Artificial intelligence

Tecuci, Gheorghe

doi:10.1002/wics.200

Cited by 70 publications

(34 citation statements)

References 42 publications

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“…This is not the case. Instead, AI encompasses information technologies that act rationally based on the information they have Russell & Norvig, 2016;Tecuci, 2012;. Artificial intelligence solves problems to achieve the best outcome or, in the case of uncertainty, the best expected outcome.…”

Section: Artificial Intelligencementioning

confidence: 99%

“…A second important AI process is ML, which encompasses computational procedures that enable AI to learn itself, i.e., improve its performance, without being explicitly programmed to do so (Russell & Norvig, 2016;Tecuci, 2012). In supervised ML, an AI technology is provided with training data sets that include the inputs and the correct outputs (i.e., correct answers), from which the computer learns the patterns and develops the rules to be applied to future instances of the same problem.…”

Section: Processesmentioning

confidence: 99%

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Artificial intelligence (AI) and value co-creation in B2B sales: Activities, actors and resources

Paschen

Pala

et al. 2020

Australasian Marketing Journal

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Artificial intelligence (AI) allows business actors to exchange resources, particularly information and knowledge, to strengthen their businesses. These AI-enabled value co-creation processes are playing a substantial role in the business-to-business (B2B) sales context. However, little is known about the mechanisms and the process of value co-creation enabled by AI. On this basis, this study addresses this gap by employing Service-Dominant Logic to understand value co-creation with AI. This study identifies the value co-creation process, and provides an understanding of the actors, activities and resources during the usage of AI to create value in B2B sales. The study also identifies several limitations of AI, such as, value co-creation is heavily dependent on human activities and resources. Lastly, we suggest that managers continue to manage customer expectations when using AI for value cocreation and highlight the necessity of human actors and resources in the value co-creation process.

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Section: Artificial Intelligencementioning

confidence: 99%

Section: Processesmentioning

confidence: 99%

Artificial intelligence (AI) and value co-creation in B2B sales: Activities, actors and resources

Paschen

Pala

et al. 2020

Australasian Marketing Journal

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“…The generalised definitions and processes of AI development were defined by Debrev (2012), Andriole (2019), Tecuci (2012), Akerker (2019a), and other authors. It was conceptualised that the development of AI tools or the process of embedding AI into the business process covers a set of steps starting with problem recognition, following with identifying data sources, and finally finding an appropriate AI solution (Akerkar, 2019a).…”

Section: Sourcementioning

confidence: 99%

Challenges in Applying Artificial Intelligence for Supply Chain Risk Management

Žigienė¹,

Rybakovas²,

Vaitkienė³

2020

IJEBA

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Purpose: To define the scope and nature of challenges in applying artificial intelligence (AI) for supply chain risk management (SCRM). Design/Methodology/Approach: Initial theoretical conceptualisation and respective approach were set by following the risk management maturity framework. The scope of explored challenges was defined by two data categories (supply chain risk events' and risk events' indicators) that are essential for AI tools to predict risk events' probability based on a set of risk prediction indicators. The nature of challenges is associated with the ways and forms of data collection, management, and application. The qualitative primary data research strategy was employed to explore selected case company practices associated with conceptually defined categories of scope and nature of challenges in applying AI for SCRM. Findings: The article concludes with a conceptual typology of challenges in applying AI for SCRM defined by their scope and nature along with the selected illustrative practices. Practical Implications: Empirical case study data based illustrative practices serve as research indicators or practical checklist entries for empirical evaluation of the level in progress towards the application of AI in SCRM. They also could be used as guidelines setting a direction for needed improvements in the way of applying AI for SCRM. Originality/Value: This research contributes to the SCRM literature by defining the typology of challenges according to their scope and nature in applying AI for SCRM in the context of risk management maturity.

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“…Ryan Chan, Adam Morse Artificial intelligence involves the theory, practice, and design of systems that exhibit functional human intelligence characteristics such as perception, problem solving, and planning. 1 Further, artificial intelligence has been defined as the study of intelligent agents or agents that possess these human-like traits, and can interact with their environment through various sensory and input mechanisms to formulate an action in order to achieve a goal or outcome. 2 These unique properties have gained a wide interest from interdisciplinary communities, which are investigating the potential applications of intelligent-enabled technologies.…”

Section: Artificial Intelligence-enabled Technologies and Clinical Dementioning

confidence: 99%