Artificial intelligence acceptance and implementation in microenterprises: a conceptual framework and evidence from a pilot study

Purpose: This paper proposes and preliminarily validates a process model for the acceptance and implementation of Artificial Intelligence (AI) in microenterprises.It addresses the research gap concerning how small firms, often constrained by limited resources and managerial capacity, adopt AI.The study aims to identify the stages and conditions necessary for effective and sustainable AI integration in microenterprise contexts.Design/methodology/approach: A conceptual model of AI acceptance and implementation was developed based on a literature review, comprising seven stages: awareness and education, top management support, readiness assessment, strategy formulation, pilot testing and feedback, employee training and engagement, and continuous evaluation.To verify the model's conceptual validity, a quantitative pilot study was conducted in July 2025 among n = 10 whitecollar employees from microenterprises in advertising, transport, tourism, and legal services.Data were collected using an online questionnaire with both Likert-scale and non-Likert questions.Findings: Results indicate that AI adoption in microenterprises remains at an early stage.Mean scores (1.8-3.4)reveal moderate awareness but low managerial support, limited readiness, and an absence of strategic direction.Although all respondents reported using AI tools -mainly ChatGPT and other freely available applications -usage was individual and experimental rather than organisation-driven.The first two stages of the process (awareness and management support) were largely unfulfilled, constraining subsequent implementation phases.Research limitations/implications: The pilot's small, non-random sample limits generalisability.Future research should involve larger, cross-sector samples and include qualitative methods to explore managerial aspects of AI adoption in greater depth.Practical implications: The proposed process model serves as a diagnostic framework to guide microenterprise managers in prioritising early-stage interventions -particularly in developing awareness and managerial engagement -to ensure strategic and coordinated AI adoption.Originality/value: This study offers one of the first empirically grounded models of AI acceptance and implementation tailored to microenterprises.It contributes to the literature on digital transformation by highlighting stage-specific challenges and underscoring the pivotal role of managerial awareness and leadership in fostering effective, sustainable AI integration.