Human‐AI Collaboration in Creative Practice : An Integrated Adoption Framework for Organisational Excellence in Industry 5.0

ObjectivesWe aimed to evaluate a commercial artificial intelligence (AI) solution on a multicenter cohort of chest radiographs and to compare physicians' ability to detect and localize referable thoracic abnormalities with and without AI assistance.MethodsIn this retrospective diagnostic cohort study, we investigated 6,006 consecutive patients who underwent both chest radiography and CT. We evaluated a commercially available AI solution intended to facilitate the detection of three chest abnormalities (nodule/masses, consolidation, and pneumothorax) against a reference standard to measure its diagnostic performance. Moreover, twelve physicians, including thoracic radiologists, board-certified radiologists, radiology residents, and pulmonologists, assessed a dataset of 230 randomly sampled chest radiographic images. The images were reviewed twice per physician, with and without AI, with a 4-week washout period. We measured the impact of AI assistance on observer's AUC, sensitivity, specificity, and the area under the alternative free-response ROC (AUAFROC).ResultsIn the entire set (n = 6,006), the AI solution showed average sensitivity, specificity, and AUC of 0.885, 0.723, and 0.867, respectively. In the test dataset (n = 230), the average AUC and AUAFROC across observers significantly increased with AI assistance (from 0.861 to 0.886; p = 0.003 and from 0.797 to 0.822; p = 0.003, respectively).ConclusionsThe diagnostic performance of the AI solution was found to be acceptable for the images from respiratory outpatient clinics. The diagnostic performance of physicians marginally improved with the use of AI solutions. Further evaluation of AI assistance for chest radiographs using a prospective design is required to prove the efficacy of AI assistance.Key Points• AI assistance for chest radiographs marginally improved physicians’ performance in detecting and localizing referable thoracic abnormalities on chest radiographs.• The detection or localization of referable thoracic abnormalities by pulmonologists and radiology residents improved with the use of AI assistance.

Purpose This study explores the adoption of artificial intelligence (AI) in wineries, with a specific focus on its application to drive innovation and on the organisational and contextual factors that influence its exploration and adoption. Design/methodology/approach The research project is based on an exploratory approach employing a questionnaire developed through a literature review and refined using the Delphi method via a survey with over 500 participants. Findings Wineries employ many AI solutions, from generative AI tools that facilitate creative and agile processes to more embedded, enterprise-level AI systems that require significant investment and IT integration. They adopt a dual approach to exhibit the highest innovation orientation by integrating AI solutions to promote many dimensions of innovation. In contrast, wineries that rely exclusively on generative AI leverage it to innovate marketing processes. However, most wineries surveyed have not implemented AI solutions for process innovation, suggesting that AI development and adoption are in their infancy. Research limitations/implications The study did not examine whether the adoption of AI solutions actually generated innovation, nor was the extent of AI adoption at the micro or meso level assessed. However, the investigations may provide valuable insights and contribute to the development of more targeted strategies for the digital transformation of the wine sector. By exploring organisational and contextual dimensions, it provides a deeper understanding of the variables that may encourage or hinder the implementation of AI technologies. Originality/value This study can be leveraged by wineries as a pioneering analysis of innovation-oriented AI adoption in the wine sector in several countries. The findings offer practical value for the wine industry in promoting digital transformation and harnessing the innovative potential of AI in the wine sector.

The AI-environment paradox: Unraveling the impact of artificial intelligence (AI) adoption on pro-environmental behavior through work overload and self-efficacy in AI learning.

Added value of artificial intelligence solutions for arterial stenosis detection on head and neck CT angiography: A randomized crossover multi-reader multi-case study

This paper investigates drivers of Artificial Intelligence (AI) adoption, specifically in rural small and medium-sized enterprises (SMEs). Research on AI has gained traction in recent times, however, remains an area in need of further investigation, notably the adoption of AI by SMEs, and particularly among rural SMEs. The focus on SMEs is important as they account for the majority of businesses worldwide, playing an important role in job creation and economic development. The research uses secondary data from the Longitudinal Survey for Small Business (LSBS), a large UK panel survey of SMEs, which provides a broad range of variables on a range of SMEs. Probit regression, using a series of environment, firm and network engagement factors as predictive variables, identifies drivers of AI adoption for rural SMEs. Among the numerous drivers of AI adoption in rural SMEs, networking, is identified as a key variable associated with adoption. This research contributes to the limited knowledge on this subject and more broadly to technology adoption in organisations. This leads to policy recommendations in promoting AI adoption among rural SMEs through better communication of the advantages of adoption among SMEs and network development. • A rural location is not a barrier to AI adoption for SMEs, with numerous rural SMEs adopting AI technology in their operations. • There are numerous antecedents to AI adoption for rural SMEs, including networking, prior innovative activity influence, and strategic planning. • Rural SMEs that adopt AI technology are more likely to have employees, tend to be larger, have more sites, and have a greater turnover than non-adopters. • Engagement in networks can facilitate the adoption of AI technology among rural SMEs, as well as enhance understanding of the benefits of AI technology to SMEs' operations. • Policy should seek to encourage increased levels of AI adoption among rural SMEs to support growth, and ensure that appropriate infrastructure exists to support the use of AI technology.

Purpose This study investigates the interplay between artificial intelligence (AI) adoption, ethical capitalistic orientation and the moderating role of human–AI synergy, a new construct developed via grounded theory. This study aims to understand how these variables influence organizational decision-making in the AI era, specifically promoting ethical practices and mitigating negative outcomes such as employee displacement. Design/methodology/approach Using both qualitative and quantitative regression analysis, this study examines the relationships among AI adoption, ethical capitalistic orientation and human–AI synergy. Statistical tools were used to test hypotheses, identify AI adoption’s impact on organizational ethics and assess human–AI synergy’s moderating role. This research also explores how combining AI and human collaboration can foster a more ethical, socially responsible business model. Findings Findings show AI adoption positively impacts ethical capitalistic orientation (β1 = 0.45, p = 0.0001). Human–AI synergy significantly enhances this effect (β2 = 0.30, p = 0.0005) and moderates the relationship between AI adoption and ethical practices (β4 = 0.15, p = 0.0030). The study emphasizes focusing on human–AI collaboration over workforce displacement to maintain ethical practices and achieve efficiency. These results highlight the importance of ethical AI adoption and social responsibility. Originality/value This study introduces human–AI synergy as a novel construct, demonstrating its critical moderating role in the relationship between AI adoption and ethical capitalistic orientation. It offers new insights into how businesses can leverage AI without workforce reduction or unethical practices. This research emphasizes building ethical frameworks for AI adoption, presenting a novel perspective integrating technology with human-centered decision-making.

Artificial intelligence (AI) has brought about change in the manner of traditional recruitment processes. It has improved the efficiency, accuracy, and experiences of candidates during the process significantly. The organizations are also fast adopting such AI-driven solutions in the form of resume-screening algorithms, chatbots, predictive analytics, and video interviews for assessments by streamlining the decision-making. However, concerns around the fairness of AI and reduction of bias in hiring successes are highly controversial. This study investigates the statistical effect of AI adoption on the effectiveness of recruitment using a quantitative research method. AI adoption acts as an independent variable, while recruitment efficiency, reduction of bias, and candidate experience are dependent variables. A total of 250 responses was collected from HR professionals across multinational corporations and startups with the help of structured questionnaires. Multiple regression analysis using SPSS was conducted to determine the relationships between AI adoption and the identified recruitment outcomes. The results show that AI adoption significantly influences efficiency in recruitment (β = 0.58, p < 0.001) and candidate experience (β = 0.61, p < 0.001); in other words, AI-driven tools speed up the process of hiring and enhance candidate engagement. Nevertheless, it was also concluded that AI has a positive, albeit weak, impact regarding bias reduction: β = 0.32, p = 0.041. This indicates that AI alone cannot completely remove the inherent biases of recruitment; hence, there is an ongoing need for human supervision and improvement of the models of AI. These results present both the opportunities and challenges associated with AI-driven recruitment. While AI saves a lot of time in the hiring process and improves the candidate experience, issues of fairness, algorithmic bias, and ethical concerns still prevail. Best practices for organizations include regular auditing of AI-driven tools, high-quality data input, and integration of human decision-making with AI solutions. Ethical considerations of data privacy, transparency, and responsible use of AI will help in gaining trust in the technology by both candidates and HR practitioners. This is an empirical study adding to the growing body of literature on AI in HR, as there has been little to no statistical relationship between AI adoption and key recruitment metrics. Hence, future research should focus on longitudinal studies that will examine the long-term impact of AI on recruitment and assess additional moderating variables such as industry type, company size, and AI training methodologies. The study provides clear evidence that a balanced approach is required, in which AI could complement but not replace human judgment in recruitment decisions.

Artificial intelligence (AI) technology has become one of the most frequently discussed subjects in the development of technology in recent years. Due to its incredible pattern recognition, it can help humans complete work much faster than before with little to no monetary cost. Despite the widespread impact that AI technologies have on various fields, acceptance and adoption of AI lag behind because of a wide range of factors among users. This paper outlines the results of a large literature review that attempts to tease out some of these factors by examining individual differences that may impact the acceptance and adoption of AI. This goal was achieved through an exploration of individual differences that play a role in the acceptance and adoption of new technologies more broadly, as well as AI technologies, to gain a more holistic understanding of the factors contributing to the lack of acceptance and adoption of AI. The main goal of this literature review was to find the individual differences (IDs) associated with the acceptance and adoption of AI technology and general technology. A secondary goal was to create a model based on the acceptance of general technology that could assist in future AI technology research, development, and implementation. This paper identifies several IDs that were found to play a role in the adoption and acceptance of AI technology, as well as 15 specific IDs that were commonly shown to play a role in the adoption and acceptance of general technology. Because of the rapid development of AI technologies in recent years, there is a lack of research examining the acceptance and adoption of AI technologies; however, there is a great deal of research examining the broader acceptance and adoption of technology, and there is significant overlap between the studies that examined general technology acceptance and adoption and those that examined AI-specific technology acceptance and adoption. Because of this, we believe that the research on general technology acceptance and adoption can be used as a foundation and inspiration for future research on AI technology in this area.

This review paper investigates the potential of Artificial Intelligence (AI) solutions to drive innovation within Small and Medium-sized Enterprises (SMEs), addressing adoption barriers and exploring future growth opportunities. The primary objective is to synthesize existing literature on AI applications in SMEs, identifying the benefits, challenges, and strategies for successful implementation. The paper highlights that AI technologies can significantly enhance operational efficiency, product development, customer engagement, and competitive advantage for SMEs. Despite these benefits, several barriers hinder widespread AI adoption, including limited financial resources, lack of technical expertise, resistance to change, and concerns about data security and privacy. By reviewing various case studies and research findings, the paper identifies key strategies to overcome these challenges. These strategies include government incentives, public-private partnerships, affordable AI-as-a-Service models, and targeted training programs to build AI competencies within SMEs. The importance of fostering a supportive ecosystem with robust infrastructure, favorable regulatory frameworks, and access to funding is emphasized. The paper concludes that AI has the potential to revolutionize SMEs by enabling rapid and efficient innovation. However, realizing this potential requires concerted efforts from multiple stakeholders to address adoption barriers and create an enabling environment for AI-driven growth. Future research should focus on developing frameworks for scalable AI implementation tailored to the unique needs of SMEs and tracking the long-term impact of AI adoption. This review provides a comprehensive understanding of the current state of AI in SMEs, offering insights into overcoming challenges and capitalizing on future opportunities for growth and innovation.

Problem definition: Artificial intelligence (AI) assistants—software agents that can perform tasks or services for individuals—are among the most promising AI applications. However, little is known about the adoption of AI assistants by service providers (i.e., physicians) in a real-world healthcare setting. In this paper, we investigate the impact of the AI smartness (i.e., whether the AI assistant is powered by machine learning intelligence) and the impact of AI transparency (i.e., whether physicians are informed of the AI assistant). Methodology/results: We collaborate with a leading healthcare platform to run a field experiment in which we compare physicians’ adoption behavior, that is, adoption rate and adoption timing, of smart and automated AI assistants under transparent and non-transparent conditions. We find that the smartness can increase the adoption rate and shorten the adoption timing, whereas the transparency can only shorten the adoption timing. Moreover, the impact of AI transparency on the adoption rate is contingent on the smartness level of the AI assistant: the transparency increases the adoption rate only when the AI assistant is not equipped with smart algorithms and fails to do so when the AI assistant is smart. Managerial implications: Our study can guide platforms in designing their AI strategies. Platforms should improve the smartness of AI assistants. If such an improvement is too costly, the platform should transparentize the AI assistant, especially when it is not smart. Funding: This research was supported by a Behavioral Research Assistance Grant from the C. T. Bauer College of Business, University of Houston. H. Zhao acknowledges support from Hong Kong General Research Fund [9043593]. Y. (R.) Tan acknowledges generous support from CEIBS Research [Grant AG24QCS]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0093 .

The growth of artificial intelligence (AI) brings advantageous prospects along with difficulties to Nigerian startups while affecting their innovation processes decision-making approaches and market expansion. The study investigates the potential of AI-driven entrepreneurship in Nigeria by evaluating its benefits and identifying barriers to adoption. This research investigates how Nigerian startups leverage AI opportunities while evaluating their readiness for AI adoption and determining AI's effects on entrepreneurial success. The Technology Adoption Model (TAM) serves as the study’s framework because it describes the relationship between AI adoption and the factors of perceived usefulness and perceived ease of use. The study utilized a descriptive survey research design to gather data from 250 startups operating in fintech, health tech, agritech, e-commerce, and logistics sectors through a structured questionnaire. The study used both descriptive and inferential statistical methods and regression analysis to measure AI's influence on business performance. Research demonstrates that AI improves decision-making processes and operational cost efficiency while promoting innovation alongside a strong positive relationship (r = 0.829, p < 0.001) between AI implementation and entrepreneurial success. Poor digital infrastructure together with high implementation costs and insufficient AI expertise as well as regulatory constraints prevent widespread AI adoption. Investments in digital infrastructure along with funding mechanisms and AI development programs must be strategically pursued because AI offers substantial transformative possibilities. The study suggests the implementation of policy interventions and financial assistance alongside skill development programs to improve Nigerian startup AI adoption capabilities.

Artificial intelligence (AI) assistance in magnetic resonance imaging (MRI) assessment for prostate cancer shows promise for improving diagnostic accuracy but lacks large-scale observational evidence. To evaluate whether use of AI-assisted assessment for diagnosing clinically significant prostate cancer (csPCa) on MRI is superior to unassisted readings. This diagnostic study was conducted between March and July 2024 to compare unassisted and AI-assisted diagnostic performance using the AI system developed within the international Prostate Imaging-Cancer AI (PI-CAI) Consortium. The study involved 61 readers (34 experts and 27 nonexperts) from 53 centers across 17 countries. Readers assessed prostate magnetic resonance images both with and without AI assistance, providing Prostate Imaging Reporting and Data System (PI-RADS) annotations from 3 to 5 (higher PI-RADS indicated a higher likelihood of csPCa) and patient-level suspicion scores ranging from 0 to 100 (higher scores indicated a greater likelihood of harboring csPCa). Biparametric prostate MRI examinations were included for 780 men from the PI-CAI study who were included in the newly-conducted observer study. All men within the PI-CAI study had suspicion of harboring prostate cancer, sufficient diagnostic image quality, and no prior clinically significant cancer findings. Disease presence was defined by histopathology, and absence was determined by 3 or more years of follow-up. The AI system was recalibrated using 420 Dutch examinations to generate lesion-detection maps, with AI scores ranging from 1 to 10, in which 10 indicates the highest likelihood of csPCa. The remaining 360 examinations, originating from 3 Dutch centers and 1 Norwegian center, were included in the observer study. The primary outcome was diagnosis of csPCa, evaluated using the area under the receiver operating characteristic curve and sensitivity and specificity at a PI-RADS threshold of 3 or more. The secondary outcomes included analysis at alternate operating points and reader expertise. Among the 360 examinations of 360 men (median age, 65 years [IQR, 62-70 years]) who were included for testing, 122 (34%) harbored csPCa. AI assistance was associated with significantly improved performance, achieving a 3.3% increase in the area under the receiver operating characteristic curve (95% CI, 1.8%-4.9%; P < .001), from 0.882 (95% CI, 0.854-0.910) in unassisted assessments to 0.916 (95% CI, 0.893-0.938) with AI assistance. Sensitivity improved by 2.5% (95% CI, 1.1%-3.9%; P < .001), from 94.3% (95% CI, 91.9%-96.7%) to 96.8% (95% CI, 95.2%-98.5%), and specificity increased by 3.4% (95% CI, 0.8%-6.0%; P = .01), from 46.7% (95% CI, 39.4%-54.0%) to 50.1% (95% CI, 42.5%-57.7%), at a PI-RADS score of 3 or more. Secondary analyses demonstrated similar performance improvements across alternate operating points and a greater benefit of AI assistance for nonexpert readers. The findings of this diagnostic study of patients suspected of harboring prostate cancer suggest that AI assistance was associated with improved radiologic diagnosis of clinically significant disease. Further research is required to investigate the generalization of outcomes and effects on workflow improvement within prospective settings.

This study evaluates the impact of artificial intelligence (AI) assistance on the diagnostic performance of radiologists with varying levels of experience in interpreting mammograms in a Malaysian tertiary referral center, particularly in women with dense breasts. A retrospective study including 434 digital mammograms interpreted by two general radiologists (12 and 6 years of experience) and two trainees (2 years of experience). Diagnostic performance was assessed with and without AI assistance (Lunit INSIGHT MMG), using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). Inter-reader agreement was measured using kappa statistics. AI assistance significantly improved the diagnostic performance of all reader groups across all metrics (p < 0.05). The senior radiologist consistently achieved the highest sensitivity (86.5% without AI, 88.0% with AI) and specificity (60.5% without AI, 59.2% with AI). The junior radiologist demonstrated the highest PPV (56.9% without AI, 74.6% with AI) and NPV (90.3% without AI, 92.2% with AI). The trainees showed the lowest performance, but AI significantly enhanced their accuracy. AI assistance was particularly beneficial in interpreting mammograms of women with dense breasts. AI assistance significantly enhances the diagnostic accuracy and consistency of radiologists in mammogram interpretation, with notable benefits for less experienced readers. These findings support the integration of AI into clinical practice, particularly in resource-limited settings where access to specialized breast radiologists is constrained.

The COVID-19 pandemic has expedited the adoption of artificial intelligence (AI) in the healthcare industry. The need for rapid diagnosis and treatment, as well as the demand for remote care and monitoring, has led to an increased focus on AI solutions that can improve healthcare delivery and patient outcomes. AI-powered technologies such as predictive analytics, natural language processing, and computer vision have been deployed to support screening and diagnosis, drug discovery, and vaccine development. Additionally, AI-powered chatbots and virtual assistants have been used to triage patients and provide remote care. While the adoption of AI in healthcare has brought tremendous benefits, there are still challenges to be addressed. This paper will explore the adoption, benefits, and challenges of AI in the healthcare industry, shedding light on the prowess of AI in revolutionizing healthcare while also underscoring the need for careful implementation and ethical considerations. This study will conclude with 5 case studies of top U.S. hospitals that have adopted AI for diverse purposes.

Can AI assistants improve time efficiency in digital dataset preparation in virtual implant planning? A comparative study.