The integration of Artificial Intelligence (AI) in business organizations is a significant step toward optimizing business processes and enhancing operational efficiency. Thanks to rapid technological advances in computer science, including deep learning, artificial neural networks, and natural language processing, companies now have access to increasingly sophisticated models that can process large amounts of data from heterogeneous sources quickly (Akerkar, 2019).
The adoption of Generative AI (GenAI) in businesses primarily aims to improve decision-making process accuracy, optimize operational time and costs, and identify market opportunities in a more timely manner (Afandizadeh et al., 2023; Prajwal et al., 2019). This translates into advanced solutions ranging from creating accurate predictive models on demand to implementing personalized chatbots and recommendation systems based on customer preferences (Afandizadeh et al., 2023; Malik et al., 2023; Prajwal et al., 2019). GenAI models can quickly analyse big data in unstructured format (e.g. text, audio, image), offering new opportunities to improve generation and knowledge transfer. They can also generate reports based on company documents (Bozkurt, 2023), improve training paths for competencies development (Santana & Díaz-Fernández, 2023), understand the satisfaction of employees (Mahmoud et al., 2019), and optimize information sharing with a common language that can be adapted to every need (Bandi et al., 2023).
However, despite its many advantages, the implementation of GenAI in organizational business processes poses several challenges. A major challenge concerns the opacity of the models themselves limits the transparency of decisions made by AI, undermining user acceptance and trust (Peifer & Terstegen, 2024). This highlights the need to develop effective methodologies to critically evaluate decisions and interpretive models to improve the interpretation of GenAI responses and identify biases in automated decision making (Deepa et al., 2024). Despite growing concerns about the risks associated with the use of GenAI, only a minority of companies fully recognize these risks and take protective measures. This underscores the importance of developing dedicated infrastructure and protocols to protect sensitive information and to develop strategies to mitigate cybersecurity risks (Martinez et al., 2021). Finally, solving these challenges will require a comprehensive approach that covers the technological, ethical, and organizational elements in integrating GenAI into business processes (Bandi et al., 2023).
This special track focuses on the implementation of AI in organisational business processes and knowledge management, inviting researchers, academics and practitioners in the field to present original studies and research on these topics. The goal will be to stimulate discussion and promote collaboration to address challenges and develop innovative solutions in this field.

The topics of interest for this call for papers include, but are not limited to:

• Development of shared standards for GenAI integration with business processes.
• Creation of solutions to make the decisions made by GenAI more “transparent”.
• Methods to critically evaluate GenAI decisions and identify biases in the output.
• Improved technological infrastructure and strategies to protect sensitive information and mitigate cyber risks.
• Analysing how GenAI can be used to create and transfer knowledge within an organization.
• Assessing how the adoption of GenAI can ensure a positive impact.

Marianna Lezzi, University of Salento, Italy
Giuliana Barba, University of Salento, Italy
Barbara Scozzi, Polytechnic University of Bari, Italy
Filippo Chiarello, University of Pisa, Italy