Board Advisory & AI Governance

Artificial intelligence has become a board-level concern. The strategic implications of AI adoption—from competitive positioning to operational efficiency—command executive attention. Simultaneously, the risk dimensions of AI deployment—regulatory exposure, reputational vulnerability, ethical concerns—demand governance structures capable of providing meaningful oversight. Directors and officers who fail to establish appropriate AI governance may find themselves exposed to fiduciary liability claims when AI-related harms materialize. Our practice guides corporate leadership in developing governance frameworks that enable AI-driven value creation while maintaining appropriate risk management.

Fiduciary duties in the AI context require careful analysis. The duty of care obliges directors to make informed decisions, which in AI contexts demands sufficient understanding of algorithmic systems to evaluate their risks and benefits meaningfully. The duty of loyalty requires that AI deployment serve corporate interests rather than management convenience. The duty of oversight—articulated in the Caremark line of cases—requires boards to establish monitoring systems capable of detecting and addressing AI-related compliance violations. We counsel boards on the concrete steps necessary to satisfy these duties in the rapidly evolving AI landscape.

AI ethics committees have emerged as a governance mechanism for enterprises deploying consequential AI systems. Effective ethics committees require carefully crafted charters that define scope, authority, and escalation pathways. Committee composition should balance technical expertise, ethical analysis capability, and business perspective. Reporting structures must ensure that committee recommendations receive appropriate board attention without creating bottlenecks that impede legitimate operational AI deployment. We design ethics committee structures tailored to organizational context, industry requirements, and AI use case profiles.

AI risk frameworks require categorization taxonomies that capture the distinctive risks of algorithmic systems. Bias and fairness risks differ qualitatively from traditional operational risks. Explainability limitations create accountability gaps not present in rule-based systems. Model drift introduces performance degradation patterns unfamiliar to conventional risk management. We develop risk frameworks that integrate AI-specific risk categories into enterprise risk management structures, establishing appetite statements, key risk indicators, and escalation thresholds appropriate to algorithmic operations.

Board competence in AI matters has become a governance imperative. Directors need not become technical experts, but they must develop sufficient literacy to ask pertinent questions, evaluate management representations, and assess AI strategy proposals critically. We deliver board education programs that build this literacy efficiently, focusing on the governance-relevant dimensions of AI rather than technical minutiae. For boards seeking ongoing expert input, we advise on advisory board structures, technical consultant engagement, and board composition evolution to ensure appropriate AI expertise at the governance level.

Policy architecture establishes the normative framework within which AI deployment decisions occur. AI principles articulate organizational commitments to responsible AI development—transparency, fairness, accountability, and similar values—that guide operational decision-making. Use case governance policies establish review and approval processes for new AI applications, with differentiated pathways based on risk categorization. Stakeholder engagement frameworks ensure that affected parties—employees, customers, communities—have voice in AI governance without creating decision paralysis. We draft policy suites that operationalize responsible AI commitments while enabling business agility.

Disclosure and transparency obligations increasingly require board attention to AI matters. Securities regulations mandate disclosure of material risks, which may include AI-related exposures for technology-dependent enterprises. ESG reporting frameworks increasingly incorporate algorithmic accountability dimensions. Investor and analyst scrutiny of AI governance practices creates reputational incentives for visible governance commitment. We advise on disclosure strategy that satisfies legal requirements while effectively communicating governance maturity to stakeholders who evaluate AI-related investments.

Crisis preparedness for AI incidents requires advance planning. When an AI system produces discriminatory outcomes, causes operational failures, or attracts regulatory attention, response speed and coherence significantly affect outcome severity. We develop AI incident response playbooks that establish decision-making authority, communication protocols, and remediation pathways before crises emerge. Tabletop exercises test these frameworks, identifying gaps and building organizational muscle memory for effective crisis response.