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Artificial intelligence: Building blocks and an innovation typology
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
Royal Institute of Technology (KTH), Stockholm, Sweden.
University of Victoria, Victoria, BC, Canada.
2019 (English)In: Business Horizons, ISSN 0007-6813, E-ISSN 1873-6068Article in journal (Refereed) Epub ahead of print
Abstract [en]

The range of topics and the opinions expressed on artificial intelligence (AI) are so broad that clarity is needed on the the field’s central tenets, the opportunities AI presents, and the challenges it poses. To that end, we provide an overview of the six building blocks of artificial intelligence: structured data, unstructured data, preprocesses, main processes, a knowledge base, and value-added information outputs. We then develop a typology to serve as an analytic tool for managers grappling with AI’s influence on their industries. The typology considers the effects of AI-enabled innovations on two dimensions: the innovations’ boundaries and their effects on organizational competencies. The typology’s first dimension distinguishes between product-facing innovations, which influence a firm’s offerings, and process-facing innovations, which influence a firm’s operations. The typology’s second dimension describes innovations as either competence-enhancing or competence-destroying; the former enhances current knowledge and skills, whereas the latter renders existing skills and knowledge obsolete. This framework lets managers evaluate their markets, the opportunities within them, and the threats arising from them, providing valuable background and structure to important strategic decisions.

Place, publisher, year, edition, pages
Elsevier, 2019.
Keywords [en]
Artificial intelligence, Machine learning, Disruptive innovation, Product development, Decision making, Strategic planning, Situational awareness
National Category
Business Administration
Research subject
Industrial Marketing
Identifiers
URN: urn:nbn:se:ltu:diva-77124DOI: 10.1016/j.bushor.2019.10.004OAI: oai:DiVA.org:ltu-77124DiVA, id: diva2:1376811
Available from: 2019-12-10 Created: 2019-12-10 Last updated: 2020-01-07

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Paschen, Ulrich

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