Organizational Decision-Making Structures in the Age of Artificial Intelligence

Shrestha, Yash Raj; Ben-Menahem, Shiko M.; Krogh, Georg von

doi:10.1177/0008125619862257

Cited by 369 publications

(233 citation statements)

References 20 publications

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“…Recent advances in AI software allow an increasing number of conversational agents to offer natural interactions between humans and their machine agent counterparts [ 11 , 12 ]. However, drawbacks such as biased and opaque decision-making leading to limited trust in the final outcomes still exist and are only partially solved [ 60 ]. Combined with the functional difficulty of needing large datasets for algorithmic training, this could explain the overall small number of existing applications [ 61 ].…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review

Schachner¹,

Keller²,

Wangenheim³

2020

J Med Internet Res

160

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Background A rising number of conversational agents or chatbots are equipped with artificial intelligence (AI) architecture. They are increasingly prevalent in health care applications such as those providing education and support to patients with chronic diseases, one of the leading causes of death in the 21st century. AI-based chatbots enable more effective and frequent interactions with such patients. Objective The goal of this systematic literature review is to review the characteristics, health care conditions, and AI architectures of AI-based conversational agents designed specifically for chronic diseases. Methods We conducted a systematic literature review using PubMed MEDLINE, EMBASE, PyscInfo, CINAHL, ACM Digital Library, ScienceDirect, and Web of Science. We applied a predefined search strategy using the terms “conversational agent,” “healthcare,” “artificial intelligence,” and their synonyms. We updated the search results using Google alerts, and screened reference lists for other relevant articles. We included primary research studies that involved the prevention, treatment, or rehabilitation of chronic diseases, involved a conversational agent, and included any kind of AI architecture. Two independent reviewers conducted screening and data extraction, and Cohen kappa was used to measure interrater agreement.A narrative approach was applied for data synthesis. Results The literature search found 2052 articles, out of which 10 papers met the inclusion criteria. The small number of identified studies together with the prevalence of quasi-experimental studies (n=7) and prevailing prototype nature of the chatbots (n=7) revealed the immaturity of the field. The reported chatbots addressed a broad variety of chronic diseases (n=6), showcasing a tendency to develop specialized conversational agents for individual chronic conditions. However, there lacks comparison of these chatbots within and between chronic diseases. In addition, the reported evaluation measures were not standardized, and the addressed health goals showed a large range. Together, these study characteristics complicated comparability and open room for future research. While natural language processing represented the most used AI technique (n=7) and the majority of conversational agents allowed for multimodal interaction (n=6), the identified studies demonstrated broad heterogeneity, lack of depth of reported AI techniques and systems, and inconsistent usage of taxonomy of the underlying AI software, further aggravating comparability and generalizability of study results. Conclusions The literature on AI-based conversational agents for chronic conditions is scarce and mostly consists of quasi-experimental studies with chatbots in prototype stage that use natural language processing and allow for multimodal user interaction. Future research could profit from evidence-based evaluation of the AI-based conversational agents and comparison thereof within and between different chronic health conditions. Besides increased comparability, the quality of chatbots developed for specific chronic conditions and their subsequent impact on the target patients could be enhanced by more structured development and standardized evaluation processes.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review

Schachner¹,

Keller²,

Wangenheim³

2020

J Med Internet Res

160

View full text Add to dashboard Cite

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“…How do we preserve our safety and values? Further, as our familiar methods of interfacing with computing systems (e.g., screens and keyboards) are superseded, what consequences will arise when AI-enabled systems surround us, sensing and responding to us, and making decisions (Shrestha et al, 2019)? To this end, Himma (2009) asked if it is indeed possible to produce artificially intelligent moral agents, while Bostrom (2014) discussed ways of responding to AI that becomes 'super intelligent', being fundamentally different than human intelligence, 2 surpassing it and transforming societies in unimaginable ways (Van Rijmenam, 2019).…”

Section: Ai Agents and Challenges For Organisational Theorisingmentioning

confidence: 99%

Revising the ‘science of the organisation’: theorising AI agency and actorhood

Rijmenam

Logue

2020

Innovation

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Artificial intelligence is a central technology underpinning the fourth industrial revolution, driving dramatic changes in contemporary cyber-physical systems and challenging existing ways of theorising organisations and management. AI agency and the rise of the artificially intelligent agent are both fundamentally different and yet increasingly similar to human agency in terms of intentionality and reflexivity. As 'Child AI' emerges-AI that is created by other AI-the early human design and interaction becomes increasingly distant and removed. These developments, while seemingly futuristic, change the human-technology interface through which we organise. In this essay, we explore understandings of AI agency, capability, and governance, and present implications for organisatonal theorising in sociomateriality, actor-network theory, institutional theory and the behavioral theory of the firm. We contribute to a growing and reflexive research agenda that can accommodate and regenerate theorising around this significant technological advancement.

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“…As computational capacity and speed increase, and as data sets available to support decisions grow, the frontier of substitutability of AI for human decision-making shifts. Shrestha et al (2019) suggest several possibilities, as follows:…”

Section: Ai In Management Decision-makingmentioning

confidence: 99%

“…It is this latter alternative which reflects most closely the topic of this paper. Shrestha et al (2019) also suggest that the appropriateness of these alternatives, and in particular the likelihood of appropriateness of full delegation to AI, depends upon:…”

Section: Ai In Management Decision-makingmentioning

confidence: 99%

Artificial intelligence (AI) in strategic marketing decision-making: a research agenda

et al. 2020

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PurposeThe purpose of this article is to review literature and thinking about the application of AI in strategic situations and to identify the research that is needed in the area of applying AI to strategic marketing decisions. Design/methodology/approachReview of literature and consultation with marketing experts who were invited to contribute to the article. FindingsThere is little research into applying AI to strategic marketing decision-making. This is needed as the frontier of AI application to decision-making is moving in many management areas from operational to strategic. Given the competitive nature of such decisions and the insights from applying AI to defence and similar areas, it is time to focus on applying AI to strategic marketing decisions.There are strong implications for all businesses, particularly large businesses in competitive industries, where failure to deploy AI in the face of competition from firms who have deployed AI to improve their decisionmaking could be dangerous. Social implicationsThe public sector is a very important marketing decision-maker. Although in most cases it does not operate competitively, it must still make decisions about making different services available to different citizens and identify the risks of not providing services to certain citizens, so this article is relevant to the public sector. Originality/valueThis is one of the first articles to probe deployment of AI in strategic marketing decision-making.

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Organizational Decision-Making Structures in the Age of Artificial Intelligence

Cited by 369 publications

References 20 publications

Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review

Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review

Revising the ‘science of the organisation’: theorising AI agency and actorhood

Artificial intelligence (AI) in strategic marketing decision-making: a research agenda

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