PurposeThis article ties in with current debates on the digital transformation of society and the consequent work changes. Using an artificial intelligence (AI)-based accounting context, the focus of this paper is on actors, roles and tasks and related skills on an individual level. The authors look at the effect of AI-based “smart” technology on the workforce in the broader accounting profession taking an intrafirm perspective, yet acknowledging that the digital transformation encompasses a much larger field in the financial sector.Design/methodology/approachThe authors conduct a Delphi study to identify the new roles and tasks in future accounting. In addition, the authors use expert workshops to clarify the related tasks and skills and determine whether either humans or AI-based technologies perform the roles or collaborate in professional accounting occupations.FindingsThe results show that tasks and skills for existing professional occupations in the broader acounting context will be subject to major changes in the next 10 years due to (AI based) digital technologies, while “core” roles and tasks will continue to exist in the future, some will not be performed by humans but by AI-based technology. For other “new” roles, humans will need to make informed use of digital technologies and, to some extent, collaborate with AI-based technology.Research limitations/implicationsThe authors look at the effect of AI-based “smart” technology on the workforce in the broader accounting profession, taking an intrafirm perspective.Practical implicationsThis article ties in with current debates on the digital transformation of society and the consequent work changes. Using an AI-based accounting context, the focus of this paper is on the new and adapted roles and tasks.Originality/valueThe comprehensive analysis based on the Delphi study and expert workshops provide ample innovative ground for future research on the impact of AI on organisations and society.

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PurposeWith the help of “self-learning” algorithms and high computing power, companies are transforming Big Data into artificial intelligence (AI)-powered information and gaining economic benefits. AI-powered information and Big Data (simply data henceforth) have quickly become some of the most important strategic resources in the global economy. However, their value is not (yet) formally recognized in financial statements, which leads to a growing gap between book and market values and thus limited decision usefulness of the underlying financial statements. The objective of this paper is to identify ways in which the value of data can be reported to improve decision usefulness.Design/methodology/approachBased on the authors' experience as both long-term practitioners and theoretical accounting scholars, the authors conceptualize and draw up a potential data value chain and show the transformation from raw Big Data to business-relevant AI-powered information during its process.FindingsAnalyzing current International Financial Reporting Standards (IFRS) regulations and their applicability, the authors show that current regulations are insufficient to provide useful information on the value of data. Following this, the authors propose a Framework for AI-powered Information and Big Data (FAIIBD) Reporting. This framework also provides insights on the (good) governance of data with the purpose of increasing decision usefulness and connecting to existing frameworks even further. In the conclusion, the authors raise questions concerning this framework that may be worthy of discussion in the scholarly community.Research limitations/implicationsScholars and practitioners alike are invited to follow up on the conceptual framework from many perspectives.Practical implicationsThe framework can serve as a guide towards a better understanding of how to recognize and report AI-powered information and by that (a) limit the valuation gap between book and market value and (b) enhance decision usefulness of financial reporting.Originality/valueThis article proposes a conceptual framework in IFRS to regulators to better deal with the value of AI-powered information and improve the good governance of (Big)data.