Emirates' AI Innovation Challenge: Enhancing Customer Experience through Personalized In-Flight Services

Although the prevalence of artificial intelligence (AI) innovations is on the rise, firms frequently report failures and setbacks in their development and implementation of AI innovation efforts. One common issue behind many failing AI initiatives is that they are organized just like other information technology (IT) innovation efforts. To elucidate why and how the production of AI and IT innovations may need to be managed differently, this study juxtaposes these two types of innovations based on two key dimensions of the Schumpeterian framework: the form (product vs. process) and magnitude (radical vs. incremental) of innovations. By analyzing a matched sample of AI and IT patents, we found robust evidence that AI innovations are less radical and more process oriented than comparable IT innovations. Drawing upon our empirical discovery, we developed a conceptual framework to suggest a new way to think about organizing AI innovation. Our research contributes to the literature and practice on AI innovation by illuminating the comparative differences between AI innovations and other IT innovations and advancing a set of empirically derived propositions on how firms may be able to better manage their AI innovation activities.

Artificial intelligence (AI) innovation offers promising opportunities for underperforming firms but entails risks of failure. Whether such firms should proactively increase or conservatively decrease their AI engagement remains a controversial but important issue. This paper combines the attention-based view and resource orchestration theory to investigate this issue using panel data on Chinese A-share listed companies from 2008 to 2022. Our results reveal that performance shortfalls significantly inhibit AI innovation. The mechanism analysis confirms that performance shortfalls negatively affect AI innovation by reducing executive attention to AI. The moderation analysis indicates that government support for AI can mitigate this inhibitory effect. Heterogeneity tests reveal that the inhibitory effect is weaker when firms have more abundant external AI resources and stronger risk-taking internal governance. Further analysis shows that performance shortfalls negatively affect various R&D models for AI innovation and different types of AI technologies. Nevertheless, underperforming firms can enhance their long-term profitability by continuing AI innovation. These findings provide theoretical and practical guidance for underperforming firms in making AI innovation decisions.

A stochastic production frontier model for evaluating the performance efficiency of artificial intelligence investment worldwide

This study investigates the impact of artificial intelligence (AI) innovation on economic growth in East Asia (China, Japan, and South Korea) from 2010 to 2023, using AI patent filings as a proxy for technological advancement. A panel data approach is employed, incorporating fixed effects, random effects, and pooled ordinary least squares (OLS) models to examine the relationship between AI innovation and GDP growth. Panel cointegration tests assess long-run equilibrium relationships, while the Granger non-causality test determines the direction of causality. The results indicate that AI innovation significantly contributes to GDP growth, reinforcing the role of technological progress in economic expansion. Trade openness is also positively associated with economic performance. However, gross capital formation exhibits a counterintuitive negative effect, suggesting inefficiencies in capital allocation or diminishing returns. Inflation has a mild yet statistically significant impact on growth. This study provides empirical evidence on the role of AI-driven innovation in shaping East Asian economies. The findings offer valuable insights for policymakers, emphasizing the need for strategies that enhance AI adoption, optimize capital investment, and leverage trade to sustain economic growth in the AI era.

Purpose This study aims to examine how artificial intelligence (AI) innovation influences open innovation activities in new ventures, with a focus on the mediating role of innovation niches and the alignment between inbound and outbound open innovation in shaping entrepreneurial performance. Despite growing interest in AI-driven strategies, limited research explores these interactions in the context of emerging economies. Design/methodology/approach Using survey data from 329 high-tech venture founders in Vietnam, the study applies polynomial regression with response surface analysis to test the proposed hypotheses on the relationships between AI innovation, innovation niches, open innovation and entrepreneurial performance. Findings The results show that AI innovation enhances both inbound and outbound open innovation and strengthens innovation niches, which serve as key mediators. Additionally, the polynomial regression with response surface analysis reveals that entrepreneurial performance improves when inbound and outbound open innovation are aligned but declines under conditions of imbalance. This highlights the importance of congruence in open innovation strategies. Practical implications The findings offer actionable insights for entrepreneurs on leveraging AI and strategically balancing inbound and outbound open innovation, especially in emerging markets where resources and innovation capabilities are often limited. Originality/value This study contributes to the literature by integrating AI innovation, innovation niches and open innovation strategies into a unified framework. It offers novel empirical insights using PRRSA and responds to recent calls to understand how AI influences entrepreneurial strategies, particularly in resource-constrained and transitional economies like Vietnam.

Artificial Intelligence (AI) is increasingly discussed as a tool that can support speech and language therapy (SLT). However, clinical adoption of AI requires improved AI literacy among clinicians. AI is a rapidly evolving and often inconsistently defined field that can be difficult to navigate. Despite the definition provided by the EU AI Act, AI terminology can feel abstract for non-technical readers. To provide a foundational understanding of AI tailored for SLTs, by translating complex concepts into accessible language and organising them across three levels: (i) AI techniques (how AI works); (ii) AI capabilities (what AI can do) and (iii) clinical applications (how AI can support SLT). This tutorial is informed by foundational AI literature, established AI taxonomies, relevant SLT literature and regulatory and ethical guidelines. Clinical analogies are used to explain technical concepts, with additional technical detail signposted where relevant. Existing and conceptual examples illustrate the relevance of AI across paediatric SLT practice. This tutorial provides: (i) a clinician-focussed interpretation of the EU AI Act definition; (ii) an organisation of key AI concepts into techniques, capabilities and clinical applications; (iii) a production-line model for mapping clinical needs to AI design choices and (iv) a practice-focussed discussion of ethical and regulatory considerations. AI is best understood as a set of techniques that enable specific capabilities, which in turn support clinical applications. This tutorial promotes the safe, ethical and accountable use of AI as a tool that can support rather than replace clinicians. What is already known on this subject Current Artificial Intelligence (AI) literature is typically designed for technical audiences, making it difficult for clinicians to interpret. This can hinder the effective and responsible integration of AI into clinical practice. What this paper adds to the existing knowledge This tutorial provides a clinician-focussed explanation of AI, structured across three levels: (i) AI techniques (how AI works); (ii) AI capabilities (what AI can do) and (iii) clinical applications (how AI supports practice) in paediatric speech and language therapy. It also addresses key challenges, ethical considerations and regulatory requirements relevant to clinical contexts. What are the potential or actual clinical implications of this work? This tutorial lays the groundwork for informed engagement with emerging AI tools. It prepares clinicians to evaluate how different AI techniques and capabilities may support core clinical tasks (e.g., assessment, therapy planning and delivery).

In this study, artificial intelligence (AI) orientation, AI capabilities, as well as process-oriented dynamic capabilities (PDCs) within the realm of AI business value creation, are unpacked through multiple case studies in Hong Kong. We propose a conceptual framework suggesting that AI resources enable organizations to develop PDCs, manifesting in several abilities, thereby contributing to business value. In addition, the case study’s findings indicate that AI capabilities developed by organizations correlate with their AI orientation, which is their overall strategic direction and aspiration of employing AI technology. Apart from basic AI capabilities, AI-oriented organizations would develop advanced AI capabilities. The proposed conceptual framework and findings can guide and assist practitioners in utilizing AI resources and building AI capabilities. This study also enriches the growing body of research on AI and contributes to the limited understanding of AI capabilities in the extant literature.

Background: Innovations in artificial intelligence (AI) and machine learning (ML) are poised to transform stroke care, particularly for Neuro-Cardiac Programs (NCP) within both academic and community hospital systems. Purpose: Given AI's success in large-vessel occlusion (LVO) detection and perfusion mapping delivered to our smartphones, the next leap for this "Ghost in the Machine" technology seems to be into the world of NCP: AI-enhanced logistics have started to help with cardiac monitoring after cryptogenic, large-artery and small-vessel stroke, looking for atrial fibrillation (AF) with an insertable loop recorder (ILR) and/or external patch. Results: The 'CONNECT' study from UCSD demonstrated that AI can increase protocol efficiency and reduce patient wait-times for ILR; with more AF detected, fewer strokes may result as more patients receive anticoagulation or Left Atrial Appendage Closure (LAAC). Conclusion: Therefore, organically, the next AI and ML-enhanced NCP frontier may involve inter-departmental "Shared Decision-Making" (SDM) process with LAAC, and/or Patent Foramen Ovale (PFO), in appropriately selected patients. In this editorial, we explore AI's capability to disrupt current antiquated siloed communication tools, refine and streamline SDM processes and tailor patient-specific treatment plans, nevertheless advocating for intercalation of AI into NCP pathways in a secure, ethically-guided manner.

In today's rapidly evolving business landscape, harnessing artificial intelligence (AI) has become imperative for organizations striving to remain competitive. This concept paper explores the integration of AI in developing strategic marketing goals, aiming to provide a comprehensive framework for leveraging AI's capabilities effectively. The paper begins by outlining the current state of AI in marketing, highlighting its transformative potential across various facets such as customer segmentation, personalized targeting, predictive analytics, and automation. It underscores the necessity for businesses to embrace AI-driven approaches to stay ahead in an increasingly data-driven marketplace. Moreover, the concept paper delves into the strategic implications of AI adoption in marketing. It emphasizes the need for alignment between AI initiatives and overarching business objectives, emphasizing the role of AI in enhancing customer experience, optimizing resource allocation, and driving revenue growth. Furthermore, the paper addresses key considerations and challenges associated with integrating AI into marketing strategies, including data privacy concerns, ethical implications, and organizational readiness. It advocates for a structured approach to AI implementation, encompassing robust data governance, talent acquisition, and continuous monitoring to mitigate risks effectively. Drawing upon industry best practices and case studies, the concept paper elucidates practical methodologies for leveraging AI in setting strategic marketing goals. It emphasizes the importance of data-driven decision-making, iterative experimentation, and agile adaptation to capitalize on AI's potential for driving innovation and competitive advantage. In conclusion, the concept paper advocates for a proactive stance towards embracing AI in marketing strategy formulation. By strategically harnessing AI's capabilities, organizations can not only enhance their marketing effectiveness but also cultivate long-term sustainable growth in today's dynamic business environment. Keywords: AI, Strategic, Marketing, Environment, Goals, Business.

A substantial body of research highlights how stringent regulations disrupt innovators’ incentives and increase transaction costs, yet their information processing implication remains understudied. Building on information processing theory, we study how the interplay between formal and informal institutions shapes inventors’ information processing in developing artificial intelligence (AI) innovation. We examine the effect of stringent privacy regulations on AI innovation by exploiting the European Union’s General Data Protection Regulation (GDPR) announcement. We argue that, following the GDPR announcement, GDPR-affected countries experience lower national AI innovation rates than unaffected countries. Further, we postulate that this negative effect is weaker in GDPR-affected countries, marked by higher levels of individualism, masculinity, and indulgence, but stronger in the affected countries with higher levels of uncertainty avoidance, power distance, and long-term orientation. Our difference-in-differences analysis supports the proposed framework. Our research contributes to the international business literature by developing novel theoretical predictions at the intersection of comparative institutional analysis and national culture, explaining how privacy protection laws and cultural factors shape AI inventors’ information processing. Finally, this study provides insights into how inventors and entrepreneurs in countries with stringent privacy laws can leverage national culture to shape their AI innovation strategies and inform strategic decision-making.

Purpose This study aims to examine the impact of artificial intelligence (AI) innovation on debt default risk of construction firms. It also explores the moderating role of intellectual capital. Design/methodology/approach This study analyzes 846 firm-year observations from Chinese listed construction firms between 2007 and 2022. A two-way fixed-effects model is used to test the proposed hypotheses. Findings The findings of this study are as follows: (1) AI innovation significantly reduces debt default risk of construction firms. (2) The three dimensions of intellectual capital (i.e. human capital, structural capital, and relational capital) strengthen the mitigating effect of AI innovation on debt default risk, with human capital playing the most significant role. (3) AI innovation mitigates debt default risk through internal operational optimization and external relationship coordination. Practical implications This study offers empirical evidence on the negative AI innovation–debt default risk relationship of construction firms. It also provides actionable guidance for construction managers to improve the effectiveness of AI innovation by fostering the development of intellectual capital. Originality/value By providing empirical evidence for the mitigating effect of AI innovation on debt default risk of construction firms, this study fills a critical gap in the literature of AI innovation and construction risk management. It also provides academics and practitioners with novel insights for synergistically leveraging AI innovation and intellectual capital to manage debt default risk in the construction industry.

The rapid advancement of artificial intelligence (AI) technologies has highlighted the critical importance of data privacy within the framework of international trade law. This study aims to explore the dynamic interplay between AI innovation and data privacy regulations in the context of global trade. The primary objective is to understand how international data privacy laws influence AI development within trade activities and how AI advancements, in turn, affect data privacy compliance and public trust in international markets. This study employs system dynamics modeling to analyze the complex interplay between artificial intelligence (AI) innovation and data privacy regulations within the context of international trade law. These variables include AI innovation level (AIL), investment in AI R&D (I AI R&D), data privacy compliance level (DPC), public trust in AI (PTAI), regulatory framework strength (RFS), compliance costs (CCs), innovation incentives (II), and cross-border data flow regulations (CBDFRs). By modeling these interactions, the study seeks to provide insights into balancing technological innovation with robust privacy protections specific to international trade. The focus is on the interdependencies between AI research and development (R&D) investments, regulatory frameworks tailored to trade, data privacy compliance levels, public trust, and associated compliance costs within the context of international trade law. The model also considers the impact of cross-border data flow regulations and innovation incentives on these dynamics. A system dynamics approach was employed to create a numerical model that simulates the relationships and feedback loops among the identified variables. Initial values were set on the basis of plausible estimates, and the model was run over five years to observe the trajectories of each variable under various regulatory and innovation scenarios.

BackgroundImage-driven specialisms such as radiology and pathology are at the forefront of medical artificial intelligence (AI) innovation. Many believe that AI will lead to significant shifts in professional roles, so it is vital to investigate how professionals view the pending changes that AI innovation will initiate and incorporate their views in ongoing AI developments.ObjectiveOur study aimed to gain insights into the perspectives and wishes of radiologists and pathologists regarding the promise of AI.MethodsWe have conducted the first qualitative interview study investigating the perspectives of both radiologists and pathologists regarding the integration of AI in their fields. The study design is in accordance with the consolidated criteria for reporting qualitative research (COREQ).ResultsIn total, 21 participants were interviewed for this study (7 pathologists, 10 radiologists, and 4 computer scientists). The interviews revealed a diverse range of perspectives on the impact of AI. Respondents discussed various task-specific benefits of AI; yet, both pathologists and radiologists agreed that AI had yet to live up to its hype. Overall, our study shows that AI could facilitate welcome changes in the workflows of image-driven professionals and eventually lead to better quality of care. At the same time, these professionals also admitted that many hopes and expectations for AI were unlikely to become a reality in the next decade.ConclusionsThis study points to the importance of maintaining a “healthy skepticism” on the promise of AI in imaging specialisms and argues for more structural and inclusive discussions about whether AI is the right technology to solve current problems encountered in daily clinical practice.

This scholarly investigation delves into the transformative impact of Artificial Intelligence (AI) on enhancing customer experience in the business realm. The study's purpose was to meticulously examine the integration, evolution, and strategic implications of AI in business operations, particularly in customer engagement. A comprehensive literature review and detailed case study analysis constituted the core methodology, focusing on peer-reviewed articles and practical examples from diverse business sectors. This approach facilitated a multi-dimensional exploration, capturing both the technological advancements in AI and the associated implementation challenges within various business contexts. Central findings from this research underscore AI's evolution from an emerging technological tool to a fundamental component in customer-centric business strategies. AI's capabilities in personalizing customer interactions, automating support systems, and leveraging predictive analytics have revolutionized business-customer dynamics. However, this evolution is not without its challenges, including data privacy concerns, ethical considerations, and the need for skilled AI expertise. The study concludes that AI is a strategic asset, necessitating thoughtful integration into business models. It emphasizes the importance of a collaborative approach, where AI specialists and industry experts work synergistically to tailor AI solutions to specific business needs. Ethical considerations and maintaining customer trust are highlighted as pivotal in AI deployment strategies. The study recommends continuous innovation, investment in AI infrastructure and talent, and adherence to ethical AI practices. These measures are essential for businesses to enhance customer experiences and drive sustainable growth in the digital age
 Keywords: Artificial Intelligence, Customer Experience, Business Strategy, AI Integration, Ethical Considerations.

Artificial Intelligence (AI) is revolutionizing healthcare through its integration into various domains, significantly enhancing the efficiency, accuracy, and effectiveness of medical practices. This review explores the transformative impact of AI across multiple aspects of healthcare, including diagnostics, personalized treatment, drug discovery, surgery, and more. AI's capabilities in diagnostics and early detection are improving the precision and speed of disease identification, enabling earlier and more effective interventions. Personalized treatment approaches leverage AI to analyze patient data and tailor therapies to individual needs, optimizing outcomes and reducing adverse effects. AI-driven robotics in surgery offer enhanced precision, control, and minimally invasive options, leading to improved surgical outcomes and faster recovery times. Despite these advancements, the adoption of AI in healthcare presents challenges and ethical considerations, including data quality, algorithmic bias, patient privacy, and the responsible use of AI technologies. Addressing these issues is crucial for maintaining trust and ensuring equitable access to AI-powered healthcare solutions. AI's role in drug discovery and development is accelerating the creation of new therapies by optimizing predictive modeling, drug design, and clinical trials, thus reducing costs and speeding up the development process. Future trends and innovations in AI highlight ongoing advancements and the potential for further transformation in healthcare. These include advancements in natural language processing, AI-enhanced telemedicine, wearable health technologies, and ethical AI governance. As AI technology continues to evolve, its impact on healthcare will become increasingly significant, driving progress in patient care, operational efficiency, and medical research. Collaborative efforts among technologists, clinicians, researchers, and policymakers will be essential in harnessing AI's full potential while addressing the complexities and ethical challenges associated with its use. This review underscores the promise of AI to revolutionize healthcare and improve patient outcomes while emphasizing the need for responsible implementation and ongoing evaluation.