What challenges did New Zealand Rugby face before AI implementation?

Performance data was spread across fragmented systems. Insights could take days to surface, limiting in-the-moment decision-making. Workflows were largely manual and there was no centralized view of player histories.

What was the impact of the AI solution?

More than 150 teams surface data faster using Amazon Bedrock, eliminating delays that previously lasted days. The platform surfaces blind spots and patterns that may otherwise go unnoticed. Faster access to insights has reduced the time spent on analysis. Centralizing player data has improved continuity across teams and reduced repeated data collection.

Rank#1,093of 3,107

Source

New Zealand Rugby transforms analysis with Amazon Bedrock

Use case typeReal-time analyticsUpdated Jun 18, 2026

New Zealand Rugby (NZR) consolidated fragmented performance data into a unified, cloud-based data platform on AWS. The new platform gives high-performance staff near-real-time access to more than 1,000 data points per player per game and uses natural-language capabilities on Amazon Bedrock to surface insights for coaches and analysts. The solution helps staff identify patterns and blind spots more quickly, supports in-game adjustments, and reduces manual analysis effort.

Organization: New Zealand Rugby
Industry: Other
Location: New Zealand
Published: June 2026

Reported outcomes

150 teams

teams surfacing data fasterTime & speed

−100%insight delay reduction

Strategic outcomes

Better decisions & insightNear-real-time performance insightsScale & capacityUnified data access across 150+ teamsBetter decisions & insightMore informed performance decisionsCost efficiencyReduced repeated data collection

Primary read

Use case focus

Showing 2 of 2

1Real-time analytics
2Data platform modernization

Performance data was spread across fragmented systems.
Insights could take days to surface, limiting in-the-moment decision-making.
Workflows were largely manual and there was no centralized view of player histories.

NZR worked with AWS to consolidate multiple niche sports analytics systems into a unified player data platform.
AWS Professional Services helped design and build a scalable data foundation.
Amazon Redshift was used as a centralized data warehouse to unify performance data, while Amazon SageMaker was used to develop and run machine-learning models that generate insights for coaches and analysts.
Amazon Bedrock enabled natural-language analysis across multiple data sources so staff could identify relevant datasets and surface insights without manual analysis.
The architecture created an athlete-centric data ecosystem with continuity across club and international environments.

Technologies

Amazon Bedrock Amazon SageMaker Amazon Redshift Redshift Serverless

More than 150 teams surface data faster using Amazon Bedrock, eliminating delays that previously lasted days.
The platform surfaces blind spots and patterns that may otherwise go unnoticed.
Faster access to insights has reduced the time spent on analysis.
Centralizing player data has improved continuity across teams and reduced repeated data collection.

Architecture

AWS Professional Services collaborated with NZR to build a scalable data foundation. Amazon Redshift serves as the centralized data warehouse, Amazon SageMaker runs machine-learning models, and Amazon Bedrock provides natural-language, agentic AI capabilities on top of centralized performance data.

Implementation partners1

AWS Professional Services

Sources & evidence1

Groundedness: 4/5Type: Customer StoryPublished: Jun 18, 2026Publisher: AWSEvidence: PrimaryConfidence: High

Primary source

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