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SME-TEAM: Leveraging Trust and Ethics for Secure and Responsible Use of AI and LLMs in SMEs

Iqbal H. Sarker, Helge Janicke, Ahmad Mohsin, Leandros Maglaras

TL;DR

The paper addresses the challenge of trustworthy and ethical AI adoption in SMEs amid rapid AI/LLM diffusion. It introduces the SME-TEAM framework, a structured, three-phase lifecycle built on four pillars (Data, Algorithms, Human Oversight, Model Architecture) with explicit governance, evaluation guidelines, and domain adaptability. By reframing trust and ethics as strategic enablers rather than constraints, the work offers both theoretical insight and practical guidance for integrating ethical AI into SME workflows, including lightweight evaluation and pilot deployment considerations. The framework aims to accelerate secure, transparent, and resilient AI adoption in diverse SME contexts, guiding researchers, policymakers, and practitioners toward sustainable, competitive digital transformation.

Abstract

Artificial Intelligence (AI) and Large Language Models (LLMs) are revolutionizing today's business practices; however, their adoption within small and medium-sized enterprises (SMEs) raises serious trust, ethical, and technical issues. In this perspective paper, we introduce a structured, multi-phased framework, "SME-TEAM" for the secure and responsible use of these technologies in SMEs. Based on a conceptual structure of four key pillars, i.e., Data, Algorithms, Human Oversight, and Model Architecture, SME-TEAM bridges theoretical ethical principles with operational practice, enhancing AI capabilities across a wide range of applications in SMEs. Ultimately, this paper provides a structured roadmap for the adoption of these emerging technologies, positioning trust and ethics as a driving force for resilience, competitiveness, and sustainable innovation within the area of business analytics and SMEs.

SME-TEAM: Leveraging Trust and Ethics for Secure and Responsible Use of AI and LLMs in SMEs

TL;DR

The paper addresses the challenge of trustworthy and ethical AI adoption in SMEs amid rapid AI/LLM diffusion. It introduces the SME-TEAM framework, a structured, three-phase lifecycle built on four pillars (Data, Algorithms, Human Oversight, Model Architecture) with explicit governance, evaluation guidelines, and domain adaptability. By reframing trust and ethics as strategic enablers rather than constraints, the work offers both theoretical insight and practical guidance for integrating ethical AI into SME workflows, including lightweight evaluation and pilot deployment considerations. The framework aims to accelerate secure, transparent, and resilient AI adoption in diverse SME contexts, guiding researchers, policymakers, and practitioners toward sustainable, competitive digital transformation.

Abstract

Artificial Intelligence (AI) and Large Language Models (LLMs) are revolutionizing today's business practices; however, their adoption within small and medium-sized enterprises (SMEs) raises serious trust, ethical, and technical issues. In this perspective paper, we introduce a structured, multi-phased framework, "SME-TEAM" for the secure and responsible use of these technologies in SMEs. Based on a conceptual structure of four key pillars, i.e., Data, Algorithms, Human Oversight, and Model Architecture, SME-TEAM bridges theoretical ethical principles with operational practice, enhancing AI capabilities across a wide range of applications in SMEs. Ultimately, this paper provides a structured roadmap for the adoption of these emerging technologies, positioning trust and ethics as a driving force for resilience, competitiveness, and sustainable innovation within the area of business analytics and SMEs.

Paper Structure

This paper contains 16 sections, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Digital Transformation and AI in SMEs: Current Practice
  3. Digital Transformation of SMEs
  4. Revolution of Today's AI and LLMs
  5. AI Risk Factors and Security Challenges
  6. Conceptual Foundation for the Framework
  7. Trust and Ethical AI Principles
  8. Framework Pillars: Mapping the Foundational Principles
  9. The Framework: SME-TEAM
  10. Framework Structure and Components
  11. Evaluation Guidelines
  12. Realizing Potential and Real-World Applicability in SMEs
  13. Discussion and Implications for Research
  14. Concluding Remarks
  15. Theoretical Contributions
  16. ...and 1 more sections

Figures (5)

  • Figure 1: Typically involved technologies such as machine learning, deep learning and large language models in the broad area of AI.
  • Figure 2: An illustration of the key aspects - Trust, Ethics and AI Modeling in SME-TEAM framework.
  • Figure 3: A motivational scenario demonstrating user confidence from the perspective of real-world applicability of SME-TEAM framework, comparing with traditional modeling.
  • Figure 4: An illustration of SME-TEAM pillars with the example of different SME sectors.
  • Figure 5: An illustration of SME-TEAM framework.