Development of a Holistic Method to Implement Artificial Intelligence in Manufacturing Areas

Pokorni, Bastian; Volz, Fabian; Zwerina, Jan; Hämmerle, Moritz

doi:10.1007/978-3-030-51328-3_1

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…Despite AI being a key enabler of digital transformation, organizations struggle to find relevant talent in their region or simply lack the budget to attract experienced AI specialists (Tarafdar et al, 2019). Owing to the lack of experience and knowledge in AI, companies find it challenging to implement AI‐technology‐based solutions in manufacturing (Pokorni et al, 2020). Also, unrealistic expectations arise from AI solutions due to the lack of knowledge about AI/ML techniques' potential capabilities.…”

Section: Theoretical Background and Related Researchmentioning

confidence: 99%

“…One of the key difficulties an organization faces in AI adoption is finding the exact use case where the AI solution will give a relative advantage over the legacy application, and justifying the return on investment is one of the critical challenges practitioners face (Alsheibani et al, 2018). Similarly, challenges are seen in matching the AI application's potential to a suitable use case of a business function in a specific business environment (Pokorni et al, 2020).…”

Section: Challenge In Problem Selectionmentioning

confidence: 99%

“…Owing to the lack of experience and knowledge in AI, companies find it challenging to implement AI-technology-based solutions in manufacturing (Pokorni et al, 2020). Also, unrealistic expectations arise from AI solutions due to the lack of knowledge about AI/ML techniques' potential capabilities.…”

Section: Lack Of Ai Talentmentioning

confidence: 99%

See 2 more Smart Citations

Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective

Kar¹,

Kar

Gupta

2021

Intell Sys Acc Fin Mgmt

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Summary Artificial intelligence (AI) in business processes and academic research in AI has significantly increased. However, the adoption of AI in organizational strategy is yet to be explored in extant literature. This study proposes two conceptual frameworks showing hierarchical relationships among the various drivers and barriers to AI adoption in organizational strategy. In a two‐step approach, the literature study is first done to identify eight drivers of and nine barriers to AI adoption and validated by academic and industry experts. In the second step, MICMAC (matrice d'impacts croises‐multiplication appliqúe a un classment or cross‐impact matrix multiplication applied to classification) analysis categorizes the drivers and barriers to AI adoption in organizational strategy. Total interpretive structural modeling (TISM) is developed to understand the complex and hierarchical associations among the drivers and barriers. This is the first attempt to model the drivers and barriers using a methodology like TISM, which provides a comprehensive conceptual framework with hierarchical relationships and relative importance of the drivers and barriers to AI adoption. AI solutions' decision‐making ability and accuracy are the most influential drivers that influence other driving factors. Lack of an AI adoption strategy, lack of AI talent, and lack of leadership commitment are the most significant barriers that affect other barriers. Recommendations for senior leadership are discussed to focus on the leading drivers and barriers. Also, the limitations and future research scope are addressed.

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Section: Theoretical Background and Related Researchmentioning

confidence: 99%

Section: Challenge In Problem Selectionmentioning

confidence: 99%