Mathematical modeling of complex problems, with particular attention to multi-criteria and optimization models, is particularly effective for analyzing critical issues in the agri-food supply chain. These tools allow analyzing problems of different nature through the use of qualitative and quantitative criteria, often conflicting with each other, and decision alternatives. Their versatility allows adapting solid theoretical constructs to different contexts and application areas, ensuring a modeling that takes into account the peculiarities of the problem under examination. In fact, multi-criteria analysis tools and optimization models allow supporting decision makers in the different phases that characterize the agri-food supply chain. This work addresses in particular the decision problem related to the transition of agri-food products from the storage to the marketing phase, using the Analytic Hierarchy Process (AHP) method. Once the products have undergone transformation processes, they are stored in one or more warehouses for distribution to commercial activities. There are several choices to be made in this phase, including the choice of the commercial establishments in which to distribute the products. In particular, the use of the AHP method is proposed for the analysis of a decision problem relating to the choice of the warehouse distribution with respect to four different commercial establishments (alternatives) that sell directly to the final consumer. A sensitivity analysis is conducted to test the robustness of the method. Through the analysis of a practical case, it is highlighted how the agri-food supply chain is a very complex context, in which the competitiveness of organizations is based on the ability of the decision maker to identify problems, imagine alternatives and adopt solutions. It is also highlighted that these tools, if correctly implemented in organizations, allow the start of an organizational learning process, reducing the costs associated with decision-making errors.

The traceability of product sourcing and production processes has become a crucial need for the Agri-food Industry, where stakeholders – particularly consumers – increasingly demand guarantees on product quality and authenticity. Blockchain technology, given its characteristics of immutability and transparency, can play a key role in solving issues related to traceability and information asymmetry, which undermine security and stakeholder trust in companies, as well as in promoting corporate social sustainability. The present study aims to fill a gap in literature by focusing on the operational application of this technology, with particular attention to the issue of credibility of the raw data entered into the system. To this end, a qualitative approach is adopted based on a multiple case study conducted through secondary data, integrated with primary data collected through a focus group. The work has a twofold objective: on the one hand, to explore the motives and goals that drive companies to implement Blockchain technology; on the other hand, to identify the preliminary conditions that ensure its operational effectiveness and credibility in the eyes of stakeholders. The results suggest that traceability is the main reason why companies decide to implement Blockchain technology, with the goal of ensuring product authenticity and transparency to the consumer. In addition, two sets of factors – operational and validation – were identified as key preconditions for effectively implementing a credible Blockchain information system. The study contributes to a deeper understanding of the operational application of Blockchain in the agri-food sector, providing relevant theoretical and practical implications.

The agri-food industry is currently facing significant global challenges, including climate change, resource depletion, and growing demands for food security. In response to these challenges, sustainable innovation has become a critical focus for the industry. Sustainable innovation refers to the development and adoption of new technologies, practices, and business models that aim to improve environmental, social, and economic outcomes. One of the most promising technologies for supporting sustainable innovation is artificial intelligence (AI), which has the potential to enhance various aspects of sustainability within the agri-food sector. This study explores the role of AI in supporting sustainable innovation strategies and practices in the agri-food industry, with a focus on understanding how AI can help optimize processes, reduce waste, improve resource management, and foster more sustainable business models.
To achieve this, a comprehensive systematic literature review was conducted, analyzing seventy-three peer-reviewed articles related to AI applications in the agri-food industry. Through this analysis, the study identifies six main clusters of strategies that are currently being employed to drive sustainability in the sector. These strategies include optimizing production processes, improving supply chain efficiency, enhancing product traceability, and advancing precision agriculture practices. Additionally, the study highlights the role of AI in supporting twenty-four specific practices within these strategies, such as predictive analytics for crop yield forecasting, AI-driven waste reduction systems, and AI-based monitoring tools for environmental impacts.
However, the findings also reveal significant gaps in the application of AI, particularly the lack of support for circular economy approaches, which focus on reducing waste and reusing resources throughout the supply chain. This gap presents an opportunity for further research into how AI can be leveraged to support circular practices within the agri-food industry. The study provides a foundation for future exploration into the integration of AI and sustainable innovation, offering insights for both researchers and practitioners seeking to improve sustainability in the agri-food industry.

In the era of Industry 5.0, artificial intelligence offers new opportunities for co-creation, personalization, collaboration and integration. These affect companies’ Business Model Innovation strategies, supporting progress in the interactions between sets of actors, populations, activities, institutions and networks impacting the entire innovation ecosystems. Positioning itself in the research stream focused on analysing the impact generated by emerging technologies in the innovation of business ecosystems, this study aims of discussing the impact generated by artificial intelligence in business model innovation, reflecting on the salience of the involved stakeholders. A three-phase methodological approach was used, integrating a systematic literature review with a case study analysis. The related findings were discussed according the stakeholder theory basing on the salience attributes of power, legitimacy and urgency. This approach allowed us to establish a strong theoretical foundation while anchoring our findings in real-world example. Seven artificial intelligence-based value creation innovations were identified, demonstrating how artificial intelligence-based applications are transforming value creation mechanisms. Findings indicate that artificial intelligence has the potential to generate significant added value, primarily by enhancing value creation through data exploitation and the high degree of services customization tailored to customer needs. Moreover, new value streams emerge from artificial intelligence-based applications, driven by the widespread adoption of the technology across innovation ecosystems. This diffusion influences and engages stakeholders at every layer of the ecosystem’s structure. Stakeholders are classified as Discretionary (Standards Bodies, Public Bodies), Demanding (Distribution Channels, Research Institutes), Dangerous (other suppliers), Dependent (Suppliers), Definitive (Core Organization, Customers, Complementors). Notably, no stakeholders were identified that align with the Dormant or Dominant categories.

Previous studies have highlighted the numerous benefits of artificial intelligence (AI) models in enhancing the management of organizational knowledge assets. However, the adoption of AI in organizational settings often encounters significant barriers that hinder its optimal implementation. This paper presents preliminary findings from a timely study that uniquely focuses on the perceived obstacles at the intersection of AI and knowledge management (KM) in organizations. Our objective was to understand the perspectives of employees and managers regarding four dimensions of barriers and challenges in integrating AI technologies into knowledge management systems (KMSs) in business: human, technological, financial, and ethical-regulatory. A voluntary and anonymous online questionnaire was completed by 378 respondents from various industries. The results reveal that financial barriers were reported to be the least significant by both regular employees and managers. Instead, nearly half of the participants expressed concerns about technical barriers, particularly the inadequacy of their organizations’ technological infrastructure to support AI applications effectively. A significant percentage of 82.28% of the sample mentioned organizational barriers, specifically noting that employees lack the necessary skills to leverage AI for enhancing organizational KM. Furthermore, nine out of ten respondents indicated that a substantial cultural shift is essential for facilitating AI adoption within their organizations. Concerns about the potential leakage of sensitive information due to AI usage were significant, with approximately two-thirds of respondents highlighting this issue. Additional ethical barriers were prominent, with three out of four participants reporting a lack of clear organizational procedures to ensure information security and privacy in AI applications. These findings have significant theoretical and practical implications for the discipline of KM in general, and for KMSs in particular. These findings lay a fertile ground for future empirical investigations into the relationship between AI and KM.

Knowledge is one of the most valuable assets in modern organizations. However, its intangible and evolving nature makes it challenging to capture and leverage. Knowledge management (KM) is the organized, systematic process that acquires, refines, organizes, and applies this knowledge to improve organizational success and secure long-term competitive advantage. As data volume and complexity continue to grow, conventional systems have proven insufficient to harness the full potential of knowledge assets. As a result, emerging technologies have become indispensable, providing agile, scalable, and intelligent solutions. However, much of the existing research often examines these technologies in isolation, considering artificial intelligence (AI) and others as standalone entities, failing to reveal the synergies that emerge from combining them. This limitation is further complicated by the nature of common research approaches: literature reviews, while offering broad theoretical insights, are typically too abstract to inform practical application; meanwhile, case studies, though grounded in real-world scenarios, often lack generalizability due to their strong context-specific focus. This study addresses these gaps by investigating how AI and complementary, emerging technologies redefine KM areas. Drawing on 1,487 documented business practices—comprising case studies, pilot projects, and simulation models—derived from literature, this research focuses on five critical KM phases: knowledge creation, acquisition, organization, transfer, and application. Conceptually, knowledge is created or acquired, then captured, organized, and preserved for the long term, transferred to those who need it, and finally applied to produce value. Finally, these practices are analyzed through association analysis via Cramér’s V to quantify the strength of relationships between AI (whether used alone or alongside other technologies), business functions and the different KM areas. Theoretically, the findings advance KM research by demonstrating which technological combinations are more effective in each KM phase. From a managerial perspective, the emerging practices examined in this study offer real-world examples of how these integrated solutions can be successfully deployed, allowing managers to draw from documented practices rather than starting from scratch.

This paper examines the transformative impact of AI-enhanced data platforms on knowledge management (KM) within waste management organizations, focusing on the case of Alia Servizi Ambientali SpA in Tuscany, Italy. Utilizing a qualitative case study approach that combines on-site observations, stakeholder interviews, and system data analysis, our research demonstrates that AI integration significantly optimizes operational efficiency by consolidating diverse data streams, from IoT sensors monitoring waste receptacles to vehicle fleet metrics, into a unified, high-quality repository. The platform employs a medallion architecture to ensure data quality, enabling predictive analytics that improve route optimization, reduce vehicle movements, and lower carbon emissions. Beyond these practical benefits, the study advances theoretical insights by proposing a framework that situates AI-driven KM within broader governance and ethical contexts, contrasting traditional approaches with the dynamic capabilities of AI technologies. Despite the inherent limitations of a single-case design, our findings provide a strategic blueprint for leveraging AI-enhanced data platforms in waste management. They underscore the critical role of robust governance frameworks, leadership commitment, and targeted training in aligning technological capabilities with evolving KM practices, ensuring the sustainability and scalability of digital transformation initiatives.

Efficiency and value creation for citizens, communities, and societies are becoming increasingly important for the public sector. Public sector is continuously seeking for innovative ways to create and provide services, and despite acknowledging the key role of knowledge in innovation processes, the relationship between publicly available knowledge and innovation is still poorly understood. A more unified view of OI and the public sector originated knowledge ecosystem, alongside public-private collaboration, is crucial. The private sector’s creation of open knowledge-based innovations, such as products or services, for private markets appears to have minimal influence. To achieve successful knowledge-based innovation, functional systems are crucial for addressing the identified barriers within public knowledge and innovation. This paper investigates the potential of public sector originated knowledge to drive open innovation in partnership with public and private organizations. The potential to gather and use openly available knowledge is now greater than ever due to the possibilities provided by AI. Challenges in innovation are widely explored in existing research. Future studies should focus on developing models for successful open knowledge initiatives, including strategic planning (value realization and resource-based strategic analysis), technical enablers (new digital technologies as AI), sharing platforms (design of public knowledge sources with appropriate APIs), enabling functional public knowledge ecosystems, and legal frameworks to support open public knowledge sharing and utilization (e.g., knowledge privacy). Research needs to consider social aspects alongside technological and business ones. This includes management models for public knowledge and innovation, knowledge processes and its management to support OI, and organizational cultures that reflect experiences of control or safety, risk management, attitudes and engagement in OI processes. To better comprehend how the private sector can benefit from public knowledge, additional empirical studies are crucial.

The goal of the proposed paper is to provide a preliminary Artificial Intelligence (AI) approach addressing the analysis on the prevention of diabetes. The study is performed within the framework of the research project Telediabetolab and focuses on the check of the supervised Support Vector machine (SVM) algorithm to define a method to perform a data processing analysis based on predicted diabetes risk. The preliminary study highlights the possibility of defining a multi-parametric approach to prevent the chronic condition of diabetes by considering the pre-diabetes condition. The SVM data processing is executed by analyzing different parameters such as blood pressure, body weight, glucose, and blood ones. The predicted results show that there could be cases of diabetics who might not be diabetic and cases of non-risk that could degenerate.

The purpose of this research is to examine transformational leadership impacts open innovation through the mediating role of commitment-based human resource management practices. A quantitative survey was conducted to collect data from a sample of 169 SMEs. The findings indicate a considerable positive correlation between transformational leadership and inbound open innovation in SMEs, as demonstrated by the quantitative survey analysis conducted with SmartPLS version 4.1.0.9. Furthermore, commitment-based human resource management methods work as a significant mediator between transformational leadership towards and innovation and the acquisition of knowledge within a company, referred to as inbound open innovation. The results underscore the imperative of developing organizational leadership that promotes change, creativity, trust, and employee commitment to enhance organizational openness. Open innovation is a recognized organizational approach that promotes transformation, enhances the competitiveness of SMEs, and stimulates overall economic growth. The results compel policymakers and decision makers to concentrate more on a transformational leadership style that can facilitate transformation and boost employee loyalty to the firm. Consequently, a culture of openness will be fostered, resulting in a more favorable impact on the performance of SMEs.

In recent years, growing societal attention to gender bias and inequality has prompted Universities to reconsider their institutional practices, including the use of inclusive language in formal communication. As central actors in the production and dissemination of knowledge, academic institutions bear a cultural responsibility to ensure that their communication practices reflect principles of equity and inclusion. This study explores the extent to which written communications addressed to students, either by university faculty or generated by artificial intelligence (AI), adhere to inclusive language practices.
The research adopts a qualitative design based on content analysis and evaluates 63 announcements authored by faculty members from two Italian universities, alongside 39 messages produced by three generative AI systems—ChatGPT Basic, Deepseek, and Gemini 2.0 Flash. The analysis is guided by 16 indicators of inclusive language use, drawn from the guidelines developed by Sabatini (1987) and revised by Somma and Maestri (2020). Each message is classified as inclusive, non-inclusive, or partially inclusive, based on the presence or absence of these indicators.
Findings show that inclusive communication practices are strongly gendered: female faculty members display a much higher rate of inclusive language use (48%) compared to their male colleagues (18%). Notably, no male full professor in the sample used inclusive language. Disciplinary differences also emerge, suggesting that the adoption of inclusive language is influenced by specific cultural norms within academic fields. Among AI systems, Gemini 2.0 Flash produced the highest share of inclusive outputs (38%), followed by ChatGPT (23%) and Deepseek (8%). However, the overall low performance of AI models indicates that inclusive principles are not yet systematically embedded in their training.
This paper contributes to the literature by combining human and AI communication analysis under a unified framework and offering empirical insights into how inclusive language is—or is not—implemented in practice. The results underscore the need for targeted interventions, both educational and technological, to promote inclusive communication in academia. By advancing this dual focus on human and AI-generated texts, the study highlights inclusivity not only as an ethical imperative, but also as a strategic priority for fostering institutional credibility and evolution.

Early-stage startups face unique challenges in Human Capital (HC) management, as traditional Human Resource Management (HRM) practices, designed for established firms, overlooking the unique organizational dynamics of nascent ventures. This exploratory qualitative case study examines HRM practices in early-stage startups through a cross-case analysis of eight cases: two each from France, Italy, and the United Kingdom, complemented by one case each from Switzerland and Tunisia. We conducted semi-structured interviews with eight startup founders and three early-hire employees, enabling comprehensive perspective triangulation through multiple stakeholder viewpoints within each case organization. Through the theoretical lenses of Resource-Based View (RBV) and contingency theory, this study examines how startups orchestrate their HR resources and adapt their management systems. The findings demonstrate the imperative for adaptive HRM strategies in early-stage startups’ dynamic environments during critical phases of hiring, onboarding, and retention. The study’s primary contribution is extending the startup HRM literature by introducing a flexible management of a fractional HR management model, which provides startups with a cost-effective, expertise-driven solution for managing essential HR functions, including talent acquisition, onboarding, development, and retention. The research advances both theoretical understanding of HRM in nascent ventures and provides actionable insights for startup founders in optimizing their HC management strategies within resource constraints, particularly during their founding years.

This paper aims to investigate how investment in artificial intelligence interacts with automation-related knowledge risks to shape the resilience of small- and medium-sized enterprises in the Italian context. The rapid advancement of digital transformation requires an extensive understanding of the link between innovative technologies, such as artificial intelligence, and the management of knowledge risks linked to automation, notably related to improving the competitive advantage of small- and medium-sized enterprises. This study employs knowledge risk theory to investigate how investments in artificial intelligence, alongside the challenges introduced by automation, collectively influence organizational resilience and innovation capacity. The empirical foundation of this research is based on secondary data collected across 20 Italian regions and various industries. The dataset was obtained from Confartigianato, a leading association of small- and medium-sized enterprises in Italy. The data were then analyzed using a quantitative approach. The results showed that areas with elevated automation-related knowledge risks are more inclined to invest in artificial intelligence, suggesting a reactive rather than proactive investment approach. The results also highlight varied geographical patterns related to investment levels, risk exposure, and digital readiness, with significant discrepancies in digital adoption and the critical role of knowledge risk as an intermediary factor in the link between artificial intelligence investment and organizational resilience, suggesting that beneficial outcomes are contingent on proficient risk management. This study aims to make a substantial contribution to the current literature on knowledge risk by investigating its application in the context of artificial intelligence and automation deployment in small- and medium-sized enterprises, thereby informing management and regulatory approaches.

Innovative work behaviour (IWB) may be crucial to enable middle managers of public healthcare organizations (PHOs) to effectively fulfil their hybrid role of doctor-managers. However, psychological and cognitive boundaries associated with role hybridization can inhibit IWB. To overcome these criticalities, knowledge should be dynamically managed within PHOs. Grounded in the social identity theory, the present study aimed to explore the development of knowledge transfer at the individual-level of analysis by investigating the influence of doctor-managers’ organizational identification (OI) on their IWB, through the indirect effect of satisfaction with the managerial role. A survey was administered to doctor-managers working in Italian PHOs. A linear regression analysis was performed to test the research design. The findings demonstrated that doctor-managers’ OI address a greater satisfaction with the managerial role; in turn, this latter positively influences doctor-managers’ IWB. This study provides new insights into the individual determinants of IWB in knowledge-intensive public organizations like PHOs. In doing so, it contributes to the ongoing discussion on the pivotal role of knowledge transfer dynamics as a precursor of IWB by unleashing the potential of OI. PHOs’ top management should regulate doctor-managers’ identity by providing an environment fostering the transfer of knowledge supporting them in performing their hybrid role. This may be achieved through managerial practices that facilitate vertical and horizontal integration and knowledge exchange.

This study proposes a systematic literature review aimed at analyzing three key thematic areas: the conceptual and operational adoption of new operational paradigms related to the use of technology in the business environment (Industry 5.0), sustainability strategies in new business models (sustainability) and the concept of well-being in the workplace. The study examined 21 scientific articles selected from the Scopus and Web of Science databases, published in the period 2020-2024, considering the following keywords: industry 5.0, sustainability and well-being. The results highlight the crucial role of advanced technologies – such as artificial intelligence, collaborative robotics and the Internet of Things – in fostering safer, more efficient and well-being-oriented production environments for workers, with a positive impact on the overall sustainability of industrial systems. The ‘Industry 5.0’ phase is an evolution of the previous Industry 4.0, shifting the focus from pure automation to the enhancement of human capital, the ethical integration of technologies and the promotion of human-machine cooperation. It moves towards a society that is based on the use of technology for a more human, inclusive and resilient society, where technology is at the service of human well-being (Society 5.0). Findings reveal that technology is configured not as an end, but as a means for the improvement of collective well-being and quality of life and lastly for production systems that evolve by placing the person at the center: the goal is to reach the ethical integration of technologies through the human-machine collaboration.

The adoption of artificial intelligence (AI) has become a strategic priority for organizations of all sizes, driven by pursuit of process optimization, efficiency gains, and enhanced decision-making. Yet, the effectiveness of AI depends not only on technological implementation but on how organizations integrate it with human-centric values –a challenge that remains underexplored in the context of corporate governance and innovation ecosystems.
This paper explores how corporate governance functions as an instrumental factor in institutionalizing human-centric AI adoption, comparing practices across large organizations, SMEs, and startups within the Rome Technopole (RT) innovation ecosystem. We argue that governance plays a critical role in fostering ethical AI adoption, yet human-centricity should not be treated merely as an afterthought. Instead, it must be a constitutive principle, embedded in the design of the innovation ecosystem itself. Using data from the Rome Technopole Observatory on AI, this exploratory study investigates how organizational size shapes AI strategies and perceived alignment with human-centric values. Findings reveal that structural changes (e.g., dedicated AI teams in large organizations) do not necessarily lead to more human-centric practices or perceptions that AI is aligned with human values. This underscores a critical gap: widespread AI adoption does not guarantee alignment with human-centric principles, which highlights the need for governance that fosters human-centricity at a foundational level.
To address this, based on Sangiovanni Vincentelli’s meet-in-the-middle principle, we outline the necessary conditions for developing an innovation ecosystem grounded in human-centric principles. Furthermore, we consider organizations as social, dynamic, and adaptable systems and the RT innovation ecosystem as a context-dependent ecosystem responsible for interactions between its organizations. This theoretical exploratory study paves the way for future empirical research into RT and others innovation ecosystems.
In conclusion, this study posits that corporate governance can be an instrumental mechanism for sustaining an innovation ecosystem in which human-centricity is constitutive. These insights are particularly relevant for organizations operating within collaborative innovation ecosystems, and the innovation ecosystem itself, as they emphasize the alignment between technological progress and societal values as a prerequisite for sustainable and integrative innovation.

In an era of rapid technological advancement, digital transformation has emerged as a critical driver reshaping human resource management in public sector organizations. While talent management has long been a cornerstone in the private sector, the public sector faces unique challenges—including bureaucratic rigidity, limited resources, and evolving workforce expectations—that complicate efforts to attract, retain, and develop skilled personnel. Today, talent management strategies must strike a balance between technological innovation and human-centric approaches that prioritize employee well-being, engagement, and ethical considerations. Despite growing scholarly interest, research on talent management in the context of digital transformation within the public sector remains fragmented across disciplines and methodological approaches.
To address this gap, the present study employs a bibliometric analysis, using the Bibliometrix R package, to systematically map the literature on digital transformation and talent management in public administration. Findings reveal a significant acceleration of academic output from 2020 onward, coinciding with the pandemic-driven digitalization of public services.
Thematic analysis highlights innovation, sustainable development, leadership, and perception as core emerging themes, alongside rising interest in AI and knowledge management. However, areas such as employee well-being, lifelong learning, and digital inclusion remain underexplored.
This paper offers a comprehensive overview of the evolving academic landscape, underscoring the need for future research to integrate both technological and human dimensions of talent management in the public sector. In doing so, it contributes to advancing a more resilient, agile, and inclusive public workforce capable of navigating the challenges of digital transformation while enhancing public value and citizen trust.

In recent years, Artificial Intelligence (AI) has gained significant attention across disciplines and industries, with the adoption of Generative Artificial Intelligence (GenAI) in organizations accelerating, particularly through the introduction of user-friendly conversational chatbots. While numerous studies have investigated either the factors influencing AI adoption within organizations or the effects of AI adoption on organizational performance, the role of human-centered factors in shaping its adoption and their subsequent impact on employee quality of working life remains largely unexplored. To address this gap, and drawing on socio-technical systems theory, this study proposes a theoretical model investigating the interplay between key human-centered factors, GenAI adoption, and employee quality of working life. Specifically, employee trust in GenAI is identified as a key human-centered antecedent of GenAI adoption. GenAI adoption is conceptualized through two main constructs: GenAI use and GenAI hedonic experience. Workplace well-being is used as the indicator of employee quality of working life. The model is tested using Partial Least Squares Structural Equation Modeling on survey data collected from 214 Italian professionals with experience in using GenAI in their work activities. The findings demonstrate that employee trust in AI positively influences both GenAI use and GenAI hedonic experience, and that these two factors, in turn, strongly impact workplace well-being. Additionally, while trust in AI also directly affects workplace well-being, its impact is weaker compared to the effects of GenAI use and GenAI hedonic experience. This study provides important practical implications for organizations aiming to improving employee well-being through the adoption of GenAI. Additionally, limitations of the study are discussed, along with suggestions for future research directions.

The integration of Artificial Intelligence (AI) and Knowledge Management (KM) in healthcare is transforming decision-making, resource allocation, and service efficiency, aligning with the United Nations’ Sustainable Development Goals (SDGs). AI-driven KM systems enhance clinical decision-making, optimize disease detection, and expand medical access, particularly in underserved areas, supporting SDG 3 (Good Health and Well-being) and SDG 10 (Reduced Inequalities). Additionally, AI contributes to SDG 4 (Quality Education) by improving medical training and SDG 13 (Climate Action) through energy-efficient healthcare solutions. Despite its potential, the adoption of AI in healthcare faces significant challenges, including data privacy concerns, algorithmic biases, and regulatory uncertainties. The lack of comprehensive studies assessing AI’s measurable impact on sustainability further limits its large-scale implementation. This study addresses these gaps by analyzing how AI-driven KM systems support SDGs and identifying key obstacles to their integration. The research conducts a bibliometric and thematic analysis and examines 1,095 peer-reviewed papers from the Scopus database. Findings highlight AI’s role in enhancing efficiency, enabling knowledge-sharing, and improving resilience in healthcare systems. However, concerns regarding ethical governance, equitable access, and technological disparities underline the necessity of strong policy frameworks to ensure responsible AI deployment. This study contributes to the discourse on AI’s role in healthcare by providing a structured analysis of its impact on SDG achievement. Finally, the article addresses future research perspectives as joint scholars’ and practitioners’ analyses.

The pervasive integration of artificial intelligence (AI) is fundamentally reshaping societal and business landscapes, presenting both significant opportunities and challenges, particularly within the entrepreneurial sphere. AI’s potential to alter venture conception, development, scaling, market entry, and competitive dynamics necessitates a re-evaluation of traditional entrepreneurship theories. As a key enabling technology, AI revolutionises core entrepreneurial processes, from ideation to market expansion, fostering novel forms of value creation and opportunity identification. However, despite acknowledging the influence of digital technologies, a comprehensive understanding of specific research trends concerning AI’s role, especially following the widespread adoption of generative AI tools like ChatGPT, remains underdeveloped. This study addresses this gap through a bibliometric systematic literature review. Utilising the Web of Science database and the “bibliometrix” R-package, we employ thematic evolution analysis to map the trajectory of research topics at the intersection of AI and entrepreneurship, specifically comparing the pre- and post-ChatGPT periods. We aim to visualise the field’s structure, identify influential contributions, and track thematic shifts. Anticipated findings include the emergence of research streams focused on generative AI’s impact on venture creation, ethical considerations, evolving decision-making processes, and the new skills required for AI-driven entrepreneurship. This analysis seeks to provide practical insights for entrepreneurs leveraging AI tools and highlight knowledge gaps and future research directions for academics, ultimately contributing to a deeper understanding of how AI is shaping the future of entrepreneurship.