What challenges did CSL face before AI implementation?

Complex, technically debt-heavy on-premises data center and VMware environment across 29 data centers. Inefficient wave planning and application discovery that made migration timelines hard to meet. Fragmented documentation and complex dependencies slowed cloud transformation efforts.

What was the impact of the AI solution?

Accelerated initial wave planning by 10x. Saved a minimum of 10.5 weeks of effort for 1,072 applications. Reduced application discovery time to about 5 minutes from 1 to 2 hours per application. On track to exit 17 data centers in about 30 months. Reported about 30% operational cost savings from licensing, hardware, and datacenter-related cost avoidance.

Source

ProductionEvidence: Medium65/100

CSL reinvents technology modernization with agentic AI using Amazon Q Business and AWS Transform for VMware

Use case typeCode modernizationUpdated Jun 13, 2026

CSL is a global biotechnology company modernizing its business-critical operations by exiting legacy on-premises infrastructure and moving to the cloud. The company faced technical debt across 29 data centers, with 5,000 VMware servers and more than 1,000 applications, plus fragmented documentation and complex dependencies that slowed migration planning. CSL used Amazon Q Business to index more than 17,000 pages of infrastructure requirements and AWS Transform for VMware to analyze VMware environments, map dependencies, and generate migration wave plans. The company is also using AWS for RISE with SAP as part of its broader modernization program.

Organization: CSL
Industry: Healthcare
Location: Australia
Published: May 2026

Reported outcomes

1-2 minutes

timeTime & speed

10xquantified impact1-2 hourstime−30%cost

Strategic outcomes

Speed & agilityAccelerated migration wave planningNew product / capabilityAutomated dependency-aware migration planningBetter decisions & insightImproved application discovery and documentation accessCost efficiencyReduced operational costs from modernization

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Quantified impact: 10 x

AWS case studyMay 27, 2026Customer storyInferred claimMedium evidence strength

Accelerated initial wave planning by 10x.

Normalized claim

Time: 1-2 minutes decrease

AWS case studyMay 27, 2026Customer storyInferred claimMedium evidence strength

Reduced application discovery time to about 5 minutes from 1 to 2 hours per application.

Normalized claim

Time: 1-2 hours decrease

AWS case studyMay 27, 2026Customer storyInferred claimMedium evidence strength

Reduced application discovery time to about 5 minutes from 1 to 2 hours per application.

Normalized claim

Cost: 30% decrease

AWS case studyMay 27, 2026Customer storyInferred claimMedium evidence strength

Reported about 30% operational cost savings from licensing, hardware, and datacenter-related cost avoidance.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: CSL
Provider: AWS
Maturity: Production
Linked source: AWS case study

Reported about 30% operational cost savings from licensing, hardware, and datacenter-related cost avoidance

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1Agentic AI
2Cloud Migration
3IT Modernization

Complex, technically debt-heavy on-premises data center and VMware environment across 29 data centers.
Inefficient wave planning and application discovery that made migration timelines hard to meet.
Fragmented documentation and complex dependencies slowed cloud transformation efforts.

CSL and AWS Professional Services used AWS Transform for VMware to automate wave planning and server mapping.
AWS Transform for VMware used specialized agents to analyze and map VMware environments and orchestrate dependency-aware migration waves.
CSL used Amazon Q Business to index infrastructure requirement documents spanning more than 1,000 applications and 17,000 pages.
The company also adopted AWS for RISE with SAP as part of its technology modernization program.

Technologies

Amazon Q Business AWS Transform for VMware AWS for RISE with SAP

Accelerated initial wave planning by 10x.
Saved a minimum of 10.5 weeks of effort for 1,072 applications.
Reduced application discovery time to about 5 minutes from 1 to 2 hours per application.
On track to exit 17 data centers in about 30 months.
Reported about 30% operational cost savings from licensing, hardware, and datacenter-related cost avoidance.

Architecture

CSL and AWS Professional Services used AWS Transform for VMware specialized agents to analyze and map complex VMware environments, convert network configurations into AWS built-in constructs, and orchestrate dependency-aware migration waves. Amazon Q Business indexed infrastructure requirement documents to support application discovery and migration documentation. AWS for RISE with SAP was also part of the broader modernization program.

Sources & evidence1

Evidence: Medium65/100Evidence strength