The study aims to identify the knowledge, skills and competencies required by accounting and auditing (AA) professionals in the context of integrating disruptive Generative Artificial Intelligence (GenAI) technologies and to develop a framework for integrating GenAI capabilities into organisational systems, harnessing its potential to revolutionise lifelong learning and skills development and to assist day-to-day operations and decision-making. Through a systematic literature review, 103 papers were analysed, to outline, in the current business ecosystem, the competencies’ demand generated by AI adoption and, in particular, GenAI and its associated risks, thus contributing to the body of knowledge in underexplored research areas. Positioned at the confluence of accounting, auditing and GenAI, the paper introduces a meaningful overview of knowledge in the areas of effective data analysis, interpretation of findings, risk awareness and risk management. It emphasizes and reshapes the role of required skills for accounting and auditing professionals in discovering the true potential of GenAI and adopting it accordingly. The study introduces a new LLM-based system model that can enhance its GenAI capabilities through collaboration with similar systems and provides an explanatory scenario to illustrate its applicability in the accounting and audit area.

Hyperautomation is a business-driven approach, conceptualized in 2019 by Gartner Inc., that combines various technologies such as Artificial Intelligence (AI), Robotic Process Automation (RPA) and integrated platforms as a service (iPaas) with the aim of making business processes more efficient by substituting human intervention. Among these, implementations of AI within business services use technologies like Natural Language Processing, Voice and Image Recognition, Virtual Agents, Machine Learning or Deep Learning platforms. Acknowledging this reality, we are interested in developing answers to the following research questions: (1) What are the main categories of business services which integrate specific AI tools? (2) What are the transformed business processes and their operations provided by AI tools? (3) What are the benefits related to AI integrated tools? For this triadic purpose, a systematic literature review on the implementation of Artificial Intelligence in the field of business services was carried out. Only works indexed in the Web of Science database, published in the last 5 years, were selected. Moreover, the websites of the main developers and client companies were investigated. Our findings include a selection of identified AI solutions, structured by main business services categories; we have also outlined the performed tasks and the resulting benefits of each listed AI tool. The synopsis of AI-powered tools presented in the paper could serve professionals, managers and researchers in designing future policies, operational procedures and research approaches to cope with new challenges of disruptive technologies from the AI spectrum.

The integration of emerging technologies in Enterprise Resource Planning systems has the potential to enhance security, automation, decision-making, and predictive capabilities. However, this also introduces new cybersecurity challenges, as the systems may become targets for malicious attacks or data breaches. Understanding the nexus between organizational systems, artificial intelligence (AI), and cyber-security requirements, offers new insights for the modern business environment. Our study begins with an exploration of recent cases of AI-enhanced cybersecurity tools implemented within organizational information systems, as these currently stand. This research landscape is our starting point for an analysis of the impact of these tools on different types of systems, of the cyber risks reported in recent literature, and the configuration of cyber-security solutions tailored after current vulnerabilities of the business environment. We also identify trends and gaps in the existing research that suggest possible new topics for further investigation.

The rapid adoption of artificial intelligence (AI) technologies into organizational information systems is reshaping internal audit practices and governance mechanisms. Nevertheless, notable gaps persist in both the academic literature and professional practice in integrating AI technologies within the internal audit function and in assessing its maturity level. To address these limitations, this study employs a two-stage methodological approach. First, a Systematic Literature Review (SLR) was conducted following PRISMA 2020 and PRISMA-S guidelines to synthesize existing knowledge and identify structural gaps in AI integration within the internal audit function (IAF). Second, drawing on the SLR findings, the study follows a theory-building and conceptual artifact-design approach. Three instruments are developed and assessed: the Internal Audit–Artificial Intelligence Integration Framework (IA-AIIF), the Internal Audit–AI Integration Assessment Cube (IA-AI Cube), and a Hierarchical Weighted Scoring Model (HWSM). These instruments enable the multidimensional evaluation of AI integration across technical, operational, and governance dimensions. They may offer guidance for both practitioners and researchers advancing AI-enabled approaches within the IAF. The findings suggest practical, managerial, and theoretical contributions by supporting AI integration in internal audit practice, outlining implementation-oriented recommendations for AI adoption, and advancing the body of knowledge at the intersection of internal audit and emerging AI technologies.