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Brage INN
Master thesis . 2025
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Kunstig intelligens i regnskapsbransjen

Authors: Skattebo, Benjamin Vidme; Rudi, Ingebjørg Berge;

Kunstig intelligens i regnskapsbransjen

Abstract

This study investigates which organizational and individual factors influence the implementation and use of artificial intelligence (AI) in the Norwegian accounting industry. The industry is undergoing a digital transformation, where increased use of AI tools challenges established work processes and competency requirements. Although the technology holds significant potential for improving efficiency and quality, previous research shows considerable variation in the degree of implementation across firms. The purpose of this study is to identify which factors act as drivers or barriers to successful AI implementation, and to examine how the use of AI affects perceived efficiency and quality in accounting work. A quantitative research design was applied, based on a survey distributed to employees and managers in small and medium-sized Norwegian accounting firms. The data were analyzed using bivariate and multiple regression analysis. The results indicate that several factors including digital maturity, digital competence, motivation, and age are positively associated with the degree of AI use. The multiple regression analysis confirms that digital competence, motivation, and age have a statistically significant effect. Individual factors proved to have greater explanatory power than organizational factors such as managerial support and digital maturity, offering a more nuanced understanding of what drives AI implementation in practice. Furthermore, the findings suggest that increased use of AI is associated with higher perceived efficiency and service quality. The study contributes to the existing literature by exploring these relationships in a Norwegian context and offers practical implications for firms aiming to succeed with digital transformation in the accounting sector. The findings particularly underscore the importance of human factors in realizing the value of AI.

Denne studien undersøker hvilke organisatoriske og individuelle faktorer som påvirker implementeringen og bruken av kunstig intelligens (KI) i den norske regnskapsbransjen. Bransjen står midt i en digital transformasjon, der økt bruk av KI-verktøy utfordrer etablerte arbeidsprosesser og kompetansekrav. Selv om teknologien har potensial for effektivisering og kvalitetsforbedring, viser tidligere forskning at implementeringsgraden varierer betydelig mellom virksomheter. Formålet med studien er å identifisere hvilke forutsetninger som fungerer som drivere og barrierer for vellykket KI-implementering, samt å undersøke hvordan bruk av KI påvirker opplevd effektivitet og kvalitet i regnskapsarbeidet. Studien benytter et kvantitativt forskningsdesign basert på spørreundersøkelse blant ansatte og ledere i små og mellomstore norske regnskapsforetak. Datamaterialet analyseres ved hjelp av bivariat og multippel regresjonsanalyse. Resultatene viser at flere faktorer, inkludert digital modenhet, digital kompetanse, motivasjon og alder, har positiv sammenheng med graden av KI-bruk, hvor den multiple regresjonsanalysen videre bekrefter at digital kompetanse, motivasjon og alder har signifikant effekt. Individuelle faktorer viste seg å ha større forklaringskraft enn organisatoriske forhold, som ledelsesstøtte og digital modenhet, noe som gir en mer nyansert forståelse av hva som driver KI-implementering i praksis. I tillegg viser funnene at økt bruk av KI er forbundet med høyere opplevd effektivitet og tjenestekvalitet. Studien bidrar til eksisterende litteratur ved å belyse disse sammenhengene i norsk regnskapskontekst, og gir praktiske implikasjoner for virksomheter som ønsker å lykkes med digital transformasjon i regnskapssektoren. Funnene understreker særlig betydningen av menneskelige forutsetninger for å realisere verdien av KI.

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Norway
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
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Green