(1) Background: Generative and, increasingly, agentic artificial intelligence (AI) are reshaping how firms create, deliver, and capture value. Yet business model innovation (BMI) research largely assumes that humans design business models and digital technologies support their decisions. The capacity of AI systems to generate value-bearing artifacts and undertake goal-directed action challenges this assumption. (2) Approach: Integrating research on business models, dynamic capabilities, and AI in management, this concept paper develops a framework that distinguishes three logics of AI-enabled BMI: a supporting logic (AI as instrument), a generative logic (AI as co-creator), and an agentic logic (AI as economic actor). It explains how these logics vary in the locus of agency and the tempo of innovation. (3) Contribution: The paper advances six propositions showing how generative and agentic AI redistribute agency in value creation, delivery, and capture, while shifting BMI from episodic redesign toward continuous and potentially autonomous adaptation. (4) Conclusions: It develops a research agenda spanning value architectures, human–AI configurations, governance and legitimacy, organizational capabilities, and measurement, thereby providing a theoretically grounded and empirically tractable foundation for research on AI-enabled BMI.

For the last decade, the prevailing way to describe our relationship with AI has been “human–AI interaction.” It sounds reasonable: a person uses a system, the system responds, the person decides. But the essence of that relationship is rapidly changing and call it an interaction is misleading because it implies that AI sits outside the flow of work and thought, waiting to be “interacted with.” That’s not what is happening anymore. What is rapidly emerging is what I call “human-AI integration”. It is a condition where AI is embedded into everyday cognition, workflows, decision routines, and increasingly permeating organizational governance. The unit of change is no longer “a user interacting with a machine or a digital tool.” It is the reconfiguration of how work gets done—and how judgment, accountability, and capability are distributed across people and machines which are smart, connected and becoming increasingly autonomous. Let’s dig further into it.

(1) Background: Generative and, increasingly, agentic artificial intelligence (AI) are reshaping how firms create, deliver, and capture value. Yet business model innovation (BMI) research often assumes that humans retain design authority while digital technologies support analysis or execute predefined decisions. This assumption becomes contestable when AI systems can generate value-bearing artifacts, participate in organizational decision-making, and undertake goal-directed action within delegated boundaries. (2) Approach: Integrating business-model and activity-system research, dynamic capabilities, socio-technical and human–AI decision-making perspectives, and research on generative and agentic AI, this concept paper develops a framework that distinguishes three logics of AI-enabled BMI: a supporting logic (AI as instrument), a generative logic (AI as co-creator), and an agentic logic (AI as delegated economic actor). The framework distinguishes business-model operation from business-model innovation and identifies delegated reconfiguration authority as a key condition under which agentic action can constitute BMI. (3) Contribution: The paper advances six propositions explaining how AI redistributes ideation, decision, execution, and reconfiguration rights; how machine agency may compress the interval between business-model reconfigurations; how value-capture mechanisms may change; and why complementary capabilities, governance, and legitimacy remain decisive. (4) Conclusions: The paper offers a focused research agenda centered on agency allocation and decision rights, the temporal dynamics and measurement of BMI, and value capture and governance in agent-mediated business models. It thereby provides a more precise theoretical basis for distinguishing autonomous operation within a business model from autonomous or semi-autonomous reconfiguration of the business model itself.

Anthropomorphism in text-based chatbots has become a central construct in research on human–AI interaction, yet findings remain dispersed across studies of trust, social presence, satisfaction, adoption, and purchase intention. This article reports a systematic review and meta-analytic structural equation modelling (MASEM) synthesis of 20 independent samples from 19 peer-reviewed quantitative studies (N = 8,249) restricted exclusively to text-based chatbots across Scopus, Web of Science, PsycINFO, ABI/Inform, and Business Source Complete. Two-stage MASEM yielded robust positive pooled correlations between anthropomorphism and satisfaction (r = .696), trust (r = .633), social presence (r = .599), adoption/use intention (r = .595), and purchase intention (r = .565). Trust emerged as the strongest mechanism linking anthropomorphism to adoption and purchase intention, while satisfaction fully mediated the anthropomorphism–purchase pathway. Evidence for social-presence mediation was directionally positive but statistically inconclusive given the small number of studies reporting this construct pair. The review advances a conservative ACMM framework — antecedents, chatbot anthropomorphism, consequences, mediators, and moderators — grounded in the Computers Are Social Actors paradigm, media equation theory, and the three-factor theory of anthropomorphism. Cultural context moderated the anthropomorphism–trust path (East Asian r̄ = .680 vs non-East Asian r̄ = .577; Δr = .103). The review identifies full Fornell-Larcker matrix reporting as the single most important practice for enabling future integrated MASEM and offers specific reporting recommendations for primary researchers.