AI Integration Scaling AI for business model innovation

dc.contributor.authorLundblad, Linnea
dc.contributor.authorOlsson, Tilda
dc.contributor.departmentInstitutionen för tillämpad informationsteknologiswe
dc.contributor.departmentDepartment of Applied Information Technologyeng
dc.date.accessioned2022-10-28T13:05:51Z
dc.date.available2022-10-28T13:05:51Z
dc.date.issued2022-10-28
dc.description.abstractArtificial Intelligence (AI) is still largely unexplored within information systems research, and most published work remains non-academic (Collins et al., 2021). While there exists some research related to the challenges incumbent organizations have to scale AI, there is little insight to why some succeed. The paper applies a multidimensional framework called the Artificial Intelligence Innovation Maturity Index (AIMI) (Yams et al. (2020) to analyze six AI deployments in European organizations. The purpose of the study was to identify success factors and assess organizations’ maturity to integrate AI for business model innovation. The study conducts a qualitative approach where 7 respondents were interviewed regarding an implemented AI use case. The paper contributes with practical advice to managers of what to consider when leading and investing in AI. The findings indicate that the primary investment focus is dependent on the organization's AI maturity level and considers a short term and long term purpose. The paper contributes to research formation related to applied artificial intelligence, digital transformation, maturity models and business model innovation.en
dc.identifier.urihttps://hdl.handle.net/2077/74027
dc.language.isoengen
dc.relation.ispartofseries2022:053en
dc.setspec.uppsokTechnology
dc.subjectartificial intelligenceen
dc.subjectdata-drivenen
dc.subjectbusiness model innovationen
dc.subjectdigital transformationen
dc.subjectdigital maturityen
dc.subjectAI maturityen
dc.titleAI Integration Scaling AI for business model innovationen
dc.typeTexteng
dc.type.degreeMaster theseseng
dc.type.uppsokH2

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