Towards An Artificial Intelligence Maturity Model: From Science Fiction To Business Facts

Artificial intelligence (AI) has become increasingly prevalent in organisations in different sectors. The rapid development of AI technology has rendered it essential to understand strategies for its implementation. Despite current trends, the uncertainties in the process required to establish strong AI capabilities are the major concerns for high level management. Therefore, this research-in-progress aims to understand AI practices in organisations through the development of an organisation-level AI maturity model (AIMM). However, to the best of our knowledge, no fully developed and theoretically derived AI maturity model currently exists. This research, therefore, represents an early attempt to develop an AI maturity model at the level of organisations; the results of this research will provide organisations with insights into the successful evolution and adoption of AI and can be used as a theoretical foundation for future research

Citizens’ Adoption of Digital Technologies during COVID-19

The spread of Coronavirus disease 2019 (COVID-19) has affected both governments and businesses worldwide. Besides lives loss and disease, COVID-19 causes reduction in economic growth and increase in the unemployment rate, companies’ bankruptcy, and the workload of healthcare. Governments and businesses have relied heavily on the digital technologies to eliminate or at least reduce the spread and effect of COVID-19. Therefore, individuals (as citizens requiring government services, employees working in either public or private sectors, or costumers having goods or services) were only able to accomplish their tasks through digital technologies. COVID-19 effect has implications on many research fields, including information systems. Prior studies that investigated the adoption of digital technologies have focused on technological, personal, and/or institutional factors. This work-in-progress paper attempts to explore the digital technologies adoption through the lens of COVID-19. This research uses the grounded theory. This work-in-progress paper presents research methodology and expected contributions

Artificial Intelligence Beyond the Hype: Exploring the Organisation Adoption Factors

The adoption of artificial intelligence (AI) has increasingly taken hold across global businesses. Although research exists in this domain, very little is known about the adoption factors and necessary AI specifications to ensure successful organisational adoption of this technological innovation. The present study fills this gap in the literature through the analysis of the adoption process of AI. The conceptual framework of this research is based on the technology–organisation–environment (TOE) framework and the diffusion of innovation theory (DOI) for assessing the adoption process of AI from an organisational perspective. The conceptual framework was tested and validated through the use of semistructured interviews conducted in Australia with18 expert interviewees regarding its applicability to the AI adoption process. The findings indicate that relative advantage, compatibility, top management support, management obstacles, organisational readiness, and government regulatory support are important determinants of AI adoption. In terms of academic contribution, this research provides an improved understanding of the critical factors relating to the adoption of AI from the perspective of organisations. The empirical results further support the applicability of using the DOI and the TOE framework at the organisational level, to further understand AI adoption. With regards to practical implications, this research provides Australian organisations with relevant suggestions with regard to how the adoption of AI can be improved

Winning AI Strategy: Six-Steps to Create Value from Artificial Intelligence

As part of industry 4.0, many organizations feel the need to explore the possibilities artificial intelligence may provide for their businesses. Still, not every organization that implements or makes use of AI benefits in this way let alone extracts value from them. This paper aims to investigate the challenges associated with AI business value creation. Through a comprehensive analysis of the top AI actual use cases across 7 industries and a systematization of the existing literature, we formulated the challenges in deploying and reaping business value from AI at the organization-level. Six challenges of AI at organization-level have emerged from our analysis. They are AI business case, Relative benefits of AI, Top management support, Effective use of data, AI talent, and AI compatibility. Our work aims at contributing to the body of knowledge on AI technology at organization-level, and at guiding practitioners in identifying and overcome various challenges of AI

Predictive Models for Pulmonary Artery Size in Fontan Patients

We developed models of pulmonary artery (PA) size in Fontan patients as a function of age and body surface area (BSA) using linear regression and breakpoint analyses based on data from 43 Fontan patients divided into two groups: the extracardiac conduit (ECC) group (n = 24) and the non-ECC group (n = 19). Model predictions were compared against those of a non-Fontan control group (n = 18) and published literature. We observed strong positive correlations of the mean PA diameter with BSA (r = 0.9, p \u3c 0.05) and age (r = 0.88, p \u3c 0.05) in the ECC group. The absolute percentage differences between our BSA and age model predictions against published literature were less than 16% and 20%, respectively. Predicted PA size for Fontan patients was consistently smaller than the control group. These models may serve as useful references for clinicians and be utilized to construct 3D anatomic models that correspond to patient body size or age

Tree management and the greening of the environment in Hong Kong : a study of collaborative governance

published_or_final_versionPolitics and Public AdministrationMasterMaster of Public Administratio