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.

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.