Digital transformation is about fundamentally changing and re-organising existing business models, capabilities and processes using digital technologies to reduce production costs, improve information exchange and facilitate collaboration between different entities in the digitally enabled economy, government and society. In recent years, Artificial Intelligence (AI), including generative AI (GAI), technologies have been touted to play a more significant role in digital transformation initiatives, however several research challenges remain unaddressed and warrant further research and development.

The first one is that different researchers are looking at AI applications from different domains, disciplines and perspectives. For example, researchers from the information systems field are examining topics such as the challenges associated with AI adoption and the strategies that organizations can employ to successfully implement AI solutions. In parallel, researchers from the computer science field are focusing on optimizing machine learning techniques to address specific problems and tackle issues related to data access and management.

We need integrated AI research and solutions that bring value to organizations and fit within their established development and governance processes. Such solutions must also be able to leverage existing IT and data assets. Traditional methodologies used in software planning, design, and development need to be adapted to become a collaborative effort between multidisciplinary domain experts, business managers, data scientists, information scientists and software developers.

This Research Topic calls for original research contributions that investigate the provision of effective AI solutions for real-world problems in digital transformation. Topics include (but are not limited to):

• AI application and data architecture

• Business Process Automation, Augmentation, and Improvement for Business Transformation

• AI assurance and audit

• Data governance and quality for AI systems

• Requirements engineering, development and modelling approaches for AI systems

• Collaboration methods that involve domain, analytics and technology experts (e.g. AIOps, MLOps)

• Business Process Management involving AI systems

• Cloud-based infrastructures and AI as a service (AIaaS)

• Knowledge modelling for digital transformation

• AI explainability, trust and responsibility related to digital transformation

• Case studies in Fintech, supply chains, e-government, health and environmental sustainability

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Frontiers in Artificial Intelligence

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