Accenture and AWS accelerate data transformation with agentic AI

By: Mark Stanger, Lead Architect, Semantic Layer – Accenture
By: Aditi Pendse, Lead Data Architect, Agentic Data Platforms & Cloud Modernization – Accenture
By: Sandeep Chatterjee, Product Lead, Agentic Data Discovery – Accenture
By: Rajdeep Banerjee, Senior Partner Solutions Architect – AWS

Accenture

Enterprises that treat data as a strategic asset will define the next era of competitive advantage. The opportunity is significant: agentic AI can autonomously discover, migrate, govern, and consume data at a pace and scale that was impossible only 2 years ago. Accenture and Amazon Web Services (AWS) are accelerating enterprise data transformation with three agentic AI solutions now available in AWS Marketplace, purpose-built to make enterprise data ready for generative AI and agentic applications.

Organizations are actively working to scale generative AI solutions. The Accenture Pulse of Change finds that nearly nine in ten organizations plan to increase AI investment in 2026, and most view it as a driver of revenue growth. Yet only one in five report redesigning end-to-end processes with AI at the core. Gartner projected that at least 30% of generative AI projects will be abandoned after proof of concept by end of 2025. This gap can be put down to data readiness rather than AI capability. Data remains fragmented across platforms, poorly documented, and locked in legacy formats that AI models can’t effectively use.

Accenture Agentic Data Discovery for AWS, Accenture Agentic Data Modernization for AWS, and Accenture Agentic Semantic Layer for AWS address this gap directly. Together, they transform how organizations discover, migrate, govern, and consume enterprise data, closing the readiness gap that stands between AI ambition and AI impact.

Agentic solutions across the data lifecycle

The three solutions map to four phases of data transformation: assess and discover, capture data for AI, curate data for AI, and consume data with AI. Organizations don’t need to follow these phases sequentially. You can start wherever you are and run multiple workstreams simultaneously, whether that means discovering current data estates, migrating legacy platforms, or building a semantic layer for AI consumption. The solutions are complementary but independent. Accenture Agentic Data Discovery identifies and prioritizes data assets. Accenture Agentic Data Modernization migrates and transforms them. Accenture Agentic Semantic Layer federates access to data wherever it resides, across AWS and other platforms, making it queryable by AI agents and applications without requiring data centralization.

Accenture Agentic Data Discovery for AWS

Most organizations don’t fully understand their own data landscape. Reports are undocumented, lineage is unknown, and shadow IT creates blind spots that block AI initiatives before they start.Accenture Agentic Data Discovery deploys intelligent agents that scan metadata across databases, schemas, and extract, transform, and load (ETL) code to build a comprehensive, continuously updated inventory of your data estate. The agents classify data assets as active or inactive, enabling prioritization for migration and modernization efforts. The solution delivers automated discovery across your entire data landscape, uncovering hidden patterns and delivering inventory, volumetrics, and object distribution insights. Interactive visualizations provide comprehensive views of your data estate, including sensitive data detection for personally identifiable information (PII) and protected health information (PHI). A conversational AI interface means data stewards and architects can query the data landscape in plain English, no SQL required, with agents analyzing and responding in real time. An end-to-end lineage viewer traces data flows across on-premises and cloud-based systems, with progressive graph expansion showing upstream and downstream dependencies.An example Accenture customer using Data Discovery achieved approximately 50% inventory rationalization based on inactive and one-time inventory identification. They consolidated approximately 25% of their inventory through detailed similarity analysis across data, code, and business intelligence assets. And they performed comprehensive technical analysis of over 1,400 reports with end-to-end lineage to support modernization planning.

Accenture Agentic Data Modernization for AWS

Migrating legacy data platforms to AWS is a resource-intensive phase of cloud transformation. Organizations face months of manual code conversion, data mapping, and testing, with quality and governance often addressed as afterthoughts.

Accenture Agentic Data Modernization deploys purpose-built agents that automate three workstreams. For code migration, Data Engineer agents handle the full lifecycle of legacy code transformation, from ingesting source ETL scripts, stored procedures, and SQL dialects to chunking, reviewing, converting, and optimizing code for target platforms. Supported conversions include Oracle, Teradata, Db2, Informatica PowerCenter, and DataStage to Amazon Redshift, Amazon Athena, Amazon Simple Storage Service (Amazon S3) with Apache Iceberg, Snowflake, PySpark, and others. For data quality and governance, Data Quality Engineer agents discover and profile source data, define quality rules, validate data against those rules, and generate verification scripts. For automated testing, Data Testing agents translate business requirements into test cases, generate test scripts, run them against target environments, and validate results, eliminating the manual testing bottleneck that typically extends migration timelines.

Agents are built on Amazon Bedrock AgentCore, enabling accelerated deployment within your AWS environment. By applying Agentic Data Modernization, organizations are modernizing legacy platforms, improving confidence in migrated data through automated testing and quality agents, and unlocking scalable data marketplaces backed by AWS led co-innovation.

Accenture Agentic Semantic Layer for AWS

As organizations modernize their data estates on AWS, a new challenge emerges how to make that data truly consumable for analytics, generative AI, and agentic applications without introducing new silos, duplication, or governance risk. The Agentic Semantic Layer for AWS addresses this challenge by creating a governed, AI-ready abstraction that sits above enterprise data, transforming raw, distributed datasets into business‑meaningful knowledge that can be safely and easily consumed.The solution accelerates decision-making by translating complex, distributed data into trusted, business-ready insights. Business users can query data in natural language and receive consistent, governed answers with minimal technical team involvement, while AI teams benefit from reusable, standardized metrics and semantics. This enables high-value use cases such as executive self-service analytics, domain-specific copilots, autonomous reporting, and AI-driven decision support across structured and unstructured data.At its core, the Agentic Semantic Layer establishes a shared semantic foundation that bridges technical data structures and business intent. Using AI-assisted ontology creation, it captures enterprise concepts, relationships, and metrics in a formalized model that reflects how the business thinks about its data. This semantic model becomes the backbone for analytics, natural language querying, and agentic workflows, providing all consumers with a consistent and trusted understanding of enterprise information.The solution is designed around a “data in place” philosophy. Rather than forcing organizations to centralize or duplicate data, it federates access across sources wherever they reside, whether in Amazon Redshift, Amazon S3, Amazon Athena, operational databases, or third-party platforms such as Snowflake. A knowledge graph engine continuously enriches the semantic layer with metadata, lineage, and contextual relationships. As new sources are onboarded, the layer adapts automatically, preserving alignment between physical data assets and business semantics and creating a living knowledge fabric that supports both traditional business intelligence (BI) and advanced agentic AI scenarios.Governance and trust are built in by design. Enterprise access controls, data classification, and policy enforcement are integrated directly into semantic resolution, which means users and AI agents only access what they’re authorized to see. Organizations can confidently expose data through natural language interfaces and semantic APIs while maintaining regulatory compliance and data sovereignty.An example Accenture client using the Agentic Semantic Layer deployed over 300 data products across 25 domains, achieved a 600% throughput improvement for data sourcing teams, and realized 30–40% data infrastructure savings through smart data productization while building readiness for future agentic AI workloads.

Conclusion

The future of enterprise data management is agentic, intelligent, and automated. Organizations that adopt these capabilities can gain competitive advantages in an AI-driven marketplace.AWS and Accenture invite enterprise leaders to explore how these agentic data solutions can accelerate your data transformation journey. Whether you’re beginning to assess your data landscape, planning a major migration, or building the semantic infrastructure for AI applications, these solutions provide the capabilities, governance, and scale that enterprise success requires.

Connect with the Accenture AWS Business Group to learn how we can help you transform your data estate into a strategic asset that powers innovation, drives insights, and enables the agentic AI applications of tomorrow.

The Accenture and AWS Partnership advantage

The collaboration between Accenture and AWS brings together complementary strengths that uniquely position these solutions for enterprise success. AWS provides comprehensive cloud infrastructure capabilities, with AI services through Amazon Bedrock and robust data infrastructure. Accenture contributes more than 16 years of partnership experience, having migrated over 110,000 workloads to AWS and delivered more than 4,100 joint projects across global clients.

This partnership has been recognized with multiple 2025 AWS awards, including Global Consulting Partner of the Year and Global GenAI Partner of the Year. More importantly, it combines the technical innovation of AWS with Accenture’s deep understanding of enterprise transformation challenges, regulatory requirements, and industry-specific needs.

Accenture – AWS Partner spotlight

Accenture is an AWS Premier Tier Services Partner and managed service provider (MSP) that provides end-to-end solutions to migrate to and manage operations on Amazon Web Services (AWS). By working with the Accenture AWS Business Group (AABG), a strategic collaboration by Accenture and AWS, organizations can accelerate the pace of innovation to deliver disruptive products and services.
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