Data Modernization with Agentic AI

A leading global financial services organisation with over $500 billion in assets under management faced critical challenges in modernising its fragmented data ecosystem spanning 15 countries and 60+ legacy systems. Traditional ETL processes were causing 3-week delays in regulatory reporting, while manual data governance exposed the firm to compliance risks, resulting in $2.5M in regulatory penalties in the previous year. Due to the technical complexity of data access, business stakeholders waited up to 10 days for insights. 

In partnership with our team, the firm implemented Data Modernization with Agentic AI, a revolutionary AWS-powered solution that deployed autonomous agents across their entire data lifecycle. The 12-week implementation transformed their rigid data infrastructure into an intelligent, self-optimising ecosystem. The solution delivered immediate and substantial results: regulatory reporting time reduced by 75%, data-related compliance incidents eliminated, and business insights delivered 70% faster—all while lowering total data management costs by 63%. The firm's Chief Data Officer stated, "This solution transformed our data from a compliance burden into a strategic asset driving competitive advantage." 

Customer Overview 

Company Profile 

• Customer: Confidential 

• Industry: Financial Services

• Location: Italy 

• Company Size: 11-50 

Business Challenges 

The financial institution faced multiple critical business challenges related to its data: 

• Regulatory Compliance Risks: The firm incurred $2.5M in penalties over the previous year due to incomplete or delayed regulatory reporting, with manual processes unable to keep pace with evolving requirements 

• Insight Delivery Delays: Business teams waited an average of 10 days for requested analytics, with 65% of strategic decisions delayed due to data accessibility issues 

• Cross-Domain Friction: Siloed data structures prevented integrated customer views across business lines, limiting cross-selling and risk assessment capabilities 

• Innovation Bottlenecks: New product development cycles extended 40% beyond targets due to data integration challenges and access limitations 

• Resource Misallocation: The firm maintained a team of 85 data engineers primarily focused on maintenance rather than strategic initiatives, with 72% of work hours dedicated to pipeline fixes and manual data reconciliation 

• Competitive Pressure: Emerging fintech competitors leveraging modern data platforms were capturing market share with more personalised offerings and faster onboarding experiences 

Technical Challenges 

The technical landscape presented formidable obstacles to data modernization: 

• Legacy System Fragmentation: 60+ disparate systems accumulated through acquisitions and organic growth, with incompatible data formats and minimal documentation 

• Manual ETL Processes: Over 1,200 hand-coded ETL pipelines requiring constant maintenance, with an average of 350 failures per month requiring human intervention 

• Schema Volatility: Upstream systems frequently change data formats without notification, breaking downstream processes and causing reporting failures 

• Governance Limitations: Manual data classification and policy enforcement covering only 38% of sensitive data assets, creating significant compliance exposure 

• Technical Debt: Historical point-to-point integrations creating a complex web of dependencies that resisted modernization efforts 

• Performance Issues: Batch processing limitations causing analytics queries to exceed SLAs by 300% during month-end peaks 

• Skills Gap: Internal teams lacking expertise in modern data architectures and cloud-native technologies while being fully consumed with maintenance activities 

Partner Solution 

Solution Overview 

The Data Modernization with Agentic AI solution implemented a transformative approach to data management through a network of autonomous, specialized agents powered by AWS's advanced AI services. Unlike traditional data modernization projects focused on static infrastructure, this solution created an intelligent ecosystem of agents that: 

1. Autonomously Discovered and Integrated data from all 60+ source systems without manual schema mapping or ETL coding 

2. Transformed Data Intelligently based on business context and regulatory requirements, adapting automatically to source changes 

3. Generated Contextual Insights aligned with specific business domains and user roles across all business lines 

4. Enforced Adaptive Governance based on evolving regulatory requirements with comprehensive audit trails 

5. Provided Natural Language Access to data insights for non-technical users through conversational interfaces 

The solution's unique Agent Coordinator orchestrated these activities across the entire data lifecycle, continuously optimising workflows and resource utilisation based on business priorities and usage patterns. 

AWS Services Utilised 

The solution leveraged a comprehensive suite of AWS services: 

• Amazon Bedrock: Foundation models powering agent intelligence and natural language understanding 

• AWS Lambda: Serverless compute for agent operations with dynamic scaling 

• Amazon S3: Data lake storage with intelligent tiering optimized by storage agents 

• AWS Glue: Serverless ETL capabilities orchestrated by transformation agents 

• Amazon Redshift: Data warehousing for high-performance analytics 

• Amazon QuickSight: Business intelligence visualization enhanced by context-aware analytics agents 

• Amazon Q: Natural language interface for conversational data interactions 

• AWS Step Functions: Orchestration of complex agent workflows with state management 

• Amazon EventBridge: Event-driven coordination between agents and systems 

• Amazon Dynamodb: High-performance metadata storage for agent knowledge bases 

• AWS IAM: Identity and access management integrated with governance agents 

• Amazon SageMaker: Custom ML model training for specialized analytics agents 

• Amazon CloudWatch: Continuous monitoring and observability across the agent ecosystem 

• AWS CodePipeline: Automated deployment and updates of agent capabilities 

Architecture Diagram 

High-level architecture diagram showing the five key layers of the implementation: 

Figure 1: Data Modernization with Agentic AI

• Cognitive Agent Layer: Specialised autonomous agents for different data functions 

• Serverless Compute Layer: AWS services providing dynamic infrastructure 

• Data Storage & Processing Layer: S3, Redshift, and processing services 

• Orchestration Layer: Agent Coordinator managing workflows 

• Governance & Security Layer: Policy enforcement and compliance controls 

Implementation Details 

The implementation followed a structured 12-week methodology: 

Weeks 1-2: Discovery & Planning 

• Conducted a comprehensive data landscape assessment identifying all data sources and consumers 

• Mapped business processes to data dependencies with priority scoring 

• Established success metrics and KPIs aligned with business objectives 

• Developed agent deployment strategy with security architecture 

Weeks 3-5: Foundation Implementation 

• Deployed core AWS infrastructure with security controls and IAM policies 

• Implemented Agent Coordinator with initial orchestration workflows 

• Established data lake architecture with intelligent tiering 

• Configured the integration framework for source system connectivity 

Weeks 6-8: Agent Deployment 

• Deployed Data Discovery Agents with initial crawling of high-priority sources 

• Implemented Policy Enforcement Agents with regulatory rule mapping 

• Activated Data Transformation Agents for core business entities 

• Established Analytics Agents for priority use cases 

Weeks 9-10: Cognitive Enhancement 

• Trained agents with business context and domain knowledge 

• Implemented reinforcement learning mechanisms for continuous improvement 

• Developed natural language interfaces for business user access 

• Created feedback loops for agent performance optimization 

Weeks 11-12: Validation & Transition 

• Conducted comprehensive compliance validation across all regulatory requirements 

• Performed load testing and performance optimization 

• Completed user acceptance testing with business stakeholders 

• Implemented operational monitoring and provided knowledge transfer 

Innovation and Best Practices 

The implementation introduced several innovative approaches that advanced the state of the art in enterprise data management: 

• Autonomous Schema Interpretation: Novel machine learning algorithms that could understand and map previously undocumented data structures without human intervention, reducing months of manual effort to days of automated discovery 

• Regulatory Knowledge Graphs: Advanced semantic models of financial regulations that enabled agents to translate complex compliance requirements into enforced policies automatically 

• Business Context Embedding: Proprietary techniques for infusing agents with domain-specific financial services knowledge, enabling them to understand concepts like "beneficial ownership" or "capital adequacy" in their business context 

• Agent Collaboration Protocols: Innovative coordination mechanisms that enabled specialized agents to work together on complex problems, sharing insights and resolving conflicts autonomously 

• Explainable Governance: Transparent decision-making capabilities that could provide human-understandable explanations for any data classification or policy enforcement action 

The implementation also followed industry best practices: 

• Zero-trust security model with comprehensive encryption and access controls 

• Shift-left compliance approach embedding governance at earliest stages 

• Infrastructure-as-code with comprehensive CI/CD pipelines 

• Comprehensive observability across all components 

• Business-aligned metrics and success criteria 

Results and Benefits 

Business Outcomes and Success Metrics 

The solution delivered transformative business impact within weeks of implementation: 

• Regulatory Compliance: Reporting time reduced by 75%, with zero compliance incidents or penalties since implementation, compared to $2.5M in the previous year 

• Decision Velocity: Insights delivered 70% faster, with business teams accessing data in minutes rather than days 

• Cross-Selling Effectiveness: Unified customer view enabled 28% increase in successful cross-selling across business lines 

• Innovation Acceleration: New product development cycles reduced by 35% through faster data access and experimentation capabilities 

• Resource Optimization: Data engineering team refocused from 72% maintenance to 80% innovation work, with 25 FTEs reallocated to strategic initiatives 

• Competitive Positioning: Customer onboarding time reduced by 62%, matching or exceeding fintech competitors 

• Cost Reduction: Total data management costs reduced by 63% through automation and optimization 

• Business User Empowerment: Self-service analytics adoption increased by 215% through natural language interfaces