The Vela Navigator: AI Board Governance Blueprint^SM

Can directors articulate where AI changes the business model, market position and sources of value?

Does the board scrutinize AI investment choices, returns, measurable outcomes and opportunity costs?

Is AI's materiality to products, operations and competitive position explicitly understood and tracked?

Does management have board-understood criteria to pilot, scale, pause or stop AI deployments?

Is accountability clear across board, committees and management, including escalation of consequential AI decisions?

Can directors recognize assumptions, ask informed questions and draw on outside expertise without needing technical mastery?

Does the board receive decision-useful evidence about material AI adoption, outcomes, incidents and control effectiveness?

Can the board explain how trade-offs involving fairness, privacy, IP and applicable rules were considered and documented?

Are provenance, quality, model validation, identities, permissions and autonomous-agent actions governed and auditable?

Does the board understand AI dependencies within vendors and critical services, including hidden or cascading dependencies?

Are material AI use cases stress-tested, with fallback processes, monitoring, remediation and retesting evidence?

Are security, privacy, model drift, change management and controls reviewed as systems evolve?

Does AI adoption have a workforce plan addressing redesign of work, skills, accountability and potential displacement?

Can people understand consequential AI use and access appropriate explanation, review and remedies?

Does the board understand who benefits, who bears risk and how stakeholder impacts are monitored?

Are material societal or ecosystem harms considered proportionately to the company's actual AI footprint?