11th International Conference on ICTIS

Authors - Deepa V, Atul Anilkumar, Sheena Susan Andrews
Abstract - Organizations are rapidly embedding artificial intelligence (AI), including generative AI, into core business functions, but making AI sustainable across environmental, social, and economic dimensions is still challenging, especially when data governance is weak. Public estimates suggest data centres consumed roughly 415 TWh of electricity in 2024 and may rise toward ~945 TWh by 2030 under a base-case trajectory, while reported AI-related incidents reached a new high in 2024. In parallel, industry signals point to fast enterprise adoption of GenAI and ongoing leakage of sensitive information through tools that are not properly governed. Taken together, these patterns increase sustainability risks that are often data-mediated in practiceshaped by data quality and representativeness, provenance and documentation, access control, privacy protections, and end-to-end lifecycle management. Although data governance is widely seen as “foundational” to responsible AI, the concrete mechanisms linking governance capabilities to sustainable AI outcomes, and the ways to measure them, remain dispersed across data management, AI governance, and sustainability research. This paper consolidates peer-reviewed research, public standards, and open industry evidence to position data governance as an operational, measurable capability for Sustainable AI, one that converts sustainability goals into decision rights, lifecycle controls, and auditable outcomes. It contributes: (i) a capability-based taxonomy of data governance tailored to AI lifecycles; (ii) six evidence-grounded impact pathways showing how governance mechanisms influence outcomes (quality and fairness; documentation and auditability; privacy and security; interoperability and reuse; lifecycle stewardship; and sustainability instrumentation); and (iii) the Sustainable AI Data Governance Impact Model (SAI-DGIM), accompanied by testable hypotheses (H1–H8) and a KPI-oriented measurement framework that can be validated using survey constructs, system telemetry, and governance artifacts. For practitioners, the model offers a practical roadmap to embed governance controls directly into AI delivery workflows and treat sustainability metrics as release criteria, not just retrospective reporting. For researchers, it provides aligned constructs, hypotheses, and measurement guidance to rigorously assess how organizational data governance shapes Sustainable AI outcomes at scale.