What challenges did Workhuman face before AI implementation?

Legacy reporting created a bottleneck of one-time custom report requests. Customers lacked self-service analytics. Access control and tenant isolation were complex at scale.

What was the impact of the AI solution?

Custom reporting requests were reduced dramatically because customers could modify reports themselves. Time to roll out new analytics features fell from weeks to days. Customer self-service improved satisfaction and the platform scaled without proportional growth in development effort.

Rank#918of 3,449

Source

Scaled productionEvidence: Medium50/100

Workhuman built multi-tenant self-service reporting with Amazon QuickSight embedded analytics

Use case typeReporting automationUpdated Mar 16, 2026

Workhuman rebuilt its analytics delivery for a multi-tenant SaaS environment using Amazon QuickSight embedded dashboards, tenant namespaces, row-level security, and automated CI/CD to let customers create and customize their own reports while preserving data isolation. The company serves over seven million users across 180 countries and uses embedded analytics to support customer-specific reporting within its product.

Organization: Workhuman
Industry: Tech & Comms
Location: Ireland
Published: March 2026

Reported outcomes

−80%

development time for new analytics featuresTime & speed

Strategic outcomes

Customer experience & trustEnabled customer self-service reportingCost efficiencyReduced custom reporting bottlenecksSpeed & agilityAccelerated analytics feature deployment

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

Normalized claim

Development time for new analytics features: 80% decrease

AWS Machine Learning BlogMar 16, 2026Blog postInferred claimMedium evidence strength

What previously took weeks of custom development can now be rolled out across the customer base in days.

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

Customer: Workhuman
Provider: AWS
Maturity: Scaled Production
Linked source: AWS Machine Learning Blog

Access control and tenant isolation were complex at scale

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 2 of 2

1Reporting automation
2Embedded analytics

Legacy reporting created a bottleneck of one-time custom report requests.
Customers lacked self-service analytics.
Access control and tenant isolation were complex at scale.

Workhuman implemented embedded Amazon QuickSight dashboards in its product.
It used dedicated namespaces per tenant, master templates, customer-specific analyses, row-level security, and secure time-limited embedding URLs.
The company automated analytics asset deployment across development, staging, and production using APIs, Terraform, and CI/CD pipelines.

Technologies

Amazon QuickSight Amazon Aurora PostgreSQL Amazon Redshift AWS Lambda AWS Batch GitLab

Custom reporting requests were reduced dramatically because customers could modify reports themselves.
Time to roll out new analytics features fell from weeks to days.
Customer self-service improved satisfaction and the platform scaled without proportional growth in development effort.

Architecture

Workhuman used Amazon QuickSight Enterprise Edition embedded dashboards inside its SaaS application. The design used a default internal namespace plus dedicated namespaces per tenant for isolation, master analysis templates, customer-specific analyses and datasets, row-level security based on roles and attributes, secure time-limited embedding URLs, and an automated CI/CD pipeline with APIs, Terraform, AWS Batch, AWS Lambda, Amazon Aurora PostgreSQL, and Amazon Redshift to provision and refresh analytics assets across development, staging, and production.

Sources & evidence1

Evidence: Medium50/100Evidence strength