Agentic Artificial Intelligence in Business Decision-Making: A Framework for Human–AI Collaborative Governance and Strategic Value Creation
TL;DR
A validated, practitioner-usable framework for calibrating the degree of AI autonomy granted to decision workflows against the governance capacity of the organization is contributed, and offers implications for management education, internal audit, and enterprise risk functions responsible for overseeing algorithmic decision-making.
Abstract
The rapid maturation of agentic artificial intelligence (AI) systems, capable of autonomously planning, executing, and adjusting multi-step actions with minimal human intervention, is reshaping how organizations arrive at strategic and operational decisions. Executive surveys indicate that a majority of business leaders now routinely rely on AI to inform decisions, and industry forecasts anticipate that a substantial share of business decisions will be augmented or automated by AI agents within the next several years. Yet the diffusion of decision-support and decision-making AI has outpaced the governance structures, skill sets, and trust mechanisms needed to deploy it responsibly. This paper develops and tests a conceptual framework, termed the Human–AI Collaborative Decision Governance (HACDG) model, that positions human agency, algorithmic transparency, and organizational trust as the three pillars mediating the relationship between AI adoption and decision quality. Using a mixed-methods design that combines a structured survey of 168 mid- and senior-level managers across manufacturing, financial services, retail, and information-technology sectors with semi-structured interviews of 14 senior executives, the study examines how the intensity of AI involvement in decision workflows interacts with governance maturity to influence perceived decision quality, decision speed, and employee confidence in outcomes. Findings suggest that AI involvement improves decision speed almost uniformly, but improves perceived decision quality only when paired with moderate-to-high governance maturity; in its absence, heavy AI reliance is associated with lower confidence and higher post-decision regret, mirroring patterns of automation complacency documented in other high-stakes domains. The paper contributes a validated, practitioner-usable framework for calibrating the degree of AI autonomy granted to decision workflows against the governance capacity of the organization, and offers implications for management education, internal audit, and enterprise risk functions responsible for overseeing algorithmic decision-making.