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About Khalid
Dr. Khalid is an Associate Professor in the Department of Business Administration at the University of Inland Norway. His research and teaching activities focus on digitalization, artificial intelligence (AI) technologies, accounting, auditing, innovation, and entrepreneurship. His expertise extends to topics such as the future workforce, digital strategy, technology adoption, and the impact and challenges of AI on organizations and professions. Khalid is a sought-after speaker at both business and academic conferences, delivering talks in Norway and internationally.
Dr. Khalid's recent research achievements include his publication in The British Accounting Review, where his paper was officially recognized as the MOST DOWNLOADED article in the journal (ABS 3⭐) over the past 90 days.
With 16 years of industry experience in leading global organizations, Khalid brings a wealth of real-world insights to his teaching. His multidisciplinary expertise allows him to integrate practical case studies and examples into his courses, providing a dynamic and engaging learning environment that connects theory to practice.
Dr. Khalid earned his Ph.D. from the University of Pisa, Italy, specializing in digital transformation and AI technologies in accounting.
Publikasjoner
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Bibi, Zainab; Abbas, Khalid & Olsen, Carmen
(2026).
Artificial intelligence in accounting and auditing education: a systematic review and agenda for future research.
Submitted and Under Review.
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Abbas, Khalid; Santis, Federica De & Kolbjørnsrud, Vegard
(2026).
Artificial intelligence in accounting: a comparative analysis of adoption in accounting and non-accounting firms.
Journal of Accounting and Public Policy.
ISSN 0278-4254.
57,
s. 1–31.
doi:
10.1016/j.jaccpubpol.2026.107433.
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The increasing adoption of Artificial Intelligence (AI) technologies in accounting practices is reshaping traditional processes, offering unprecedented opportunities while also posing significant challenges for organizations. However, previous research has primarily focused on large accounting firms, leaving AI adoption patterns in non-accounting firms, where 76% of accountants are employed, largely underexplored. This study addresses this gap by conducting the first comprehensive comparative analysis of AI adoption, uncovering novel insights into levels, drivers, and barriers of AI integration and how they differ between accounting and non-accounting firms. Based on 35 semi-structured interviews, the study reveals an interplay between organizational-level and individual-level factors that shape adoption of different types of AI: robotic process automation (RPA), analytical AI (i.e., machine learning), and generative AI (i.e., large language models (LLMs)). It applies the Technology-Organization-Environment (TOE) framework as analytical lens and extends it by incorporating an individual dimension drawn from the UTAUT framework. The analysis reveals distinct patterns. Accounting firms predominantly employ RPA and ML for assurance-driven tasks such as transaction testing, anomaly detection, and regulatory compliance, while non-accounting firms adopt AI largely for predictive financial forecasting and process optimization. Key barriers, including rigid business models in accounting firms and organizational silos in non-accounting firms shape adoption trajectories. This study contributes to the field of accounting and auditing research by offering a nuanced understanding of how organizational context and professional logics uniquely influence AI adoption. It provides actionable insights for academics, practitioners, and policymakers, emphasizing the need for cross-functional collaboration, regulatory frameworks, and AI literacy initiatives to foster effective adoption across diverse sectors.
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Abbas, Khalid
(2025).
Management Accounting and Artificial Intelligence: A Comprehensive Literature Review and Recommendations for Future Research.
The British Accounting Review.
ISSN 0890-8389.
58(2),
s. 1–26.
doi:
10.1016/j.bar.2025.101551.
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Digitalization and artificial intelligence (AI) technologies have the potential to disrupt and transform the management accounting domain and the role of accountants. The study systematically reviews 91 articles, synthesizing scholarly work on digitalization, AI, machine learning (ML), deep learning (DL), explainable AI, generative AI, and large language models (LLMs) in management accounting. In this context, the value of the paper is multi-fold. First, we argue that these technologies transform accounting information and organizational structures, affecting the accounting function’s relationship with other organizational functions. Second, they present new challenges for management accountants, including data privacy, confidentiality, security and ethical concerns. Third, digital technologies automate basic accounting tasks and decision-making processes, potentially reshaping management accountants’ roles and skills in terms of job elimination, upskilling, deskilling and reskilling. Fourth, these technologies create new opportunities for multidisciplinary collaboration and redefine professional boundaries. This paper contributes by discussing the impact of digitalization and the latest AI technologies on management accounting, illustrating how they can create business value, and highlighting associated challenges and risks for the profession. It proposes research agendas and potential research questions for future studies, providing insight into the potential impacts and implications for the accounting profession and the role of accountants.
Se alle arbeider i NVA
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Abbas, Khalid
(2025).
The importance of accounting in an international business context.
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Abbas, Khalid
(2023).
Management Accounting Versus Machines: Who will be the winner? A review, synthesis, and research agenda”.
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Abbas, Khalid
(2023).
Exploring the (non) adoption of RPA, AI & ML technologies in the accounting industry. An empirical investigation.
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Se alle arbeider i NVA
Adresser
Besøksadresse
Hovedbygg Østre fløy
Telthusveien 12, N-2450 Rena
Rom 3Ø226
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Postadresse
Universitetet i Innlandet
Postboks 400 Vestad
2418 Elverum