Artificial intelligence and family businesses: a systematic literature review

Abstract

Purpose

This paper examines the integration of artificial intelligence (AI) within family businesses, focusing on how AI can enhance their competitiveness, resilience and sustainability. The study seeks to provide insights into AI’s application in family business contexts, addressing the unique strengths and challenges these businesses face.

Design/methodology/approach

A systematic literature review was conducted to synthesize existing research on the adoption and integration of AI in family businesses. The review involved a comprehensive analysis of relevant academic literature to identify key trends, opportunities, challenges and factors influencing AI adoption in family-owned enterprises.

Findings

The review highlights the significant potential of AI for family businesses, particularly in improving operations, decision-making and customer engagement. It identifies opportunities such as analysing customer data, enhancing brand building, streamlining operations and improving customer experiences through technologies like Generative AI, Machine Learning, AI Chatbots and NLP. However, challenges like resource constraints, inadequate infrastructure, low customization and AI knowledge gaps inhibit AI adoption in family firms. The study proposes an AI adoption roadmap tailored for family businesses and outlines future research directions based on emerging themes in AI use within these enterprises.

Originality/value

This paper addresses the underexplored area of AI integration in family businesses, contributing to the academic understanding of the intersection between AI and family-owned enterprises. The study offers a comprehensive synthesis of existing research, providing valuable insights and practical recommendations for enhancing the competitiveness and sustainability of family businesses through AI adoption.

Citation

Kumar, D. and Ratten, V. (2024), "Artificial intelligence and family businesses: a systematic literature review", Journal of Family Business Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JFBM-08-2024-0160