Complimentary Gartner® Research: Data Lineage Is Essential to Manage the Risks of Agentic AI

Agentic AI systems autonomously plan and execute workflows, making them integral to modern enterprise operations. However, their autonomy creates significant risks – poor data quality and insufficient provenance analysis can undermine trust, compliance, and operational resilience. This Gartner report sets out why comprehensive, granular data lineage is now essential for any organization deploying AI at scale.

The Banking sector context

Data and analytics investment is accelerating. According to the 2025 Gartner CIO and Technology Executive Survey, 87% of banking respondents reported their enterprise would increase funding for business intelligence and data analytics in 2025. Yet a significant hurdle for achieving robust AI maturity in banking is often the current lack of foundational data maturity – underscoring the urgent need to prioritize investments in data and analytics alongside AI deployment.

(Source: Gartner, Hype Cycle for Data and Analytics in Banking, By Sudarshana Bhattacharya, 15 July 2025)

What this research covers

Data lineage — the comprehensive, auditable tracking of data from source to consumption — supports four critical outcomes for organizations adopting agentic AI:

• Trust and explainability – Detailed provenance records allow AI agents to validate decisions and provide explainable outputs, which is critical for fault detection, troubleshooting, and resilience, and optimization of execution trajectories.
• Regulatory compliance – Lineage supports audit readiness for mandates including the EU AI Act and BCBS 239, potentially reducing investigation times from weeks to hours by demonstrating that high-risk AI systems are trained on traceable datasets.
• Operational efficiency – Granular tracking enables real-time anomaly detection, where AI agents can automate quality checks to prevent “garbage-in, garbage-out” scenarios.
• Scalability – Active metadata and automated capture across diverse systems are required to maintain transparency in complex, multicloud environments ensuring data remains governable as it moves through the ecosystem.

Why data lineage is foundational to agentic AI

This is not an isolated finding. The Gartner Hype Cycle™ for Data and Analytics in Banking, 2025 reinforces the same dependency: the effectiveness of agentic analytics depends heavily on AI-ready data quality and consistency, requiring organizations to implement robust semantic layers that provide consistent business definitions, metrics, relationships,
and data lineage.

The same research recommends that banking leaders prioritise vendors offering strong explainability and transparency features – specifically citing data lineage tracing as a key requirement.

(Source: Hype Cycle for Data and Analytics in Banking, 2025)

The recommended actions by Gartner® for D&A leaders

The report outlines four actions for data and analytics leaders:

1. Adopt a unified metadata strategy: eliminate silos by integrating metadata across all sources using standardized protocols.
2. Deploy automated tools: invest in solutions that automatically capture and update lineage in real time as data flows through the system.
3. Integrate with governance: tie lineage systems directly to role-based access controls, audit trails, and anomaly detection.
4. Foster stewardship: train technical and business functions on the importance of data lineage to drive a culture of accountability.

Gartner, Data Lineage Is Essential to Manage the Risks of Agentic AI, By Guido De Simoni, 24 March 2026
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