TY - JOUR

T1 - Accommodating machine learning algorithms in professional service firms

AU - Faulconbridge, James

AU - Sarwar, Atif

AU - Spring, Martin

PY - 2024/4/27

Y1 - 2024/4/27

N2 - Machine learning algorithms, as one form of artificial intelligence (AI), are significant for professional work because they create the possibility for some predictions, interpretations and judgements that inform decision making to be made by algorithms. However, little is known about whether it is possible to transform professional work to incorporate machine learning whilst also addressing negative responses from professionals whose work is changed by inscrutable algorithms. Through original empirical analysis of the effects of machine learning algorithms on the work of accountants and lawyers, this paper identifies the role of accommodating machine learning algorithms in professional service firms. Accommodating machine learning algorithms involves strategic responses that both justify adoption in the context of the possibilities and new contributions of machine learning algorithms and respond to the algorithms’ limitations and opaque and inscrutable nature. The analysis advances understanding of the processes that enable or inhibit the cooperative adoption of AI in PSFs and develops insights relevant when examining the long-term impacts of machine learning algorithms as they become ever more sophisticated.

AB - Machine learning algorithms, as one form of artificial intelligence (AI), are significant for professional work because they create the possibility for some predictions, interpretations and judgements that inform decision making to be made by algorithms. However, little is known about whether it is possible to transform professional work to incorporate machine learning whilst also addressing negative responses from professionals whose work is changed by inscrutable algorithms. Through original empirical analysis of the effects of machine learning algorithms on the work of accountants and lawyers, this paper identifies the role of accommodating machine learning algorithms in professional service firms. Accommodating machine learning algorithms involves strategic responses that both justify adoption in the context of the possibilities and new contributions of machine learning algorithms and respond to the algorithms’ limitations and opaque and inscrutable nature. The analysis advances understanding of the processes that enable or inhibit the cooperative adoption of AI in PSFs and develops insights relevant when examining the long-term impacts of machine learning algorithms as they become ever more sophisticated.

U2 - 10.1177/01708406241252930

DO - 10.1177/01708406241252930

M3 - Journal article

JO - Organization Studies

JF - Organization Studies

SN - 0170-8406

ER -