Advancing Responsible AI Innovation A Playbook 2025

Appoint and incentivize AI governance leadersPlay 4 Responsible AI senior leaders enable robust governance frameworks that provide boards of directors with assurance of regulatory compliance across the enterprise, consistent risk thresholds and strategic business alignment. Organization leaders Key roadblocks that arise within the organization Highly unstructured governance, unclear accountability, and insufficient top-down guidance affecting the implementation of “responsibility”41 Problems in identifying and hiring leaders with interdisciplinary knowledge Misalignment between business functions, inducing misunderstandings about AI definitions, risks, responsibilities and success metrics Actions for organization leaders –Appoint a senior AI governance leader and cross-functional AI governance body: This provides confidence in AI risk oversight and compliance and supports alignment with broader business objectives. Key factors include: –Set AI governance as the leader’s primary responsibility: If resources only afford designation as an additional responsibility, ensure the individual can be sufficiently dedicated to the role. –Evaluate where to house AI governance: Examine the trade-offs of housing governance responsibilities within an existing or new function. Organizations report variability in focus areas for individuals assigned to responsible AI, such as privacy, ethics and risk, or analytics.42 Ensure leaders are resourced to act cross-functionally to advance end-to-end governance. –Promote cross-functional alignment: Intentionally align on terminology and expectations by defining key AI terms accompanied by examples, including edge cases. –Segregate duties: Separate responsibilities between delivery and assurance teams and distribute discrete critical functions of authorization, custody, record-keeping and reconciliation across independent teams. Prevent any single entity from holding unchecked control over AI processes or assets to reduce risks of errors, fraud and regulatory breaches. –Use a phased approach to maturing AI governance: Start with a centralized governance model (e.g. a cross-functional committee) to ensure consistency and accountability. As practices mature, evolve towards a more federated or hybrid model that empowers business units with context-aware oversight (see Case studies 1 and 5). Advancing Responsible AI Innovation: A Playbook 19