AI and SMEs: Challenges and opportunities in the digital era

The research highlights significant gaps in digitalization and AI adoption among SMEs. Most SMEs rely on foundational tools like websites and spreadsheets, with only 13.9% utilizing AI for business processes and a mere 11% familiar with generative AI. High costs, lack of expertise, and resistance to change emerged as primary obstacles, compounded by limited access to quality data and outdated infrastructure.

In the contemporary digital age, artificial intelligence (AI) is a driving force behind innovation, efficiency, and competitiveness across various industries. From automating routine tasks to enhancing decision-making processes, AI offers unprecedented opportunities for businesses. However, while large corporations have embraced AI to transform their operations, small and medium-sized enterprises (SMEs) often face significant barriers in adoption. Limited resources, a lack of technical expertise, and resistance to change hinder their ability to harness AI’s potential. These challenges are particularly pronounced in Italy, where SMEs form the backbone of the economy but often operate within constrained environments.

Recognizing this critical gap, the research titled "Assessing AI Adoption and Digitalization in SMEs: A Framework for Implementation" by Serena Proietti (University of Rome Tor Vergata) and Roberto Magnani (TOPForGrowth), published in 2025, investigates the landscape of AI integration within Italian SMEs. By analyzing the state of digital transformation across 36 SMEs spanning 14 industries, the study identifies critical barriers and drivers of AI adoption. The authors propose a comprehensive framework to address these challenges and enable successful implementation.

Artificial intelligence (AI) has emerged as a transformative technology, revolutionizing processes and decision-making across industries. Despite its benefits, SMEs lag behind larger corporations in AI adoption due to limited resources, data availability, and expertise. The European Union’s definition of SMEs, which considers metrics like employee count and turnover, highlights the variability in organizational size and capacity, further complicating AI implementation. Addressing these challenges, Proietti and Magnani aim to provide actionable insights through this study.

The research focuses on two key questions: What is the current state of AI and digitalization among SMEs in Italy? How can SMEs effectively adopt AI using a structured framework? By examining these questions, the study equips managers and decision-makers with strategies to enhance AI integration and leverage digital transformation.

Methodology and findings

The study employed a mixed-methods approach, combining quantitative and qualitative data collection. Surveys were conducted across 36 SMEs in sectors such as commerce, IT, and metalworking. Questions covered digital maturity, barriers to AI adoption, and perceptions of AI’s impact.

Based on the responses, the researchers categorized digital maturity into four levels. Very Low Maturity represents minimal use of digital tools and no AI adoption, while Low Maturity involves basic digital tools with limited knowledge of AI. Medium Maturity indicates the presence of some advanced tools and moderate AI usage, and High Maturity signifies the use of advanced tools with strategic AI integration. Notably, none of the SMEs achieved high maturity, with the majority demonstrating limited AI understanding and adoption.

The research highlights significant gaps in digitalization and AI adoption among SMEs. Most SMEs rely on foundational tools like websites and spreadsheets, with only 13.9% utilizing AI for business processes and a mere 11% familiar with generative AI. High costs, lack of expertise, and resistance to change emerged as primary obstacles, compounded by limited access to quality data and outdated infrastructure.

Generative AI, while underutilized, presents substantial opportunities for automating tasks, enhancing customer communication, and content creation. However, traditional work cultures and fear of job displacement continue to impede technological transitions, underscoring the need for human-centered AI strategies.

Framework for AI implementation

The study proposes a multi-dimensional framework tailored to the unique challenges of SMEs. Objective setting is essential, requiring SMEs to establish clear, realistic goals using the SMART criteria - Specific, Measurable, Achievable, Relevant, and Time-bound. Iterative milestones help sustain motivation and focus. A human-centric approach is equally crucial, emphasizing employee involvement, upskilling, and fostering a culture of adaptability to overcome resistance.

Leadership commitment plays a pivotal role in driving change. External support from nonprofit organizations, state funding, and AI mentors or consultants can bridge resource and knowledge gaps. Technical enablers, such as data quality and infrastructure, are critical. SMEs should explore cloud-based AI solutions and proof-of-concept projects to incrementally introduce AI capabilities.

Discussion and implications

This research provides a foundational understanding of AI adoption in Italian SMEs, offering insights into current challenges and potential pathways for progress. However, the limited sample size underscores the need for broader studies. Future research should explore cross-country comparisons and sector-specific strategies, leveraging best practices to refine the proposed framework.

The adoption of AI, as Proietti and Magnani argue, is not merely a technological shift but a strategic imperative for SMEs. By addressing cultural, financial, and technical barriers, SMEs can unlock significant competitive advantages, ensuring resilience in an increasingly digital economy. The framework serves as a roadmap, guiding SMEs toward successful integration and sustainable growth.