Artificial intelligence

Building blocks and an innovation typology

Document identifier: oai:DiVA.org:ltu-77124
Access full text here:10.1016/j.bushor.2019.10.004
Keyword: Social Sciences, Economics and Business, Business Administration, Samhällsvetenskap, Ekonomi och näringsliv, Företagsekonomi, Artificial intelligence, Machine learning, Disruptive innovation, Product development, Decision making, Strategic planning, Situational awareness, Industrial Marketing, Industriell marknadsföring
Publication year: 2020
Relevant Sustainable Development Goals (SDGs):
SDG 9 Industry, innovation and infrastructure
The SDG label(s) above have been assigned by OSDG.ai

Abstract:

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.

Authors

Ulrich Paschen

Luleå tekniska universitet; Industriell Ekonomi
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Christine Pitt

Royal Institute of Technology (KTH), Stockholm, Sweden
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Jan Kietzmann

University of Victoria, Victoria, BC, Canada
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