT Operations Azure Digital Twins Together, these services provide the foundation for developing, deploying, operating, and continuously improving intelligent robotics applications.
With Kawasaki’s KARAKURI initiative, users without robotics expertise—including healthcare professionals—can build and operate robot applications using natural language instructions.
Transparency Evidence & methodology 78/100 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 value78/100
  • Problem clarity12/20
    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 specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 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
    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 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

    Kawasaki Heavy Industries customer story

    First-party
    https://www.microsoft.com/en/customers/story/26946-kawasaki-heavy-industries-azure-kubernetes-service
    Source type
    Vendor-published customer story
    Published
    Jul 24, 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 Jul 24, 2026 · 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.

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