Artificial
Intelligence (AI) has moved from a peripheral digital capability to a central
driver of corporate strategy, reshaping decision-making, customer engagement,
operations, and risk exposure. Yet the same systems that enable predictive
analytics and automation can create material harms: discriminatory outcomes,
privacy and security failures, opacity in decision logic, and regulatory
noncompliance. These harms increasingly translate into financial loss through
litigation, enforcement penalties, brand erosion, and failed deployments. This
paper argues that AI governance should be treated as a strategic governance
function—anchored in board oversight and enterprise risk management—rather than
a narrow technical or compliance task. Using an integrative conceptual design
grounded in corporate governance theory, enterprise risk management (ERM), and
emerging regulation, the study develops an AI Governance Strategic Framework
(AIGSF) and an implementation roadmap that connect ethical accountability,
regulatory readiness, cybersecurity resilience, and performance outcomes. To
strengthen practical relevance, the paper presents case illustrations across
hiring, credit, consumer services, and generative AI, drawing lessons on
controls such as model documentation, algorithmic audits, impact assessments,
and human-in-the-loop oversight. The central contribution is a governance model
that links “trustworthy AI” practices to competitive advantage through reduced
uncertainty, faster deployment cycles, and higher stakeholder trust.
