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Advanced Business Model Innovation Supported by Artificial Intelligence and Deep Learning

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Abstract

Businesses have classically put emphasis on human bonds related to their BM’s [www.conansence.org]. By the fast development of more sensoring, persuasive and virtual BMs increasingly run autonomously by machines, businesses should expect to be able to, build competence and thereby be capable in the future to innovated BM’s and operate BM’s in new types Business Model Ecosystems (BMES) (Lindgren and Rasmussen in J Multi BMI 4:1, 2016) in the future—where physical, digital and virtual BMES become integrated. This will investable open up to new multi business model potential but also require that businesses operate and innovate their multitudes of BM’s differently. BMES and BM’s (Lindgren in J Multi Bus Model Innov Technol 4:1, 2016; Lindgren and Rasmussen in J Multi Bus Model Innov Technol 1: 135, 2013) have for a longtime been based and built up with mainly human bond communication, but new technologies very much based on machine to human communication and machine to machine communication evolves and change the game of BMI with exponential speed. How will this change the game of Business Model Innovation (BMI) between humans, humans and machines and machines to machines. How will this evolvement influence businesses ability to “download”, “see”, “sense”, “relate” and “receive” and relate BM’s with their AS IS and TO BE BM’s. The paper addresses the exponential development of artificial intelligence technologies, persuasive technologies, virtual technologies and thereby increase the potential to create, capture, deliver, receive and consume physical, digital, persuasive and virtual BMs in Business model innovation and introduce a conceptual model to future business model innovation and operation.

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Fig. 1

(Adapted from Valter et al. [9])

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Adapted from Valter et al. [9]

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Fig. 5

(Adapted from Valter et al. [9])

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Authors and Affiliations

  1. Business Development and Technology, Aarhus University, Aarhus, Denmark

    Per Valter, Peter Lindgren & Ramjee Prasad

  2. School of Business and Social Sciences, Aarhus University, Birk Centerpark 15, Office: CGC LAB, 7400, Herning, Denmark

    Peter Lindgren

Authors
  1. Per Valter
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  2. Peter Lindgren
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  3. Ramjee Prasad
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Correspondence to Per Valter.

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Valter, P., Lindgren, P. & Prasad, R. Advanced Business Model Innovation Supported by Artificial Intelligence and Deep Learning. Wireless Pers Commun 100, 97–111 (2018). https://doi.org/10.1007/s11277-018-5612-x

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