ABSTRACT Artificial Intelligence (AI) is significantly impacting personalized and dynamic pricing strategies by allowing for automated, high‐frequency, data‐driven price setting in various sectors. However, revenue‐efficient algorithmic pricing can become unsustainable when it compromises customer legitimacy or attracts regulatory intervention. This systematic review, conducted following the PRISMA protocol, examines whether AI‐based pricing can attain sustainability through a tri‐stakeholder equilibrium: (i) customer unfairness minimized, (ii) firm revenue/profit maximized, and (iii) meeting policymaker standards, acting as a mediating constraint to “profit at any cost.” Systematic searches of prominent academic databases (2008–2024, with iterative updates until December 2025) resulted in 153 relevant studies, with 56 included in the analysis through structured extraction and stakeholder‐based allocation. The results indicate that customer reactions operate through a mechanism pipeline, connecting price dynamics (volatility, personalization, channel inconsistency) to reference price comparisons, procedural interpretability, and inferred firm motives, which in turn affect customer trust, search, avoidance, and retaliation. The study also finds support for a transparency‐trust paradox, whereby “bare” AI disclosures can backfire by triggering exploitative attribution, particularly among price‐sensitive consumers. For businesses, profit maximization is contingent upon market factors, including perishability, demand instability, and infrastructure limitations, while sustainability is best achieved by considering transparency as a profit‐preserving constraint through price range, price drivers, and auditable processes. For policymakers, sustainability risks include privacy, bias, and autonomous collusion, emphasizing the importance of transparency, oversight, and acceptable price dispersion. The systematic review contributes to a bounded optimization agenda by balancing algorithmic pricing efficiency with customer legitimacy, while also highlighting research and managerial priorities for sustainable AI‐based pricing.
Araf et al. (Sun,) studied this question.