How AI is reshaping B2C customer relationships

Artificial intelligence (AI) is rapidly transforming how companies interact with consumers, but its growing role in business-to-consumer (B2C) marketing is now challenging the very foundations of relationship-building in modern commerce, according to new research published in Marketing Theory. The study argues that AI-driven systems are not just enhancing marketing efficiency but fundamentally altering the nature of trust, commitment, and interaction between firms and consumers.

The study “AI transforming B2C relationships and relational exchange theory” examines how the integration of artificial intelligence (AI) into marketing disrupts long-standing assumptions about relational exchange theory (RET), a framework that has guided understanding of consumer–firm relationships for decades.

The findings come at a time when AI technologies such as recommendation engines, chatbots, virtual assistants, and predictive analytics are increasingly embedded across the consumer journey. While these tools promise personalization and efficiency, the study highlights deeper theoretical tensions that question whether traditional relationship marketing models can survive in an era where interactions are increasingly mediated by nonhuman systems.

AI challenges core assumptions of trust, commitment, and relational exchange

Relational exchange theory has long been a cornerstone of marketing thought, built on the idea that enduring relationships between firms and consumers are driven by trust, emotional connection, mutual commitment, and shared norms. These relationships were traditionally understood as human-centered exchanges rooted in intention, reciprocity, and moral responsibility.

The study argues that AI disrupts these assumptions at a fundamental level. Unlike human actors, AI systems operate through data-driven algorithms, probabilistic reasoning, and pattern recognition. They lack intentionality, emotional authenticity, and moral agency, yet they increasingly perform roles that were once reserved for human interaction.

This shift introduces a structural mismatch between theory and practice. Consumers are no longer interacting solely with firms or human representatives but with intelligent systems that simulate relational behaviors without actually possessing relational intent. As a result, concepts such as trust and commitment are being redefined in ways that diverge from their original theoretical meanings.

The research identifies five key relational constructs at the heart of RET: trust, commitment, mutuality, relational norms, and expectations of future interaction. Each of these constructs is affected by the presence of AI in different ways, leading to a growing gap between how relationships are conceptualized and how they are experienced in modern markets.

Trust, for instance, has traditionally been based on the belief that a partner will act in good faith. In AI-mediated environments, trust increasingly stems from system reliability, performance consistency, and predictive accuracy rather than moral credibility. Consumers may trust an algorithm not because it is perceived as ethical or benevolent, but because it consistently delivers useful outcomes.

Similarly, commitment, once understood as an emotional and moral investment in a relationship, is being reshaped by algorithmic convenience. Consumers may repeatedly engage with AI-driven platforms due to habit, personalization, or lack of alternatives, rather than genuine loyalty. This raises questions about whether observed behavioral continuity reflects true relational commitment or engineered dependence.

The study also highlights how mutuality, a core principle of relational exchange, becomes problematic in AI interactions. While consumers may perceive AI systems as responsive and attentive, these behaviors are programmed rather than intentional. The absence of true reciprocity challenges the theoretical foundation of mutual exchange.

Five emerging tensions redefine the nature of consumer–firm relationships

The study identifies five major tensions that arise when AI systems mediate B2C relationships. These tensions illustrate how AI both replicates and distorts relational dynamics.

• Simulation of emotion: Advances in affective computing allow AI systems to recognize and mimic emotional cues, creating interactions that appear empathetic and human-like. However, these expressions are not grounded in genuine emotional experience. This creates a gap between perceived and actual authenticity, raising questions about the role of emotional sincerity in relationship quality.
• The shift from interpersonal trust to algorithmic trust: Traditional trust is built through shared experiences and moral judgment, whereas algorithmic trust is based on repeated positive outcomes and system performance. This transformation reduces trust to a functional calculation, weakening its relational dimension.
• Reciprocity: In human relationships, reciprocity is driven by intentional actions and a desire to return benefits. AI systems, however, operate without awareness or intent. While they may appear responsive, their actions are determined by programmed rules and optimization processes. This creates a form of pseudo-reciprocity that challenges existing theoretical frameworks.
• Opportunism and power imbalance: AI systems can subtly influence consumer behavior through personalization, nudging, and behavioral targeting. These mechanisms may exploit cognitive biases while maintaining the appearance of customer-centricity. Because AI lacks moral accountability, traditional safeguards against opportunistic behavior become less effective.
• The nature of commitment: AI-driven interactions often foster behavioral loyalty through convenience, personalization, and system lock-in rather than emotional attachment. This shifts the basis of commitment from moral and affective dimensions to functional dependence.

Together, these tensions highlight a broader transformation in the concept of relationships within marketing. The study suggests that what appears as relational engagement may, in many cases, be a technologically engineered experience rather than a genuine social exchange.

Toward a dual-path framework for human and AI-mediated relationships

In response to these challenges, the study proposes a rethinking of relational exchange theory to accommodate the realities of AI-driven markets. Rather than abandoning RET, the research advocates for its evolution into a dual-path framework that distinguishes between human-to-human and human-to-machine relationships.

The first pathway preserves the traditional foundations of RET, focusing on moral agency, emotional connection, and intentional reciprocity in human interactions. This pathway remains relevant in contexts where human engagement plays a central role.

The second pathway introduces a functional perspective on relationships, recognizing that interactions with AI systems are governed by different dynamics. In this framework, relational quality is defined by usability, personalization, and system performance rather than emotional or moral factors.

This shift reflects a broader transition from moral to functional relationality. As AI becomes more embedded in consumer interactions, relationships are increasingly shaped by technical capabilities rather than social norms. Consumers engage with systems that anticipate their needs, adapt to their preferences, and deliver consistent outcomes, creating a new form of relationship that is efficient but fundamentally different from traditional human exchanges.

The study also calls for a redefinition of the actor model in marketing theory. While AI lacks true agency, consumers often perceive it as a social actor, attributing qualities such as friendliness, reliability, and even empathy. This creates a dual reality in which AI is both a tool and a perceived relational partner.

To address this complexity, the research suggests distinguishing between perceived relationality and actual relationality. This distinction allows scholars to account for the psychological experience of interacting with AI while acknowledging its lack of genuine agency.

Another critical area of transformation lies in governance. Traditional relational exchange relies on trust, norms, and mutual understanding to regulate behavior. In AI-mediated environments, governance is increasingly embedded in algorithms and digital infrastructures. These systems shape consumer choices, control information flows, and influence behavior in ways that are often opaque.

The rise of algorithmic governance introduces new challenges related to transparency, fairness, and accountability. As AI systems take on a greater role in managing relationships, the balance of power between firms and consumers may shift, raising ethical and regulatory concerns.

Implications for marketing strategy and future research

The transformation of consumer–firm relationships has both theoretical and practical implications. For marketers, the challenge lies in leveraging AI capabilities while preserving the relational values that underpin long-term engagement.

Organizations are encouraged to adopt a hybrid approach that combines AI-driven efficiency with human-centered interaction. While AI can handle routine tasks and enable large-scale personalization, human oversight remains essential for maintaining trust, addressing complex issues, and ensuring ethical standards.

Transparency emerges as a key factor in sustaining consumer trust. Firms must clearly communicate how AI systems operate, how data is used, and when human intervention is available. Providing users with control over personalization and data sharing can help mitigate concerns about surveillance and manipulation.

The research also highlights the importance of interdisciplinary collaboration in advancing marketing theory. Understanding AI-mediated relationships requires insights from fields such as human–computer interaction, ethics, sociology, and data science.

Future research is expected to focus on defining the boundaries of human–AI relationships, exploring different types of trust, and examining how commitment and loyalty evolve in algorithm-driven environments. There is also a growing need to investigate the role of AI in shaping power dynamics and governance structures within consumer markets.