Fibreglass and steel: De-imagineering AI value claims for Danish SMEs

This study investigates the relationship between artificial intelligence (AI) adoption, knowledge sharing, and employee performance in Malaysian small and medium-sized enterprises (SMEs), with knowledge sharing examined as a mediating mechanism. SMEs represent a vital component of Malaysia’s economy, yet many face resource limitations that affect their readiness to fully harness AI technologies. While AI is recognized for its potential to enhance efficiency and innovation, its impact on employee performance is not always straightforward. This research therefore, explores whether knowledge sharing acts as the bridge through which AI adoption translates into performance outcomes. Data were collected through a survey of SME employees across service and manufacturing sectors, and the responses were analysed using partial least squares structural equation modelling (PLS-SEM). Measurement model results confirmed strong reliability and validity for the constructs of AI adoption, knowledge sharing, and employee performance. Structural model assessment revealed that AI adoption significantly and positively influences knowledge sharing but does not directly affect employee performance. Meanwhile, knowledge sharing revealed a strong and significant relationship with employee performance and was also found to partially mediate the relationship between AI adoption and performance. The study findings highlight that AI’s value in SMEs lies not in the technology itself but in its ability to foster knowledge exchange, learning, and collaboration. In addition, employee performance improves when AI is embedded into organizational practices that encourage knowledge sharing, thereby complementing human creativity and expertise. Theoretically, this study integrated the Knowledge-Based View (KBV) and the Technology–Organization–Environment (TOE) framework to explain how AI adoption and knowledge sharing practices together influence employee performance. Practically, the results underscore the need for SME leaders to move beyond technology acquisition and focus on building collaborative cultures that enable knowledge sharing. Overall, this research contributes both theoretical and practical insights into how SMEs can strategically leverage AI adoption to enhance employee performance through the mediating mechanism of knowledge sharing.

This study investigates how artificial intelligence (A.I.) influences digital marketing strategies for small and medium-sized enterprises (SMEs) in local markets. Study investigates the effect of artificial intelligence (AI) tools on enhancing sales process efficiency in small and medium-sized enterprises (SMEs). Statistical analyses, including ANOVA and regression, reveal a strong positive relationship between AI adoption and improved sales efficiency. The findings offer valuable insights for SMEs aiming to gain a competitive edge through AI, with a recommendation for further research into specific AI applications that optimize efficiency and drive business growth. Through a literature review, we examine the integration of AI technologies into SMEs' digital marketing efforts and assess the opportunities and challenges associated with AI adoption in this sector. Our findings reveal the evolving landscape of digital marketing strategies among local SMEs, highlighting trends, techniques, and implications of A.I. implementation. We explore A.I.'s role in transforming digital marketing practices, including personalized content delivery, predictive analytics, and automated customer service. Moreover, our analysis sheds light on SMEs' unique challenges when adopting A.I. technologies, such as resource limitations, skill gaps, and data privacy issues. Despite these hurdles, SMEs acknowledge A.I.'s importance in enhancing their digital presence and driving business growth in today's competitive digital marketplace. Our research aims to inform strategic decision-making and empower SMEs to capitalize on the opportunities A.I. presents in the digital age. Ultimately, we hope to contribute to a more nuanced understanding of how A.I. can be a game-changer for SMEs striving to thrive in an increasingly digital world.

The recent surge in the adoption of artificial intelligence (AI) by small and medium-sized enterprises (SMEs) has garnered significant research attention. However, the existing literature reveals a fragmented landscape that hinders our understanding of how SMEs use AI. We address this through a systematic literature review wherein we analyze 106 peer-reviewed articles on AI adoption in SMEs and categorize states and trends into eight clusters: (1) compatibility, (2) infrastructure, (3) knowledge, (4) resources, (5) culture, (6) competition, (7) regulation, and (8) ecosystem: according to the technology–organization–environment model. Our research provides valuable insights and identifies significant gaps in existing literature, notably overlooking trends identification as a pivotal driver and neglecting legal requirements. Our study clarifies AI implementation within SMEs, offering a holistic and theoretically grounded perspective to empower researchers and practitioners to facilitate more effective adoption and application of AI within the SME sector.

This study delves into the factors influencing the adoption of Artificial Intelligence (AI) in e-commerce by small and medium-sized enterprises (SMEs) in Shandong Province, China. Leveraging a quantitative approach with a 300-questionnaire survey among 305 SMEs, it uncovers that technological, organizational, and environmental factors play crucial roles. Technological factors, such as perceived ease of use and usefulness, exert the strongest influence on SMEs' attitude towards AI adoption. Adequate financial resources, skilled personnel, and top management support within organizations enhance the likelihood of AI adoption. External pressures like competition and customer demand also drive SMEs to consider AI. However, SMEs face challenges, including high costs and skills shortages, despite a generally positive attitude towards AI. The research findings offer practical implications for SMEs, policymakers, and industry stakeholders. SMEs can integrate user-friendly AI solutions, explore cost-effective options like AI-as-a-Service (AIaaS), and seek government financial incentives. Policymakers can enhance financial incentives, collaborate with educational institutions to address skills shortages, and streamline data regulations. Industry stakeholders, such as technology providers and industry associations, can develop more suitable AI solutions and promote best practices and industry standards. Future research should focus on long-term impact studies, the influence of emerging technologies like generative AI, improvement of data collection methodologies, and in-depth exploration of factor interactions to further understand and promote AI adoption in Shandong's e- commerce SMEs.

Small and medium-sized enterprises (SMEs) face significant challenges in financial decision-making due to resource constraints, limited access to capital, and unpredictable cash flow. To overcome these challenges, many SMEs are turning to artificial intelligence (AI) to improve efficiency and profitability. AI, through tools like machine learning and predictive analytics, has transformed how SMEs manage their finances, offering solutions that enhance accuracy, speed, and data-driven decision-making. AI-driven accounting tools are revolutionizing routine tasks such as bookkeeping, invoicing, and expense tracking. By automating these functions, SMEs can reduce human error, save time, and allocate resources more effectively. AI’s ability to integrate real-time data ensures that financial records remain up to date, enabling more accurate cash flow forecasting. For example, AI can predict liquidity needs based on historical data, seasonal trends, and market conditions, helping SMEs maintain healthy cash flow and avoid financial shortfalls. In addition to improving efficiency, AI plays a crucial role in optimizing revenue and profitability. AI-based pricing models allow SMEs to adjust prices dynamically, responding to market demand and competitor behavior in real-time. This data-driven approach ensures that businesses maximize revenue without compromising customer satisfaction. Moreover, AI helps SMEs identify high-value customers by analyzing purchasing patterns, preferences, and behaviors. By focusing on customer segmentation and tailored marketing strategies, SMEs can boost sales and customer retention. Despite the clear benefits, AI adoption in SMEs is not without challenges. High implementation costs, limited technical expertise, and concerns over data privacy can hinder the integration of AI tools. However, scalable, cost-effective AI solutions are becoming increasingly available, making it easier for SMEs to incorporate AI into their financial processes. As AI continues to evolve, it will play an even more significant role in helping SMEs navigate financial complexities, improve decision-making, and enhance long-term profitability.

This study investigates the impact of Artificial Intelligence (AI) adoption on the sustainable performance of small and medium-sized enterprises (SMEs). Employing a hybrid quantitative approach, this research combines Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANN) to examine the influence of various organizational, technological, and external factors on AI adoption. Key factors considered include top management support, employee capability, customer pressure, complexity, vendor support, and relative advantage. Data collected from 305 SMEs across multiple sectors were analyzed. The results reveal that all the proposed factors significantly and positively affect AI adoption, with top management support, employee capability, and relative advantage being the most influential predictors. Additionally, the adoption of AI technologies substantially enhances the economic, social, and environmental performance of SMEs, reflecting improvements in operational efficiency, cost reduction, and social value creation. The ANN results confirm the robustness of the SEM findings, highlighting the critical role of AI in driving sustainability outcomes. Furthermore, the study emphasizes the positive mediation effects of AI adoption on organizational performance, indicating that AI adoption serves as a key enabler in achieving both short-term operational gains and long-term sustainability objectives. This research contributes to the understanding of AI’s transformative role in enhancing the sustainable performance of SMEs in developing economies, offering strategic insights for both policymakers and business leaders.

This paper empirically examines factors and firm-level determinants of artificial intelligence (AI) adoption. It is particularly focused on factors related to missing skills in European small and medium-sized enterprises (SMEs). It uses micro-level data from Flash Eurobarometer 537, which includes more than 19,000 firms from 27 EU and 9 non-EU countries. The aim was to identify the key characteristics associated with the likelihood of AI adoption and the anticipation of significant skill transformations related to AI adoption. Results of probit and bivariate probit regressions reveal that firms with younger employees, growing turnover, and membership in industry clusters are more likely to adopt AI. Moreover, firms that have problems with shortages of skills in specific areas (such as IT, R&D, marketing, and customer care) are more likely to adopt AI. These firms also expect that AI adoption will significantly change their skill demands in the future. Interestingly, community size and difficulties with recruiting are closely related to the adoption of AI but have limited effect on firms' expectations on its impact on skills demanded in the future. Furthermore, the analysis revealed significant differences across industries. These findings contribute to the emerging literature on digital transformation and expand the knowledge on the decision-making process for AI introduction by clarifying the drivers of AI adoption and its perceived skill implications.

In an era of rapid digital transformation, small and medium-sized enterprises (SMEs) are increasingly leveraging artificial intelligence (AI) and predictive analytics to enhance operational efficiency, optimize supply chain management, and improve overall business performance. This study explores the integration of AI-driven solutions in SMEs, focusing on their impact on demand forecasting, logistics optimization, and predictive maintenance. Using a mixed-methods approach, both quantitative and qualitative data were collected from 250 SMEs across various industries, supplemented by in-depth interviews with key decision-makers. The findings reveal that AI implementation leads to significant improvements in cost reduction (30% in logistics), operational efficiency (20% in inventory turnover), and predictive accuracy (15% improvement in maintenance planning). Despite these benefits, SMEs face challenges such as high initial investment costs, lack of AI expertise, and data management complexities. The study identifies best practices for overcoming these barriers, including phased AI adoption, strategic partnerships, and employee training programs. Additionally, the research highlights sector-specific AI applications, demonstrating its effectiveness in retail, e-commerce, and manufacturing industries. This study contributes to the growing discourse on AI adoption in SMEs by providing empirical evidence of its role in business optimization. It underscores the necessity of integrating AI into SME operations to remain competitive in a data-driven economy. The findings offer valuable insights for business leaders, policymakers, and technology providers, advocating for the development of AI frameworks that align with SME scalability and resource constraints. Future research should explore AI’s long-term impact on financial performance and sustainability, as well as its potential applications in emerging markets

Purpose This paper investigates the adoption of Artificial Intelligence (AI) by Small and Medium Enterprises (SMEs) in Pakistan and explores its potential economic implications at both organizational and national levels. Design/Methodology/Approach The study is based on survey data collected from 368 SMEs across multiple industries. It examines adoption patterns, levels of investment, perceived benefits, and the barriers hindering widespread integration of AI. Findings Results reveal that nearly two-thirds of SMEs have already adopted AI, with the highest application observed in marketing, sales, and customer service, while adoption in HR and product development remains limited. Efficiency, cost reduction, and improved decision-making emerged as the primary drivers for adoption. However, challenges such as high implementation costs, skill shortages, limited awareness, and resistance to change continue to impede broader integration. The analysis further demonstrates a strong positive association between AI adoption, productivity gains, and revenue growth, which collectively enhance organizational competitiveness and contribute to national economic development. Practical Implications The findings highlight the need for targeted policy support, educational initiatives, and financial incentives to enable SMEs to overcome structural and resource-related barriers. These measures are critical for accelerating AI integration, strengthening SME competitiveness, and fostering sustainable digital transformation. Originality/Value This study contributes to the limited body of empirical research on AI adoption among SMEs in developing economies, particularly within the Pakistani context. By offering insights into adoption patterns, challenges, and economic outcomes, it provides valuable guidance for policymakers, entrepreneurs, and industry stakeholders in advancing technology-driven growth.

This review paper investigates the potential of Artificial Intelligence (AI) solutions to drive innovation within Small and Medium-sized Enterprises (SMEs), addressing adoption barriers and exploring future growth opportunities. The primary objective is to synthesize existing literature on AI applications in SMEs, identifying the benefits, challenges, and strategies for successful implementation. The paper highlights that AI technologies can significantly enhance operational efficiency, product development, customer engagement, and competitive advantage for SMEs. Despite these benefits, several barriers hinder widespread AI adoption, including limited financial resources, lack of technical expertise, resistance to change, and concerns about data security and privacy. By reviewing various case studies and research findings, the paper identifies key strategies to overcome these challenges. These strategies include government incentives, public-private partnerships, affordable AI-as-a-Service models, and targeted training programs to build AI competencies within SMEs. The importance of fostering a supportive ecosystem with robust infrastructure, favorable regulatory frameworks, and access to funding is emphasized. The paper concludes that AI has the potential to revolutionize SMEs by enabling rapid and efficient innovation. However, realizing this potential requires concerted efforts from multiple stakeholders to address adoption barriers and create an enabling environment for AI-driven growth. Future research should focus on developing frameworks for scalable AI implementation tailored to the unique needs of SMEs and tracking the long-term impact of AI adoption. This review provides a comprehensive understanding of the current state of AI in SMEs, offering insights into overcoming challenges and capitalizing on future opportunities for growth and innovation.

Small and medium-sized enterprises (SMEs), despite constituting over 90% of global businesses, face persistent financial management challenges, including resource constraints, operational inefficiencies, and vulnerability to market volatility. While artificial intelligence (AI) offers transformative potential for risk mitigation, cash flow optimization, and sustainable growth, existing research disproportionately focuses on large enterprises, leaving SME-specific barriers and opportunities underexplored. This study addresses this gap by examining how AI adoption reshapes SME financial practices, with a dual focus on technological empowerment and systemic inequities. Employing a mixed-methods design, the research synthesizes findings from a systematic literature review (2020–2024) and primary data collected via surveys (n=300 SMEs) and interviews (n=30 managers) across North America, Europe, and Asia-Pacific. Quantitative analysis using structural equation modelling (SEM) and qualitative thematic analysis reveals that AI tools like ChatGPT and feedforward neural networks enhance risk assessment accuracy by 30–40% and reduce fraud detection time by 90%, while blockchain-AI integrations improve transactional transparency. However, data scarcity (reported by 68% of SMEs), infrastructure costs (57%), and skill gaps (63%) disproportionately hinder adoption, particularly in emerging markets. Theoretically, the study extends the Technology–Organization–Environment (TOE) model by emphasizing dynamic organizational learning and ethical governance as critical drivers of AI integration. Practically, it advocates for phased AI implementation strategies, starting with low-cost tools (e.g., generative AI for forecasting) and progressing to advanced systems, complemented by stakeholder training and policy interventions to subsidize infrastructure. The findings underscore AI’s dual role as an enabler of financial resilience and a catalyst for structural inequities, urging policymakers to establish regulatory sandboxes and ethical frameworks to democratize access. This research bridges theoretical gaps in SME-centric AI adoption literature while offering actionable pathways for SMEs to harness AI’s potential, fostering equitable and sustainable growth in the digital economy.

Analyzing AI adoption in European SMEs: A study of digital capabilities, innovation, and external environment

Despite the transformative potential of artificial intelligence (AI), small and medium-sized enterprises (SMEs) continue to face significant challenges in its effective adoption. While prior studies have emphasized strategic benefits and readiness models, there remains a lack of operational guidance tailored to SME realities—particularly regarding implementation barriers, resource constraints, and emerging demands for responsible AI use. This study presents an analysis of AI adoption in SMEs by integrating the technology–organization–environment (TOE) framework with selected attributes from the diffusion of innovations (DOI) theory to examine adoption dynamics through a dual structural and perceptual lens. Empirical insights from sectoral and regional contexts are also incorporated. Ten critical challenges are identified and analyzed across the TOE dimensions, ranging from data access and skill shortages to cultural resistance, infrastructure limitations, and weak governance practices. Notably, the framework is expanded to incorporate responsible AI governance and democratized access to generative AI—particularly open-weight large language models (LLMs) such as LLaMA, DeepSeek-R1, Mistral, and FALCON—as emerging technological and ethical imperatives. Each challenge is paired with actionable, context-sensitive solutions. The paper is a structured, literature-based conceptual analysis enriched by empirical case study insights. As a key contribution, it introduces a structured, six-phase roadmap methodology to guide SMEs through AI adoption—offering step-by-step recommendations aligned with technological, organizational, and strategic readiness. While this roadmap is conceptual and has yet to be validated through field data, it sets a foundation for future diagnostic tools and practical assessments. The resulting study bridges theoretical insight and implementation strategy—empowering inclusive, responsible, and scalable AI transformation in SMEs. By offering both analytical clarity and practical relevance, this study contributes to a more grounded understanding of AI integration and calls for policies, ecosystems, and leadership models that support SMEs in adopting AI not merely as a tool, but as a strategic enabler of sustainable and inclusive innovation.

Purpose: This study aimed to analyze the influence of trust on the acceptance of artificial intelligence (AI) technologies among small and medium-sized enterprises (SMEs). Based on the modified Technology Acceptance Model (TAM), the relationships between trust and perceived usefulness, ease of use, attitude, and implementation intention were examined. Need for the study: In dynamic digital transformation, SMEs face limited resources, low technological competence, and uncertainty about AI's ethical and functional aspects. Previous studies have rarely considered trust as a central factor in AI adoption models in the SME sector. Filling this research gap became the main motivator of this project. Methodology: A quantitative research method based on structural equation modeling (SEM) was used, using a five-point Likert scale questionnaire. Data were collected from the management staff of SMEs in Poland. The relationships between constructs were analyzed using confirmatory factor analysis (CFA) and model fit indices. Findings: The results confirmed the significant influence of trust on perceived ease of use, usefulness, and attitude towards AI and indirectly on implementation intention. Trust was a key factor supporting the positive perception and acceptance of AI technologies in the SME environment. The model achieved high construct validity and a good fit to empirical data. Practical Implications: The study suggests strengthening trust in AI – through algorithm transparency, user education, certification, and ethical frameworks – should be essential to SMEs' digital transformation strategies. The results provide practical guidance for decision-makers, policymakers, and technology providers on effectively supporting AI adoption in the SME sector.

Purpose This study aims to investigate the antecedents of artificial intelligence (AI) adoption and sustainable performance (SP) in the context of manufacturing small and medium-sized enterprises (SMEs) in Jordan. Design/methodology/approach To fill the gap in the literature and achieve the research work objectives, the technology–organization–environment (TOE) framework and the partial least squares structural equation modeling (PLS-SEM) approach were used to analyze the sample data collected (n = 190) from manufacturing SMEs. Findings The findings indicate that technological, organizational and environmental context significantly influence the manufacturing SMEs’ AI adoption. Furthermore, AI adoption positively associated with economic, environmental and social performance. Originality/value This paper explains the TOE theory in an innovative technological setting. The paper also provides knowledge about how AI adoption may influence sustainable performance. Overall, this study provides new investigation into the literature concerning AI technologies and sustainable performance through the TOE lens.