AI adoption creates a familiar enterprise tension: leaders want faster innovation while governance teams need to manage risk and prove business value. In the final part of a three-part series, Beth Hiatt, SVP, Product Management, Data Governance & Responsible AI at LPL Financial, shares her perspective on making AI priorities actionable with Jessie Smith, Chief Product Officer at Ataccama.

Hiatt argues that AI initiatives need clear business prioritization. Leaders can identify which use cases create business value, decide where to focus, and measure outcomes as capabilities are deployed. Executive buy-in matters, but direction matters too. Leadership needs to determine whether the priority is a new product, an efficiency gain, or another defined business outcome.

The conversation also examines a common challenge in data management and governance: building programs around a model that does not match organizational maturity. Hiatt reflects on learning to assess where an organization stands before rolling out roles, responsibilities, operating models, and core governance capabilities.

The discussion highlights four practical themes:
• Business prioritization: AI use cases need a clear connection to organizational goals.
• Organizational maturity: Governance programs need to match current readiness for new roles and processes.
• Course correction: Data leaders need to reassess progress and adjust the path when needed.
• Execution discipline: Progress depends on pushing when the organization is ready and taking incremental steps when it is not.

As the concluding installment, the conversation leaves data leaders with a practical starting point: assess organizational maturity, clarify the outcome, and course-correct when necessary.

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