In Conversation With Susmit Sen On Data, AI Governance And Trust In Critical Minerals
From building enterprise Data Governance at scale to leading Data Management, Data Governance and AI Governance at scale in world’s largest companies, including Albertsons which is one of the largest grocery chains in US, Susmit Sen has spent decades at the intersection of data, technology and business decisions. Having led governance and technology initiatives across diverse sectors — including…
Susmit Sen is a seasoned professional with over two decades of experience in data, technology, and governance. He currently leads Data & AI Governance at a leading mining and critical minerals company, a field he sees as a natural evolution of his prior work in the industry. Susmit emphasizes that AI's trustworthiness hinges on the data, processes, and controls surrounding it, which becomes even more crucial in the critical minerals sector where decisions can have wide-ranging consequences.
He stresses that AI cannot automatically make decisions trustworthy; governance is essential in ensuring that decisions are responsible and reliable. Susmit argues that Data Governance and AI Governance cannot be treated as separate disciplines because the quality of data directly impacts the outcomes and risks generated by AI systems. He believes that governance should start with the specific use case, identifying potential risks and determining appropriate controls, accountability, and oversight.
Susmit notes that the line between Data Governance and AI Governance is becoming increasingly blurred due to advancements in AI, particularly in areas like generative and agentic AI. Traditional Data Governance principles such as data ownership, quality, lineage, access, and lifecycle have expanded to encompass model training data, explainability, monitoring, human oversight, and outcomes.
He highlights the need for increased accountability and oversight in AI systems, moving beyond mere output and delving into AI behavior, decision-making processes, and outcomes.
The challenges of AI Governance in the mining and critical minerals industry are significant due to the complex interplay of operational technology, enterprise platforms, geological and engineering data, environmental information, supply chains, and multiple jurisdictions. Susmit believes that organizations must focus not only on whether data exists but also whether it is fit for AI-driven decision-making.
As the role of data leaders evolves, they must increasingly consider strategy, risk, and business value in their approach.
Regarding agentic AI, Susmit expresses concern about the transition from AI making recommendations to AI taking actions autonomously. He stresses the importance of clearly defining boundaries, authorization, access, accountability, traceability, and human intervention when AI operates with increased autonomy. Organizations should prioritize measuring the impact of AI beyond mere efficiency and productivity gains, focusing on creating measurable value responsibly.
He advocates for a shift in measuring outcomes related to decision quality, risk reduction, resilience, speed, trust, and overall business success, with responsible AI serving as a standard beyond mere compliance.
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