Integrating intuition and artificial intelligence in organizational decision-making

Artificial intelligence (AI) is fundamentally changing organizational decision-making processes. With the abilities to self-learn and to improve decision quality, AI is now taking over many decision responsibilities that were formerly assigned to humans alone. However, the effectiveness of AI for ill-structured and uncertain decision environments is still in question. In such decision contexts that have no precedent on which to base a solution, humans have historically relied on their intuition to make decisions. Yet intuition, too, has been found to have weaknesses that restrict decision quality. Therefore, this article introduces a decision-making model that effectively integrates the strengths of both intuition and AI while minimizing the vulnerabilities of each method. The model specifies when and how both modes should be combined for effective organizational decision-making. In addition, the article presents important future research considerations relating to AI for both practitioners and academics.     

Decision augmentation and automation with artificial intelligence: Threat or opportunity for managers?

Artificial intelligence (AI) has emerged as a promising and increasingly available technology for managerial decision-making. With the adoption of AI-enabled software, organizations can leverage various benefits of the technology, but they also have to consider the intended and unintended consequences of using the technology for managerial roles. It is still unclear whether managers will benefit from enhancing their abilities with AI-enabled software or become powerless puppets that do more than announce AI-enabled software results. Our research has revealed distinct ways in which organizations can use AI-enabled decision-making solutions: as tools or novelties, for decision augmentation or automation, and as either a voluntary or a mandatory option. In this article, we discuss the implications of each of these combinations on the relevant managers. We consider outcomes related to managerial job design and derive practical advice for organizational designers and managers who work with AI. Our outcomes provide guidance on how to deal with the conflict-riddled relationship between managers and technology with regard to capabilities, responsibilities, and acceptance of AI-enabled software.     

Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making

Artificial intelligence (AI) has penetrated many organizational processes, resulting in a growing fear that smart machines will soon replace many humans in decision making. To provide a more proactive and pragmatic perspective, this article highlights the complementarity of humans and AI and examines how each can bring their own strength in organizational decision-making processes typically characterized by uncertainty, complexity, and equivocality. With a greater computational information processing capacity and an analytical approach, AI can extend humans’ cognition when addressing complexity, whereas humans can still offer a more holistic, intuitive approach in dealing with uncertainty and equivocality in organizational decision making. This premise mirrors the idea of intelligence augmentation, which states that AI systems should be designed with the intention of augmenting, not replacing, human contributions.     

The convenience of shopping via voice AI: Introducing AIDM

The purpose of this paper is to propose an updated view of consumer choice based on AI and inherent convenience addiction to smart speakers. Following the MacInnis framework for developing conceptual contributions of summarization, integration, and delineation, we review the current consumer decision-making literature and theory to demonstrate consumers' increasing tendency to outsource decisions to AI. Today's customers value convenience: the less time and effort they spend on a purchase, the better they perceive the transaction. AI is taking convenience to higher levels for consumers as they outsource their decisions to bots and inherent algorithms. This is particularly accurate for low-involvement everyday purchases. Our study's contribution is fourfold. First, we introduce a new model of AI-influenced decision-making (AIDM) processes. Second, our conceptual model suggests that managers need to change their interpretation of their customers' decision-making-processes in the new, AI-influenced marketplace. The shift in consumers' behavior toward reliance on home voice bots for purchase has significant implications for the retail sector. Third, our model differentiates between high and low involvement AI-influenced decision-making processes. Fourth, our study highlights how branding as we know it is challenged in an AI-dominated environment.     

Embedding Decision Support in E-Sourcing Tools: Quotes, A Case Study

In the everyday business world, the sourcing process of multiple goods and services usually involves complex negotiations that include discussion of product and service features. Currently, this is a high-cost process due to the scarce use of tools that streamline negotiations and assist purchasing managers' and providers' decision-making. With the advent of Internet-based technologies, it became feasible the idea of tools enabling low-cost, assisted, fluid, on-line dialogs between buyer enterprises and their providers located anywhere. This article presents Quotes, an iSOCO's commercial application that, in addition to cover the whole sequence of sourcing tasks, incorporates decision support facilities based on Artificial Intelligence (AI) techniques that successfully address highly challenging issues in automated negotiation within a single and coherent framework.     

Virtual influencers as an emerging marketing theory: A systematic literature review

Over the past few years, the popularity of influencers on social media (SM) has increased, and influencer marketing has become an important element in companies' marketing strategies. This has resulted in significant interest from researchers and practitioners; consequently, the number of publications devoted to the topic of influencers and influencer marketing has risen. Simultaneously, computer-generated avatars and virtual influencers (VIs), including those using artificial intelligence (AI) and machine learning, have begun to emerge and gain popularity in the SM space. Thus far, research on these topics has been limited, with few studies examining the issue from different perspectives. Given the growing potential of VIs' inclusion in the consumer decision-making process, and this being a developing field, a comprehensive and critical review of existing research on this subject is urgently needed. In response, this study consolidates the current state of research on virtual, AI, and computer-generated influencers. A systematic review of peer-reviewed articles was conducted to identify key themes and dominant concepts. An analysis of 35 articles provides an understanding of this phenomenon, shedding light on the mechanisms underlying the appeal of VIs and their role in shaping consumer attitudes and behaviors. Based on the analysis, the main thematic streams from the study are presented. Further, research gaps have been identified, and recommendations made for future research directions.     

From data to action: How marketers can leverage AI

Artificial intelligence (AI) is at the forefront of a revolution in business and society. AI affords companies a host of ways to better understand, predict, and engage customers. Within marketing, AI’s adoption is increasing year-on-year and in varied contexts, from providing service assistance during customer interactions to assisting in the identification of optimal promotions. But just as questions about AI remain with regard to job automation, ethics, and corporate responsibility, the marketing domain faces its own concerns about AI. With this article, we seek to consolidate the growing body of knowledge about AI in marketing. We explain how AI can enhance the marketing function across nine stages of the marketing planning process. We also provide examples of current applications of AI in marketing.     

A Framework for Collaborative Artificial Intelligence in Marketing

We develop a conceptual framework for collaborative artificial intelligence (AI) in marketing, providing systematic guidance for how human marketers and consumers can team up with AI, which has profound implications for retailing, which is the interface between marketers and consumers. Drawing from the multiple intelligences view that AI advances from mechanical, to thinking, to feeling intelligence (based on how difficult for AI to mimic human intelligences), the framework posits that collaboration between AI and HI (human marketers and consumers) can be achieved by 1) recognizing the respective strengths of AI and HI, 2) having lower-level AI augmenting higher-level HI, and 3) moving HI to a higher intelligence level when AI automates the lower level. Implications for marketers, consumers, and researchers are derived. Marketers should optimize the mix and timing of AI-HI marketing team, consumers should understand the complementarity between AI and HI strengths for informed consumption decisions, and researchers can investigate innovative approaches to and boundary conditions of collaborative intelligence.     

Machines augmenting entrepreneurs: Opportunities (and threats) at the Nexus of artificial intelligence and entrepreneurship

Artificial intelligence (AI) refers to machines that are trained to perform tasks associated with human intelligence, interpret external data, learn from that external data, and use that learning to flexibly adapt to tasks to achieve specific outcomes. This paper briefly explains AI and looks into the future to highlight some of AI's broader and longer-term societal implications. We propose that AI can be combined with entrepreneurship to represent a super tool. Scholars can research the nexus of AI and entrepreneurship to explore the possibilities of this potential AI-entrepreneurship super tool and hopefully direct its use to productive processes and outcomes. We focus on specific entrepreneurship topics that benefit from AI's augmentation potential and acknowledge implications for entrepreneurship's dark side. We hope this paper stimulates future research at the AI-entrepreneurship nexus.Executive summaryArtificial intelligence (AI) refers to machines that are trained to perform tasks associated with human intelligence, interpret external data, learn from that external data, and use that learning to flexibly adapt to tasks to achieve specific outcomes. Machine learning is the most common form of AI and largely relies on supervised learning—when the machine (i.e., AI) is trained with labels applied by humans. Deep learning and adversarial learning involve training on unlabeled data, or when the machine (via its algorithms) clusters data to reveal underlying patterns.AI is simply a tool. Entrepreneurship is also simply a tool. How they are combined and used will determine their impact on humanity. While researchers have independently developed a greater understanding of entrepreneurship and AI, these two streams of research have primarily run in parallel. To indicate the scope of current and future AI, we provide examples of AI (at different levels of development) for four sectors—customer service, financial, healthcare, and tertiary education. Indeed, experts from industry research and consulting firms suggest many AI-related business opportunities for entrepreneurs to pursue.Further, we elaborate on several of these opportunities, including opportunities to (1) capitalize on the “feeling economy,” (2) redistribute occupational skills in the economy, (3) develop and use new governance mechanisms, (4) keep humans in the loop (i.e., humans as part of the decision making process), (5) expand the role of humans in developing AI systems, and (6) expand the purposes of AI as a tool. After discussing the range of business opportunities that experts suggest will prevail in the economy with AI, we discuss how entrepreneurs can use AI as a tool to help them increase their chances of entrepreneurial success. We focus on four up-and-coming areas for entrepreneurship research: a more interaction-based perspective of (potential) entrepreneurial opportunities, a more activities-based micro-foundation approach to entrepreneurial action, a more cognitively hot perspective of entrepreneurial decision making and action, and a more compassionate and prosocial role of entrepreneurial action. As we discuss each topic, we also suggest opportunities to design an AI system (i.e., entrepreneurs as potential AI designers) to help entrepreneurs (i.e., entrepreneurs as AI users).AI is an exciting development in the technology world. How it transforms markets and societies depends in large part on entrepreneurs. Entrepreneurs can use AI to augment their decisions and actions in pursuing potential opportunities for productive gains. Thus, we discuss entrepreneurs' most critical tasks in developing and managing AI and explore some of the dark-side aspects of AI. Scholars also have a role to play in how entrepreneurs use AI, but this role requires the hard work of theory building, theory elaboration, theory testing, and empirical theorizing. We offer some AI topics that we hope future entrepreneurship research will explore. We hope this paper encourages scholars to consider research at the nexus of AI and entrepreneurship.     

How artificial intelligence will affect the future of retailing

Artificial intelligence (AI) will substantially impact retailing. Building on past research and from interviews with senior managers, we examine how senior retailing managers should think about adopting AI, involving factors such as the extent to which an AI application is customer-facing, the amount of value creation, whether the AI application is online, and extent of ethics concerns. In addition, we highlight that the near-term impact of AI on retailing may not be as pronounced as the popular press might suggest, and also that AI is likely to be more effective if it focuses on augmenting (rather than replacing) managers’ judgments. Finally, while press coverage typically involves customer-facing AI applications, we highlight that a lot of value can be obtained by adopting non-customer-facing applications. Overall, we remain very optimistic as regards the impact of AI on retailing. Finally, we lay out a research agenda and also outline implications for practice.     

Siri,Siri, in my hand: Who’s the fairest in the land? On the interpretations,illustrations, and implications of artificial intelligence

Artificial intelligence (AI)—defined as a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation—is a topic in nearly every boardroom and at many dinner tables. Yet, despite this prominence, AI is still a surprisingly fuzzy concept and a lot of questions surrounding it are still open. In this article, we analyze how AI is different from related concepts, such as the Internet of Things and big data, and suggest that AI is not one monolithic term but instead needs to be seen in a more nuanced way. This can either be achieved by looking at AI through the lens of evolutionary stages (artificial narrow intelligence, artificial general intelligence, and artificial super intelligence) or by focusing on different types of AI systems (analytical AI, human-inspired AI, and humanized AI). Based on this classification, we show the potential and risk of AI using a series of case studies regarding universities, corporations, and governments. Finally, we present a framework that helps organizations think about the internal and external implications of AI, which we label the Three C Model of Confidence, Change, and Control.     

Innovation analytics: Leveraging artificial intelligence in the innovation process

Artificial intelligence (AI) is about imbuing machines with a kind of intelligence that is mainly attributed to humans. Extant literature—coupled with our experiences as practitioners—suggests that while AI may not be ready to completely take over highly creative tasks within the innovation process, it shows promise as a significant support to innovation managers. In this article, we broadly refer to the derivation of computer-enabled, data-driven insights, models, and visualizations within the innovation process as innovation analytics. AI can play a key role in the innovation process by driving multiple aspects of innovation analytics. We present four different case studies of AI in action based on our previous work in the field. We highlight benefits and limitations of using AI in innovation and conclude with strategic implications and additional resources for innovation managers.     

Team challenges: Is artificial intelligence the solution?

Artificial intelligence (AI) is emerging as a potential growth area for facilitating the improvement and development of teams in the workplace. AI, as used in the team context, is currently underdeveloped and limited, thus reducing the wide-scale adoption and implementation of AI to improve team effectiveness. The use of AI to provide team diagnostics and improvements represents a significant shift in the approach organizations currently use to facilitate and strengthen effective teamwork. We describe the challenges involved in developing team effectiveness in organizations and the potential application of AI to improve teamwork. Further, we report on our experiences using AI in business school student project teams, the important advantages and disadvantages that emerged from this, and insights for future consideration when adopting and implementing AI in the workplace. Based on our use of AI and our experience training high-performing teams, we propose a multistep process for analyzing and improving teams in organizations.     

The social significance of AI in retail on customer experience and shopping practices

This paper draws on practice-informed, ethnographic research to develop an understanding of the novel social consequences and opportunities afforded from consumers' interactions with AI digital humans as part of the in-store shopping experience. We reveal and interrogate consumers’ experiences with AI digital humans in an exploratory study undertaken during the launch phase of an in-store kiosk digital store greeter in a flagship store of a large national technology and appliance chain. Our findings contribute to understanding the social significance of AI in retail on customer experience (CX) and the managerial implications of consumers interactions with AI digital humans are described and discussed.     

Social anxiety as a moderator in consumer willingness to accept AI assistants based on utilitarian and hedonic values

This study develops a comprehensive research model to explain user willingness to accept AI assistants, and the acceptance path pertaining to this process. User data was used to test how the advantages of AI assistant (accuracy, responsiveness, compatibility, anthropomorphism, & affinity) influence consumer utilitarian and hedonic value, and explore how their willingness to accept AI assistants is affected by their value perceptions. This research also examines whether social anxiety moderates the relationship between AI assistant advantages and utilitarian/hedonic value. The study reveals that AI assistant advantages are important factors affecting the utilitarian/hedonic value perceived by users, which further influence user willingness to accept AI assistants. The relationships between AI assistant advantages and utilitarian and hedonic value are affected differently by social anxiety. Marketers and managers in the AI context can refer to the study methods to help improve AI assistants and develop more effective marketing strategies for product promotion.     

Artificial Intelligence and Marketing: Pitfalls and Opportunities

This article discusses the pitfalls and opportunities of AI in marketing through the lenses of knowledge creation and knowledge transfer. First, we discuss the notion of “higher-order learning” that distinguishes AI applications from traditional modeling approaches, and while focusing on recent advances in deep neural networks, we cover its underlying methodologies (multilayer perceptron, convolutional, and recurrent neural networks) and learning paradigms (supervised, unsupervised, and reinforcement learning). Second, we discuss the technological pitfalls and dangers marketing managers need to be aware of when implementing AI in their organizations, including the concepts of badly defined objective functions, unsafe or unrealistic learning environments, biased AI, explainable AI, and controllable AI. Third, AI will have a deep impact on predictive tasks that can be automated and require little explainability, we predict that AI will fall short of its promises in many marketing domains if we do not solve the challenges of tacit knowledge transfer between AI models and marketing organizations.     

人工智能技术变革对国际贸易的影响

人工智能技术的快速发展正催生第四次工业革命,可能引发全球价值链深度重构和世界经贸格局重大变革。世界主要经济强国将发展人工智能技术作为争夺新一轮产业竞争优势的重要战略抓手。本文基于全球价值链视角研究人工智能技术变革对国际贸易的影响,我们发现人工智能技术变革可能推动国际贸易规模扩大,提升服务贸易份额,并促进国际贸易交易模式平台化、小宗化,可为中小企业创造更多参与国际贸易的机会。然而,人工智能技术变革也可能通过降低企业劳动力需求从而对我国等发展中国家的出口拉动型增长模式造成严重的潜在威胁。为应对人工智能技术变革,我国应部署并强化对人工智能产业发展的政策支持,加快培育制造业国际竞争新优势,大力推动先进制造业与现代生产性服务业深度融合发展,全面促进"中国制造"攀升全球价值链中高端。     

Artificial intelligence and knowledge management: A partnership between human and AI

Emerging artificial intelligence (AI) capabilities will likely pervade nearly all organizational contours and activities, including knowledge management (KM). This article aims to uncover opportunities associated with the implementation of emerging systems empowered by AI for KM. In doing so, we explicate the potential role of AI in supporting fundamental dimensions of KM: creation, storage and retrieval, sharing, and application of knowledge. We then propose practical ways to build the partnership between humans and AI in supporting organizational KM activities and provide several implications for the development and management of AI systems based on the components of people, infrastructures, and processes.     

Trust me,I'm an influencer! - Causal recipes for customer trust in artificial intelligence influencers in the retail industry

Prior examinations of relationship development and the leverage of trust among artificial intelligence (AI) influencers and followers have been few. This study employed complexity theory to understand the main causal recipes that can lead to high trust in AI influencers. Asymmetrical fuzzy set qualitative comparative analysis (fsQCA) was used to explore the recipes that can drive high customer trust. Data were collected from 683 consumers who are familiar with AI influencers in Saudi Arabia. Our findings indicated that no single factor is sufficient to drive trust in influencers, but five causal recipes were explored for their power to secure high levels of trust in AI influencers. The findings revealed that a configuration of source attractiveness (i.e., physical attractiveness, homophily), source credibility (i.e., authenticity, expertise) and congruences (i.e., influencer, product, consumer) act as driver of consumers' trust in an AI influencer. These results are useful for practitioners since they provide new methods for boosting trust in AI influencers.     

Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential

Artificial intelligence (AI) and machine learning (ML) may save money and improve the efficiency of business processes, but these technologies can also destroy business value, sometimes with grave consequences. The inability to identify and manage that risk can lead some managers to delay the adoption of these technologies and thus prevent them from realizing their potential. This article proposes a new framework by which to map the components of an AI solution and to identify and manage the value-destruction potential of AI and ML for businesses. We show how the defining characteristics of AI and ML can threaten the integrity of the AI system’s inputs, processes, and outcomes. We then draw from the concepts of value-creation content and value-creation process to show how these risks may hinder value creation or even result in value destruction. Finally, we illustrate the application of our framework with an example of the deployment of an AI-powered chatbot in customer service, and we discuss how to remedy the problems that arise.