[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117025-en":3,"doc-seo-117025-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},117025,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Accommodating machine learning algorithms in professional service firms - Research paper","Explores how professional service firms accommodate the distinctive capabilities of machine learning algorithms amid technological change. Distinguishes machine learning from prior automation by emphasizing autonomous recommendations and opaque decision-making, which require professionals to adapt interpretive and judgement tasks. Reviews literature on “cooperatively realigned” work during digital introduction, including organizational and social-political adjustments, coalitions, client-benefit narratives, and strategies to exploit or defuse perceived threats.","Accommodating machine learning algorithms in professional service firms  \nJames Faulconbridge, Lancaster University, UK* [j](j.faulconbridge@lancaster.ac.uk)[.faulconbridge@lancaster.ac.uk](j.faulconbridge@lancaster.ac.uk)  \nAtif Sarwar, Liverpool Hope University, [UK](UK sarwara@hope.ac.uk)[ ](UK sarwara@hope.ac.uk)[sarwara@hope.ac.uk](UK sarwara@hope.ac.uk)  \nMartin Spring, Lancaster University, [UK](UK m.spring@lancaster.ac.uk)[ ](UK m.spring@lancaster.ac.uk)[m.spring@lancaster.ac.uk](UK m.spring@lancaster.ac.uk)  \n* Corresponding author  \nAccommodating machine learning algorithms in professional service firms  \nIntroduction  \nTechnological change is a key theme in studies of professional work. Most recently, debates have focused on the way intelligent algorithmic technologies create the potential for, at one extreme, the end of the professions and professional service firms (PSFs) (Kokina and Davenport, 2017; Susskind and Susskind, 2015), or alternatively new business models for PSFs and new types of work for professionals as the services offered to clients are revolutionised (Armour and Sako, 2020; Faulconbridge, Sarwar and Spring, 2023; Kronblad, 2020 ; Pemer and Werr, 2023 ; Spring, Faulconbridge and Sarwar, 2022) .  \nIntelligent algorithmic technologies are distinctive because they are “more encompassing, instantaneous, interactive, and opaque than previous technological systems”(Kellogg Valentine and Christin., 2020: 366) . Intelligent technologies are not, however, universal in their form and effects. Whilst often subsumed under the label of algorithms or artificial intelligence (AI), technologies vary in their underlying architecture. For example, technologies using machine learning with neural networks rely on big datasets for training, which rule-based AI does not (Ågerfalk et al., 2022) . In relation to professional work and PSFs, it is the possibilities created by intelligent technologies that use supervised and/or unsupervised machine learning that have attracted most recent attention. Machine learning“has the capacity to learn and improve its analyses through the use of computational algorithms…[that] use large sets of data inputs and outputs to recognize patterns and effectively ‘learn’ in order to train the machine to make autonomous recommendations or decisions”(Helm et al., 2020: 69) .  \nMachine learning algorithms are significant for professional work because they create the possibility for interpretation and judgement tasks to be automated and completed by computers instead of human professionals (Pachidi, Berends, Faraj and Huysman, 2021; Raisch and Krakowski, 2021) . This is different to previous forms of automation that involved a series of steps being defined by professionals that were then repeated in a codified manner using an algorithm (Anthony, Bechky and Fayard, 2023) . In particular, the ability of machine learning technologies to not only process data but also “make determinations by themselves”(Murray, Rhymer and Sirmon, 2021: 553) means that professionals have to adapt to some predictions, interpretations and judgements that inform decision-making being made by algorithms. As Glaser, Pollock and D’Adderio (2021: 2) note, it is therefore important to explore the biography of an algorithm and how the specific “generative and diverse possibilities” of a particular technology “are used to automate decisions, enact roles and expertise”, thus replacing the interpretative analytical work of professionals. It also means understanding how professionals encounter the augmentation of their work, when machine learning algorithms assist the work of professionals by providing new interpretations and analytical insights that inform new types of advice to clients (Davenport and Kirby, 2016; Raisch and Krakowski, 2021) .  \nAn emerging body of literature suggests that responses to the distinctive features of machine learning enabled automation and augmentation might depend on whether those advocating new tech","cbCaie79QiDzeGeD","https://ap.wps.com/l/cbCaie79QiDzeGeD","pdf",486540,1,55,"English","en",105,"# Introduction\n## Intelligent algorithmic technologies and their variability\n## Why machine learning matters for professional work\n## Cooperative realignment and organizational responses\n## Defusing threats and managing inscrutability","[{\"question\":\"How is machine learning different from earlier automation in professional service work?\",\"answer\":\"Machine learning can make determinations and autonomous recommendations rather than relying on fixed, codified steps defined by professionals, changing how interpretation and judgement tasks are performed.\"},{\"question\":\"What does “biography of an algorithm” refer to in this context?\",\"answer\":\"It highlights how a specific technology’s generative possibilities are used to automate decisions and enact roles, potentially replacing professionals’ interpretative analytical work.\"},{\"question\":\"What responses help professional service firms adopt machine learning more effectively?\",\"answer\":\"Literature emphasizes cooperative realignment during digital introduction and integration, including changes to goals, activities, and social or political relations, alongside focusing on benefits for clients and strategies to defuse threats from opaque 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