[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83129-en":3,"doc-seo-83129-105":30,"detail-sidebar-cat-0-en-105":91},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83129,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Digital Fragmentation and Generative AI Use Across 103 Million Application Events","Knowledge workers switch between applications thousands of times per day, spending nearly a tenth of the work year transitioning between digital apps in a process termed digital fragmentation. Analyzing 103 million second-by-second application events from 1,017 employees across eight knowledge-work organizations, the study attributes most within-person day-to-day variation to changes in employees’ own work demands (44.6%). Fragmentation rises over the workweek and resets after weekends and holidays. Communication-heavy days show more fragmentation, and generative AI use aligns with more fragmented activity. After AI use, application patterns become narrower, longer, and more predictable.","Digital Fragmentation and Generative AI Use Across 103 Million Application Events  \nAuthors: Sumer S. Vaid 1* and Ashley V. Whillans2*  \nAffiliations:  \n1Artificial Intelligence Institute, Harvard Business School  \n2Negotiation, Organizations and Markets Unit, Harvard Business School  \n*Corresponding author. Email: [svaid@hbs.edu](svaid@hbs.edu) ; [awhillans@hbs.edu](awhillans@hbs.edu)  \nAbstract: Knowledge workers switch between applications thousands of times per day, spending nearly a tenth of the work year transitioning between digital applications in a process called digital fragmentation. Whether this fragmentation reflects who an employee is, where they work, or what kind of day they are having, has remained an open question. We analyzed 103 million application events recorded second-by-second from 1,017 employees across eight organizations that largely employ knowledge workers (e.g., law, financial services). Day-to-day variation in fragmentation within individual employees accounted for 44.6% of the variation in digital fragmentation, slightly exceeding stable individual differences between employees (35.8%), and far exceeding variation between organizations (19.6%) . Fragmentation rose over the work week and reset after weekends and holidays. Higher-than-typical use of communication applications coincided with more fragmented work. Generative AI use also occurred on more fragmented days, but the period following AI use was marked by narrower, longer, and more predictable application use. These findings identify the workday as a key level for understanding and intervening on digital fragmentation and suggest that AI may help structure fragmented work rather than merely intensify it.  \nKeywords: Digital fragmentation; generative artificial intelligence; task switching; knowledge work; digital trace data; within-person variability  \nPreprint version 3.0 (6 July 2026). Please note that this is a preprint. The manuscript is currently under review, and the findings may change based on the peer review process.  \nKnowledge work, the application and exchange of information 1, primarily unfolds inside digital applications such as email, spreadsheets and, increasingly, AI tools. Knowledge workers move between these applications and tools in rapid succession, and they rarely remain within a single application for more than a few minutes at a time. We refer to these rapid switches between applications2 as “digital fragmentation.” By some estimates, knowledge workers toggle between applications up to 1,200 times each day, spending nearly 9% of their work year in transition3.  \nDigital fragmentation requires employees to reorient after each switch and is associated with poor work outcomes. A high frequency of application switching is associated with long task completion times4, high error rates5, and increased difficulty resuming interrupted work6–10. Moreover, digital fragmentation has intensified over time. According to research that has tracked workers’ activities using direct in-person observation and activity logs, the amount of uninterrupted time that employees spend on individual applications has dropped from about 2.5 minutes two decades ago2 to 47 seconds in the last decade 11.  \nThe time it takes for employees to reorient between applications9 accumulates to roughly 5 weeks of productive output per year per employee3, a magnitude that, multiplied across the global knowledge workforce, translates into hundreds of billions of dollars in lost productive activity each year.  \nDespite mounting evidence that digital fragmentation is harmful and widespread2–9, a finegrained and representative account of digital fragmentation has remained elusive. The largest studies of the digital workday have relied on aggregated measures of digital fragmentation such as email volume and meeting counts over days or weeks 12–14, which cannot show how employees move between applications on a momentary, second-by-second basis. Other studies that h","cbCaidmdecrhdZh3","https://ap.wps.com/l/cbCaidmdecrhdZh3","pdf",2227835,2,1,30,"English","en",105,"# Digital fragmentation: background and importance\n## Definition and consequences\n## Why prior studies are limited\n# Research questions and study design\n## Sources of fragmentation\n## Temporal dynamics across the workweek\n## Linking generative AI use to fragmentation","[{\"question\":\"What is digital fragmentation in knowledge work?\",\"answer\":\"Digital fragmentation refers to the rapid switches between digital applications and tools that knowledge workers make, rarely staying within a single application for more than a few minutes.\"},{\"question\":\"Where does most digital fragmentation variation come from?\",\"answer\":\"Within-person day-to-day variation accounts for 44.6% of the variation in digital fragmentation, exceeding stable differences between employees (35.8%) and variation between organizations (19.6%).\"},{\"question\":\"How is generative AI use related to fragmented application behavior?\",\"answer\":\"Generative AI use occurs on more fragmented days, but the period after AI use shows narrower, longer, and more predictable application use, suggesting AI may help structure fragmented 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