Accenture and AWS Accelerate Data Transformation with Agentic AI
Accenture and AWS have launched three new agentic AI solutions—Data Discovery, Data Modernization, and Semantic Layer—now available on AWS Marketplace, directly targeting the data readiness gap that causes 30% of generative AI projects to fail after proof of concept. The offerings address a four-phase data transformation lifecycle—assess, capture, curate, and consume—leveraging over 16 years of partnership and 110,000 workloads migrated.
Agentic Data Discovery Automates Metadata Inventory with Conversational AI
Accenture Agentic Data Discovery scans metadata across databases, schemas, and ETL code to build an automated inventory of enterprise data assets. The solution features a conversational AI interface and end-to-end lineage tracking. In a customer deployment, it achieved approximately 50% rationalization and roughly 25% consolidation of the data landscape, providing a clear foundation for downstream AI initiatives.
Agentic Data Modernization Automates Code Migration to Amazon Redshift
Accenture Agentic Data Modernization automates the migration of legacy code from platforms such as Oracle and Teradata to Amazon Redshift, Athena, and S3 with Iceberg. The solution performs automated data quality profiling and testing via purpose-built agents running on Amazon Bedrock AgentCore. This reduces manual migration effort and accelerates the modernization timeline for enterprises moving to cloud-native architectures.
Agentic Semantic Layer Enables Natural Language Querying Without Centralization
Accenture Agentic Semantic Layer creates a governed abstraction layer using a knowledge graph, enabling natural language querying across distributed data sources without requiring centralization. A customer example reported the creation of over 300 data products, a 600% improvement in throughput, and 30-40% savings on data infrastructure costs. The solution provides a practical path for enterprises to make disparate legacy data usable for generative AI while maintaining governance and security perimeters.
The collaboration signals a mature, integrated approach to closing the gap between AI investment and real business impact. Rather than focusing on AI model capabilities, the solutions prioritize data readiness—addressing the fragmented legacy data estates that often derail generative AI deployments. With over 110,000 workloads already migrated between the two firms, the offering represents a significant step in operationalizing enterprise AI through agent-driven data transformation.
