A Conceptual Model for AI Adoption in Financial Decision-Making: Addressing the Unique Challenges of Small and Medium-Sized Enterprises

TL;DR

This paper proposes a layered conceptual model to facilitate AI adoption in SME financial decision-making, addressing resource and expertise barriers, and emphasizing data quality and validation for effective integration.

Contribution

It introduces a practical, layered framework tailored for SMEs to incrementally adopt AI in financial processes, focusing on overcoming resource and expertise challenges.

Findings

The model emphasizes incremental AI implementation for SMEs.

Data quality and validation are critical for successful AI adoption.

Provides a practical roadmap for integrating AI into SME finance.

Abstract

The adoption of artificial intelligence (AI) offers transformative potential for small and medium-sized enterprises (SMEs), particularly in enhancing financial decision-making processes. However, SMEs often face significant barriers to implementing AI technologies, including limited resources, technical expertise, and data management capabilities. This paper presents a conceptual model for the adoption of AI in financial decision-making for SMEs. The proposed model addresses key challenges faced by SMEs, including limited resources, technical expertise, and data management capabilities. The model is structured into layers: data sources, data processing and integration, AI model deployment, decision support and automation, and validation and risk management. By implementing AI incrementally, SMEs can optimize financial forecasting, budgeting, investment strategies, and risk management. This paper highlights the importance of data quality and continuous model validation, providing a practical roadmap for SMEs to integrate AI into their financial operations. The study concludes with implications for SMEs adopting AI-driven financial processes and suggests areas for future research in AI applications for SME finance.