Crafting an Effective AI Governance Framework for B2C Marketing Success and Competitive Edge - Kulkarni, Sanjay (Digital Repository of Theses - SSBM Geneva, 2025)
Artificial Intelligence (AI) is a transformative technology evolving at a fast pace with rapid innovation. AI is revolutionizing B2C marketing and customers are interacting with brands like never before and both classical AI/ ML and Generative AI using Large Language models (LLMs) like ChatGPT, Gemini, etc., offer tremendous benefits. AI systems are a bane and boon to the global community, operate in a borderless world, and are considered a double edge sword with harmful and non-harmful use. There are variety of AI risks to consumer/human well-being like exploitation of vulnerabilities, behavior manipulation and societal risks of bias, discrimination and data privacy. The recent risk-based regulatory approach focuses primarily on unacceptable-risk, high-risk and medium- risk, while not obligating the low-risk AI systems like B2C marketing, leading to lack of accountability issues for AI systems used in consumer targeting and personalized advertising. The aim of this research is a novel attempt to create a holistic AI governance framework for B2C marketing success and competitive edge with a theoretical, conceptual and operational frameworks for B2C marketers to comprehend to a legal framework within which they can operate reliably, protect their reputation, protect consumers’ & society from potential harms and comply with AI regulation. This study considers four main AI regulation policies with the already legislated and in-force EU AI Act, (2024) based on OECD AI Principles for human well-being and safety, the self-regulation and non-binding US AI Risk Management framework (AI RMF), (2023), the Chinese AI regulations, the Brazil AI regulations and the Singapore model governance framework and addresses the gaps by creating a novel AI governance framework by harmonizing the global regulatory frameworks for B2C marketing success and competitive edge. They current regulations lack operational details and there is no established framework or theoretical model that is commonly accepted by the industry that describes in detail the overall AI governance firms should adopt. The study highlights and proposes the critical changes in corporate governance, organization strategy, change management, roles & responsibility, accountability of stakeholders and recommends an agile & collaborative approach to evolve the organization culture on AI governance.