Adaptive Product Governance for AI-Mediated Customer Journeys: A Dynamic Framework for Requirements Fluidity and Governance Complexity in Enterprise AI Systems

Abstract

The integration of generative artificial intelligence (AI), adaptive recommendation systems, and large language model (LLM)-powered conversational interfaces into enterprise digital products has fundamentally transformed how customers interact with products and services. Historically, customer journeys were modeled as stable, linear sequences of touchpoints that product teams could define, validate, and govern using conventional Product Development Lifecycle (PDLC) frameworks. However, AI-mediated customer journeys are inherently dynamic rather than static, continuously evolving through model retraining, adaptive personalization, user feedback, and shifting data distributions. This behavioral dynamism introduces persistent customer journey volatility that challenges conventional governance approaches and reduces the effectiveness of traditional product management practices. This paper formalizes the AI-Induced Instability Model, a four-stage conceptual framework describing how AI behavioral adaptation propagates into customer journey volatility, requirement instability, governance complexity, and execution friction throughout enterprise product development. Building upon this diagnostic model, the study proposes the Adaptive Governance Framework (AGF), a six-dimensional governance architecture that redefines product governance through velocity-matched requirement revalidation, hypothesis-driven product specifications, progressive capability streaming, continuous risk assessment, AI lifecycle governance, and dynamic portfolio management. The framework is designed to support organizations in maintaining alignment between rapidly evolving AI capabilities and business objectives while preserving governance, compliance, and delivery quality. The AGF was empirically evaluated through a 14-week enterprise e-commerce product governance program involving 14 concurrent AI-enabled product initiatives. The evaluation demonstrated a 43% reduction in requirement revalidation cycle time, a 37% decrease in backlog volatility, a 29% improvement in delivery alignment, and governance documentation completeness reaching 91%, compared with 54.2% under a traditional PDLC baseline. These findings demonstrate that adaptive governance significantly improves organizational responsiveness, decision quality, and execution consistency in AI-driven product environments. The proposed framework provides enterprise product managers, technology leaders, and governance practitioners with a practical, scalable, and quantitatively validated architecture for managing AI-mediated digital products operating in continuously evolving customer ecosystems while ensuring sustainable innovation, regulatory compliance, and long-term business value.