Lars Espen Andersen

简介

Espen Andersen is a Teaching Professor with the Department of Strategy and Entrepreneurship at BI Norwegian Business School and Adjunct Senior Lecturer with the Department of Computer Science at University of Oslo. Based in Oslo, Norway, he has done research on topics such as technology strategy, mobile business, electronic commerce, knowledge management, learning technologies, digital business strategy, machine learning and generative AI. He is an Excellent Teaching Practitioner (merittert underviser) since 2023.

代表成果

• Scientific articles and book chapters
• Andersen, Espen (2026). Hyperefficient Mediocrity--the Future Jobs of GenAI. ACM Ubiquity. 2026(May), p. 8–8. doi: 10.1145/3819594. Full text in Research Archive Show summary GenAI is coming for the comfortable, not the creative minds Generative AI provides organizations with conversation (an always-on, endlessly patient interface for any simple question), distillation (summarizing vast volumes of text), and fabrication (generating the flood of standard documents and compliance material that modern bureaucracies seem to need). Together, these capabilities can automate "bullshit jobs" while raising the bar for what counts as genuinely original work, as companies rethink how they recruit, train, and create value.
• Johnson, Jeffrey; Wolmar, Christian; Andersen, Espen; Waldez, Miguel; Delic, Kemal & Odlyzko, Andrew (2025). Driverless Vehicles: A case study in applied artificial intelligence. ACM Ubiquity. doi: https:/dl.acm.org/doi/pdf/10.1145/3777393. Full text in Research Archive Show summary The case study of driverless vehicles shows that AI has limitations. Understanding this can lead to successful systems, while not understanding it has led to expensive failures. Despite billions of dollars invested over many decades, no vehicle entirely controlled by AI can operate on public roads. Success comes from an appropriate combination of AI doing what it does best and humans doing what they do best. The examples of a grocery delivery system and driverless taxis show that viable systems can be produced when vehicles drive partly using AI but request remote human assistance when the AI cannot cope. Such successes can give the public the impression that the AI can solve much harder problems than it can—to operate in the highly complex real world AI systems need humans in the loop. There are various aids for drivers such as AI controlling cars on freeways, but these all require the driver to take back control when instructed. Other lessons for engineering AI system include: the level of autonomy for intelligent machines and systems should be well defined; a systems approach that codesigns AI systems with their operating environment can lead to successful systems capable of incremental improvement; new and unanticipated problems can emerge on applying AI; the highest standards of conventional software engineering are required for robust and safe AI applications; driverless vehicles challenge the idea that regulation stifles innovation, and provide examples of companies putting profit before safety—regulation is essential for safe applications of AI.
• Andersen, Espen; Johnson, John Chandler; Kolbjørnsrud, Vegard & Sannes, Ragnvald (2018). The data-driven organization: Intelligence at SCALE. In Sasson, Amir (Eds.), At the Forefront, Looking Ahead: Research-Based Answers to Contemporary Uncertainties of Management. Universitetsforlaget. ISSN 9788215031408. p. 23–42. doi: 10.18261/9788215031583-2018-03. Full text in Research Archive Show summary Evolving at an unprecedented pace, digital technologies promise to automate not only labor-intensive and repetitive work, but also the traditional and exclusive domain of educated humans—knowledge work. This is evident in the new ways of reaching customers and coordinating activities, as well as in the fact that companies conducting a business built on the new technologies now constitute the world’s largest enterprises. The presence and evolution of these companies challenge established divisions of labor between man and machine, and almost casually redraws the boundaries between industries. Machine learning and analytics challenge the managers leading and the managerial scientists studying organizations. Everybody says they want to be data driven—but what does a company really need to do to achieve that? This article will explore the managerial, organizational, and strategic implications of allowing an ever increasing number of organizational decisions to be taken not by managers employing intuition and common sense, but by algorithms and learning systems based on massive amounts of data derived from electronically based customer interactions. We argue that these companies can be thought of as “intelligent enterprises” with enhanced abilities to sense, comprehend, act, learn and explain (SCALE) their environment and their interactions with it. To acquire these capabilities, managers need to cede authority over some decisions while acquiring new capabilities and roles for themselves.
• Andersen, Espen & Sannes, Ragnvald (2018). Er du klar for digitalisering? Praktisk Økonomi & Finans. ISSN 1501-0074. 34(3), p. 196–213. doi: 10.18261/issn.1504-2871-2018-03-04. Full text in Research Archive Show summary I denne artikkelen utvikler og presenterer vi et rammeverk, en digitaliseringskanvas, for å artikulere, beskrive og analysere hvordan en virksomhet kan bli påvirket av teknologisk utvikling og digitalisering. Formålet er å gi ledere et verktøy for systematisk utforsking av muligheter og kunne se potensielle trusler som følger av digitalisering før de oppstår. Bedre forståelse for dette vil kunne føre til mer informerte beslutninger om hvilke valg egen virksomhet skal foreta seg, og bedre innsikt i om de valgene er levedyktige. Vi presenterer tre eksempler på anvendelse av rammeverket, to på bransjenivå (forsikring samt regnskap og revisjon) og en på forretningsenhet (Jotun Hull Performance System). Eksemplene demonstrerer at man gjennom et slikt verktøy kan være forberedt også på disruptive innovasjoner.
• Andersen, Espen & Sannes, Ragnvald (2017). Hva er digitalisering? Magma forskning og viten. ISSN 1500-0788. 20(6), p. 18–24. Full text in Research Archive Show summary Digitalisering er blitt et moteord, men hva er det, hvorfor er det viktig nå, og hva kommer det til å gjøre med forretningsstrategi, organisasjonsprosesser og samfunnsforhold? I denne artikkelen diskuterer vi sammenhengen mellom teknologiutvikling, forretningsbetingelser og hvordan organisasjoner og samfunn vil og bør respondere i form av nye, digitale strategier.
• Sannes, Ragnvald & Andersen, Espen (2017). Er norske bedrifter digitale sinker? :. Magma forskning og viten. ISSN 1500-0788. 20(6), p. 43–53. Full text in Research Archive Show summary I denne artikkelen spør vi om norske bedrifter er digitale sinker. Dette belyses gjennom en internasjonal spørreundersøkelse til IT-direktører i store selskaper i Asia, Nord-Amerika, Europa, Sverige og Norge. Dataene viser at det er store forskjeller mellom bedrifter i hver region, men også at skandinaviske bedrifter i gjennomsnitt er på etterskudd. I artikkelen bruker vi forskjellene i gjennomsnittstall som basis for en generell diskusjon om forskjellen på digitale mestre og sinker. Norske bedrifter er i varierende grad truffet av dette, men alle bedrifter som ikke er digitale mestre, vil ha nytte av drøftingen i artikkelen.
• Andersen, Espen (2014). Den digitaliserte virkelighet: Strategier for en verden full av data. Magma forskning og viten. ISSN 1500-0788. 17(3), p. 22–29. Full text in Research Archive