THE WHY AND WHAT OF AI DEPLOYMENT AND INNOVATION IN COMPANIES — RESULTS AND LEARNINGS FROM A SYSTEMATIC LITERATURE RESEARCH ENLIGHTENED BY THE CAPABILITY THEORY

Seppo Ruotsalainen, Pã„ivi Hokkanen (), Jari Porras () and Olli Kuivalainen ()
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Seppo Ruotsalainen: LUT Licenciate of Technology, LUT University, Väätämönniementie 58, 52100 Anttola, Finland
Pã„ivi Hokkanen: ��LUT, Doctor of Science, LUT University, Engelinaukio 2–4, A5, 00150 Helsinki, Finland
Jari Porras: ��LUT School of Engineering, Professor of Software Engineering, LUT University, Yliopistonkatu 34, 53850 Lappeenranta, Finland
Olli Kuivalainen: �LUT Business School, Professor of Business Studies, LUT University, Yliopistonkatu 34, 53850 Lappeenranta, Finland

International Journal of Innovation Management (ijim), 2024, vol. 28, issue 07n08, 1-32

Abstract: Despite soaring interest in Artificial Intelligence (AI), only one-tenth of companies have reported tangible business results. We conducted a Systematic Literature Research on peer-reviewed reports to establish an updated status and baseline of AI deployment for research and companies. We analysed selected peer-reviewed articles for deployment objectives, approaches, results, and learnings. This research confirms that AI is still at the early stages of deployment and innovation in companies. Deploying AI successfully represents a management — rather than a technology — challenge. It requires more cognisance, innovation, learning, and effort than generally thought. Through the results and a novel conceptual framework, this research increases the knowledge and emphasises the importance of the pre-deployment from theoretical and practical viewpoints. The Sensing stage of the Dynamic Capability theory aligns well with the developed pre-deployment concept. We propose several research topics to increase the knowledge and theoretical understanding of deploying AI cognisably for business and stakeholder benefits.

Keywords: Artificial intelligence; strategy; deployment; innovation; objectives; results; learnings (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S1363919624500294

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