AI ETHICS IN BUSINESS SCENARIOS: CORPORATE LEAP FORWARD, GOVERNANCE STRATEGIES, AND ACTIONABLE RISK FRAMEWORKS

As generative AI is integrated into core business processes such as marketing, risk control, recruitment, and customer service, enterprises face ethical and compliance risks, including bias and discrimination, privacy breaches, insufficient explainability, and unclear attribution of responsibility, while experiencing leaps in efficiency. This paper takes "AI Ethics in Business Applications" as its research object, drawing on the empirical paradigm of "time structure alignment + interval summary comparison" from the appendix paper. It selects publicly available reports on enterprise AI adoption rates and policy governance signals as samples to construct a reproducible descriptive empirical framework. On the one hand, it depicts the phased leaps in enterprise AI adoption rates from 2017 to 2025; on the other hand, it proposes actionable control points for business management based on AI governance frameworks (OECD AI Principles, NIST AI RMF). The study finds that enterprise AI adoption accelerated significantly around 2024, with generative AI penetration increasing even faster. Simultaneously, discussions on AI-related legislation and governance on the policy side are also on the rise, suggesting that enterprises need to upgrade "ethical governance" from a passive task of compliance departments to an integral part of their business strategy and risk management system. Finally, this article presents actionable governance guidelines to help companies reduce ethical risks and enhance trust while pursuing growth and innovation.