Kone uses Amazon Bedrock to build a Technician Assistant for field service
Use case typeStaff assistantUpdated Jun 13, 2026
KONE, a global leader in elevator and escalator solutions, uses AWS generative AI to speed up elevator and escalator maintenance. The company built a Technician Assistant to help field technicians resolve equipment issues more efficiently and improve service delivery.
- Organization
- KONE
- Industry
- Manufacturing
- Location
- Finland
- Published
- April 2026
Reported outcomes
Strategic outcomes
New product / capabilityBuilt a generative AI technician assistantSpeed & agilityFaster maintenance issue resolutionEmployee experienceReduced technician support burdenScale & capacitySupported service efficiency at global scale
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- KONE
- Provider
- AWS
- Maturity
- Unknown
- Linked source
- AWS
No explicit deployment-stage evidence found.
Customer identity supportedSource describes one deploymentMaturity evidence evaluated
Primary read
Use case focus
Showing 3 of 3
- 1Field service assistant
- 2Technician productivity
- 3Maintenance support
- KONE needed to speed resolution of maintenance issues for elevator and escalator field service operations.
- The company also needed to reduce the burden on technicians and the technical help desk, which handles about 30,000 calls per month.
- KONE implemented a generative AI Technician Assistant on AWS using Amazon Bedrock.
- The solution accesses Claude 3 through Amazon Bedrock to support technicians during maintenance and troubleshooting workflows.
- KONE also uses AWS IoT technologies in its broader connected operations and service environment.
Technologies
- The assistant helps over 40,000 field technicians per day resolve equipment issues more efficiently.
- KONE reports faster resolution and improved technician productivity.
- The solution supports service efficiency at global scale across major field-service volumes.
Sources & evidence1
Evidence: Low40/100Evidence strength
- Customer explicitly identified
- Primary source available
- Technical implementation details available
Type: Case StudyPublished: Apr 29, 2026Publisher: AWSEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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