Artificial Intelligence in Corporate Boards

Artificial intelligence in corporate boards refers to the use of data-driven computational systems, including machine learning, predictive analytics, natural language processing and generative models, to support the work of boards of directors in one-tier systems and supervisory boards in two-tier systems. Executive management, including management boards in two-tier systems, is considered insofar as it supplies information to or is overseen by these bodies. Such systems are applied to the established functions of the board: monitoring and control, strategic direction, advice and counsel to executive management, and the provision of information, expertise and external linkages. Applications range from anomaly and compliance monitoring to environmental scanning, scenario modeling and the synthesis of board materials. A distinction is essential between decision support, in which systems supply information and analysis; decision augmentation, in which board members and systems jointly shape an evaluation; and autonomous decision-making, in which a system determines an outcome without human deliberation; board-level use is discussed in the literature almost entirely in the first two modes. The central governance tension is that capabilities which expand the information-processing capacity of a body constrained by limited meeting time and by dependence on management-supplied reporting also introduce opacity, vendor dependence and new difficulty in attributing responsibility. Under prevailing corporate governance arrangements, formal responsibility for board decisions remains with the human members of the relevant governing body. Responsible use therefore rests on explicit governance arrangements: defined human oversight, documentation, verification, data governance and board-level competence.