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Assessing AI Adoption and Digitalization in SMEs: A Framework for Implementation

Serena Proietti, Roberto Magnani

TL;DR

This study investigates AI adoption and digitalization among Italian SMEs, revealing低 digital maturity and limited AI usage compared with larger firms. It combines a state-of-the-art–driven framework with an empirical survey of 36 SMEs across sectors to identify barriers and to propose a bottom-up, four-factor framework (Objectives, Human Aspects, External Support, Technical Enablers) for SME-ready AI implementation. Key contributions include quantifying maturity levels, highlighting managerial guidelines, and detailing human-centric, data-driven, and collaboration-enhanced pathways (e.g., PoCs and cloud AI) to enable scalable AI adoption. The work offers practical implications for managers and policymakers to accelerate intelligent transformation in Italy’s SME ecosystem, while acknowledging the need for broader, cross-country validation and SME-specific self-assessment tools.

Abstract

The primary objective of this research is to examine the current state of digitalization and the integration of artificial intelligence (AI) within small and medium-sized enterprises (SMEs) in Italy. There is a significant gap between SMEs and large corporations in their use of AI, with SMEs facing numerous barriers to adoption. This study identifies critical drivers and obstacles to achieving intelligent transformation, proposing a framework model to address key challenges and provide actionable guidelines

Assessing AI Adoption and Digitalization in SMEs: A Framework for Implementation

TL;DR

This study investigates AI adoption and digitalization among Italian SMEs, revealing低 digital maturity and limited AI usage compared with larger firms. It combines a state-of-the-art–driven framework with an empirical survey of 36 SMEs across sectors to identify barriers and to propose a bottom-up, four-factor framework (Objectives, Human Aspects, External Support, Technical Enablers) for SME-ready AI implementation. Key contributions include quantifying maturity levels, highlighting managerial guidelines, and detailing human-centric, data-driven, and collaboration-enhanced pathways (e.g., PoCs and cloud AI) to enable scalable AI adoption. The work offers practical implications for managers and policymakers to accelerate intelligent transformation in Italy’s SME ecosystem, while acknowledging the need for broader, cross-country validation and SME-specific self-assessment tools.

Abstract

The primary objective of this research is to examine the current state of digitalization and the integration of artificial intelligence (AI) within small and medium-sized enterprises (SMEs) in Italy. There is a significant gap between SMEs and large corporations in their use of AI, with SMEs facing numerous barriers to adoption. This study identifies critical drivers and obstacles to achieving intelligent transformation, proposing a framework model to address key challenges and provide actionable guidelines
Paper Structure (13 sections, 4 figures, 2 tables)

This paper contains 13 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Methodology
  4. Sample design
  5. Data Collection and analysis
  6. Findings
  7. Framework
  8. Key factors
  9. Objectives
  10. Human Aspects
  11. External support
  12. Technical Enablers
  13. Discussion

Figures (4)

  • Figure 1: Research method
  • Figure 2: Distribution of SMEs Across Categories
  • Figure 3: Key factors to evaluate for new AI implementation
  • Figure 4: Framework for AI implementation in SMEs