Artificial Intelligence for Business

Akerkar, Rajendra

doi:10.1007/978-3-319-97436-1

Cited by 76 publications

(45 citation statements)

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“…The inclination of AI solicitation and adoption is not up to the satisfaction level and still not return effectively to speedily developing intelligence competences (Chui et al, 2018, Luo et al, 2019. Also, businesses every so often may short-term a business atmosphere of uncertainty and bitterness towards each other within corresponding businesses and countries (Akerkar, 2019, Burgess, 2017. This may be one of the reason for data collection, analytics, and evolving information and knowledge for AI business approach.…”

Section: Business Model Transformationmentioning

confidence: 99%

“…Furthermore, most of the undeveloped and underdeveloped countries is not having potential infrastructure for digital transformation (Fountaine, et al, 2019, Liu, et al, 2020, Brock et al, 2019.The AI based business are still beyond them for establishment of essential digital data infrastructure enlargement (Gursoy et al, 2019, Chui et al, 2018. The absence of digital infrastructure, lack opportunity, disappoints data openings and digital innovations, creating it thoughtprovoking to address business data, information and knowledge cravings su ciently -leave-taking businesses with archaic data, information, and intelligence (Chui et al, 2018, Akerkar, 2019, Muthuveloo et al, 2013. Although, the developed countries like United States, UK, European countries and some of the Asian countries like China , Japan, Korea, India and many more is having good infrastructure for AI based business (Liu, et al,., 2020, Gursoy et al, 2019, Schank, 1991.They have competent enough software developers to apply cutting edge decision making technology.…”

Section: Business Model Transformationmentioning

confidence: 99%

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AI Business Model: An Integrative Business Approach

Mishra

Tripathi

2020

Preprint

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Artificial Intelligence is the ecosphere’s prevalent and most comprehensive general knowledge and common-sense cognitive engine. The Artificial Intelligence (AI) business platform model is virtually at affluence with cloud SaaS model. It concerns AI solutions that can work together on top layer of the other digital systems, like a Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) business system. AI admittances in digital data fluid through these coordination, fueling business enhancements over phase. In this business model, the business will safekeeping a recurrent subscription. This study endeavors to emphasis on the preemptive side of the use of AI and Machine learning (ML) technology to enterprise digital platform business model innovation and business dynamics.

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Section: Business Model Transformationmentioning

confidence: 99%

Section: Business Model Transformationmentioning

confidence: 99%

AI Business Model: An Integrative Business Approach

Mishra

Tripathi

2020

Preprint

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“…With the rapid development of technologies, businesses have been placed in new and different competitive situations [2]. To gain an edge, organizations attempt to progress towards optimal decision-making [11].…”

Section: Introductionmentioning

confidence: 99%

Cognitive biases in developing biased Artificial Intelligence recruitment system

Soleimani¹,

Intezari²,

Taşkın³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Artificial Intelligence (AI) in a business context is designed to provide organizations with valuable insight into decision-making and planning. Although AI can help managers make decisions, it may pose unprecedented issues, such as datasets and implicit biases built into algorithms. To assist managers with making unbiased effective decisions, AI needs to be unbiased too. Therefore, it is important to identify biases that may arise in the design and use of AI. One of the areas where AI is increasingly used is the Human Resources recruitment process. This article reports on the preliminary findings of an empirical study answering the question: how do cognitive biases arise in AI? We propose a model to determine people's role in developing AI recruitment systems. Identifying the sources of cognitive biases can provide insight into how to develop unbiased AI. The academic and practical implications of the study are discussed.

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“…Natural language processing has also contributed to developing the area of information retrieval, which is fueled by the currently exponential growth rate of text data on the World Wide Web. Content and link analysis of web pages, text mining, extraction of specified information from documents, automatic classification, and personalized agents hunting for information of interest to a specific individual are some of the active areas associated with information retrieval today (Akerkar, 2019). With the available information far exceeding the limits of human imagination, the named areas are of high relevance for assessing ISFs.…”

Section: Introductionmentioning

confidence: 99%