AI Governance Best Practices: A Framework for Data Leaders

Author(s): David Sweenor

Originally published on Towards AI.

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Colorado State Capital — Photo by Author David E. Sweenor

The debate over the role of AI governance in business success is not just about compliance or ethical concerns — it’s a question of whether companies can realize the financial potential that AI promises to unlock. As AI moves from concepts and proofs-of-concept (POCs) to a core component of business operations, the real question for business leaders is not if AI governance matters, but how it directly influences their bottom line.

Unfortunately, many organizations underestimate the importance of AI-ready data and governance, assuming they can retrofit data and AI strategies once their AI/ML models are in place. This assumption leads to project delays, underperformance, and inflated costs, all of which can be avoided with proper governance from the outset.

This blog argues that robust AI governance should be a forethought, not an afterthought. AI governance, grounded in trustworthy data, is essential for ensuring AI’s financial returns and long-term business viability. Business leaders who fail to prioritize this foundational step set themselves up for failure.

The argument for AI governance is not just theoretical — it’s backed by measurable financial outcomes. Research by Gartner… Read the full blog for free on Medium.

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Published via Towards AI