AWS re:Invent 2025 - Inside Alight’s AI-driven Expansion to Cloudera on AWS (AIM221)

Summary of AWS re:Invent 2025 - Inside Alight's AI-driven Expansion to Cloudera on AWS (AIM221)

Alight's Journey to the Cloud and Cloudera Data Lakehouse

• Alight is a leading human capital cloud-based technology company that manages benefits, wealth, leaves, and payroll data for thousands of clients and millions of employees.
• Alight faced challenges with their on-premises data infrastructure, including scalability issues, SLA delays, technical debt, and poor user experience.
• To address these challenges, Alight decided to migrate to Cloudera Data Platform (CDP) on AWS, leveraging features like Data Hub, Data Lake, and the Lakehouse architecture.

Alight's Migration Approach and Execution

• Alight took a phased approach to the migration, evaluating CDP capabilities through a 4-week POC and planning the migration in 9 waves over 6 months.
• Key aspects of their approach included:
  • Onboarding stakeholders and communicating planned outages to clients
  • Handling legacy workloads by rehosting, retiring, or rewriting applications
  • Extensive automation to manage the 6-month migration window
  • Collaboration with Cloudera and AWS to ensure a successful transition

Migration Results and Benefits

• Alight successfully migrated over 8,000 workloads and 124 applications to CDP on AWS within the 6-month window.
• The migration resulted in:
  • Achieving all SLAs and improving scalability during peak periods
  • Reducing cloud and professional services costs below the $3 million target
  • Enabling a 100% user adoption of the new Cloudera-based reporting

Leveraging Cloudera Lakehouse for AI and Data Optimization

• Alight is leveraging Cloudera's Lakehouse architecture to accelerate their adoption of generative AI and AI capabilities.
• Key Lakehouse features they are utilizing include:
  • Apache Iceberg for faster data consumption and query performance on large datasets in S3
  • Lakehouse Optimizer for automated table management, compaction, and metadata optimization
• These Lakehouse capabilities have resulted in:
  • 40-70% reduction in object counts
  • 48% reduction in overall data size
  • 60-80% reduction in manifest files
  • Improved table access performance and user experience

The Future of AI and Agentic Workflows at Alight

• Alight is expanding their use of generative AI and AI-driven capabilities across various business areas, including:
  • Enhancing search-based processes
  • Consolidating reporting with advanced authoring capabilities
  • Automating employee enrollment and benefit selection
  • Improving contact center efficiency and claims adjudication
• Alight sees significant opportunities to leverage AI and agentic workflows to personalize employee experiences, automate manual tasks, and drive greater efficiency across their operations.

Key Takeaways

• Alight's successful migration to Cloudera Data Lakehouse on AWS enabled them to address scalability, SLA, and technical debt challenges, leading to improved user experience and cost optimization.
• The Lakehouse architecture, with features like Iceberg and Lakehouse Optimizer, has helped Alight optimize their data management and improve performance for AI and analytics workloads.
• Alight is at the forefront of leveraging generative AI and agentic workflows to enhance employee experiences, automate processes, and drive greater efficiency across their business.
• Integrating data, AI, and automation technologies in a well-architected, governed, and extensible platform is crucial for enterprises to unlock the full potential of AI and agentic innovations.