AI Agentic Workflow for Automated ESG Data Extraction from Legacy ERP Systems

The operational bottleneck is manual data extraction from legacy ERP modules like SAP MM, FI, and CO, where sustainability analysts spend weeks downloading reports, reconciling formats, and manually entering data into spreadsheets. A custom agentic workflow automates this by deploying specialized data extraction agents that connect directly to ERP front-ends via secure browser automation (e.g., Playwright) or API wrappers where available. These agents are orchestrated to execute scheduled queries, handle authentication, and parse semi-structured reports, normalizing data into a unified schema. This eliminates the primary source of delay and error in the reporting chain, cutting data collection time from weeks to hours and freeing analysts for validation and analysis.

Implementation requires an orchestrator like LangGraph to manage agent state, handle retries, and enforce data lineage. Each agent is containerized for deployment in the client's cloud, with credentials managed via a vault. The workflow integrates validation logic to flag outliers against historical baselines, routing exceptions to a human review queue in tools like ServiceNow or Jira. The final, approved data is pushed to a central data lake, creating an immutable audit trail for assurance. This architecture delivers a 70-80% reduction in manual effort for global data collection, directly lowering operational cost and accelerating the time to accurate, report-ready metrics.

This workflow automates the most labor-intensive bottleneck in ESG reporting: manually collecting data from dozens of fragmented ERP modules and legacy systems. By deploying specialized agents that connect via secure browser automation and API wrappers, you eliminate weeks of manual querying and spreadsheet consolidation. The operational upside comes from reducing data collection time from weeks to hours, cutting reporting cycle time by over 70%, and freeing sustainability analysts for higher-value validation and strategic analysis.

Implementation requires deploying containerized agents with secure credential management, each specialized for a specific ERP interface (e.g., SAP GUI scripting, Oracle Forms). The orchestrator manages authentication, schedules data pulls, and handles retries. Critical controls include data lineage logging, anomaly detection for outlier values, and mandatory human review gates for any extraction flagged by quality checks. This architecture integrates directly with your data lake, feeding downstream calculation and reporting workflows with audit-ready inputs.

Phase one establishes the core orchestration and data ingestion foundation. We deploy a LangGraph-based controller that authenticates to ERP systems via secure credential vaults, then dispatches specialized browser automation agents to navigate legacy UIs and API wrapper agents for modern endpoints. This layer executes scheduled data pulls, handles session management, and logs all extraction attempts with millisecond latency for audit. The initial focus is on high-volume, structured data sources like utility invoices and material master tables to demonstrate rapid ROI in data collection labor reduction.

Subsequent phases introduce validation, exception routing, and scale. Extracted data passes through rule-based completeness checks and outlier detection. Exceptions are routed to a human review dashboard in ServiceNow or Teams, with automated re-try logic. The final phase integrates this cleansed data stream with framework-mapping engines and approval workflows, cutting global data aggregation from weeks to hours. Governance is enforced through immutable audit logs for each data point, pre-validating the dataset for external assurance and reducing audit friction and cost.

The operational upside comes from eliminating weeks of manual data collection, but uncontrolled automation risks polluting your ESG data lake with unverified figures. Governance starts with defining data ownership per facility and material category, then embedding validation rules—such as range checks against historical consumption—directly into the extraction agents. Approval gates must be integrated with existing systems; for example, an extracted utility spend anomaly should route as a task to the local plant manager in ServiceNow before proceeding to aggregation.

Phased rollout is critical for managing complexity and building trust. Start with a single material category (e.g., natural gas) in one region, using the human review queue for 100% of extractions to refine parsing logic. Phase two expands to all Scope 1 sources in that region, automating approvals for data passing high-confidence validation. The final phase rolls out globally, with monitoring dashboards tracking extraction success rates, data drift, and time-to-approval to ensure the system scales without degrading data integrity for CSRD or assurance.

Manual ESG data extraction from legacy ERPs like SAP or Oracle is a costly bottleneck, consuming weeks of analyst time each quarter to navigate fragmented tables and custom transactions. A custom agentic workflow automates this by deploying specialized data-retrieval agents that connect directly to the ERP via secure API wrappers or browser automation where APIs are lacking. This architecture transforms operational line items into structured ESG inputs, cutting collection time from weeks to hours and ensuring data lineage for audit readiness.

Implementation requires orchestrators like LangGraph to manage agent sequencing, exception routing, and idempotent retries. Browser automation agents handle legacy GUI interactions using Playwright or RPA tools, while API agents extract structured data where available. Critical controls include data validation rulesets, approval gates for anomalous entries in systems like ServiceNow, and immutable audit logs for each data point. This workflow directly reduces FTEs dedicated to data gathering, improves data quality for Scope 1/2 reporting, and creates a scalable foundation for real-time ESG monitoring.