AI-driven Business Themes
The AI-driven Business Area of Excellence is organised around the six sub-areas. They cover the building of shared theoretical and methodological expertise, data-driven innovation and systems while taking into account the firm-stakeholder AI perspectives:
AI Business Strategy
AI Predictive Analytics
AI Consumer Behaviour
AI and Digital Innovation
AI and New Age Operations Management
Dr Fan Xia is the Principal Investigator of AI Business Strategy. This sub-area studies how AI impacts businesses and their strategies and policies. AI is considered as a special resource offering magnified data processing capabilities that advance the acquisition, understanding, and processing of knowledge, as well as resource allocations in companies. This therefore helps shape the decision making and consequent performance of strategic actions. The rise of AI may fundamentally change how firms obtain and sustain their competitive advantages, not just for high-technology industries, but also for more traditional businesses. Researchers of this sub-area develop new theories supported by empirical evidence to explore this novel opportunity.
Dr Amir Sadoghi is the Principal Investigator of AI Predictive Analytics. This sub-area operates across a number of fundamental fields of AI methodologies. In Natural language processing (NLP) the focus is on how to extract information from large corpus of texts (such as media and social media) to explain the economy. Methodologies include: content analysis, concept extraction, document classification: Latent Dirichlet allocation (LDA), Multi-Label Classification. Research is also carried out using Causal Inference from Machine Learning approaches. The emphasis here is on methodologies such as Causal Bayesian Networks and Causal Random Forest to draw reliable conclusions from machine learning testing approaches. There is also focus on Deep Learning through the application of deep learning methods to analyze, predict and examine risk in real systems.
Dr Dirk Schneckenberg is the Principal Investigator of AI and Digital Innovation. This sub-area investigates the role of artificial intelligence in the digital transformation of business and society. Broadly, this research focus examines how artificial intelligence reshapes innovation processes and the organisation of corporate R&D management. Researchers also examine how AI enables venture creation, innovation, and organisational change, and how AI can influence and redefine strategic decision processes related to innovation.
Dr Rajibul Hasan is the Principal Investigator of AI Consumer Behaviour. Everyone witnessed the emergence of a Digital Marketing era, where continual technological advances such as Big Data Analytics, Augmented Reality, Virtual Reality, Internet of Things, Smart Cities, Artificial Intelligence, and Robotics are integrated into personal and public spaces. AI Consumer Behaviour is about understanding how consumers are using these technologies, and how these technologies can be leveraged to engage with consumers more effectively. Reseachers of this sub-area study consumer behaviour related to these technologies and innovations to provide expert guidance on moving from traditional to digital AI-driven business.
Dr Oncü Hazir is the Principal Investigator of AI and New Age Operations Management. This involves investigating how AI and big data analytics can offer new opportunities to deal with challenges in operations and project management. A key challenge in the new era of AI, which researchers of this sub-area focus on, is the need to make rapid decisions which cope with the uncertainty and inherent complexity of a dynamic business environment. Computational intelligence approaches of AI and big data analytics tools help address this challenge by supporting decision making under uncertainty.
Dr Petya Puncheva is the Principal Investigator of Responsible AI Business. This sub-area recognises that the development and exploitation of AI technologies in business raises anxiety and excitement surrounding its future implications for businesses, individual workers and societal fairness. For businesses, the developments in AI will clearly offer gains in productivity and reduction of human error by replacing certain non-routine and cognitive tasks currently requiring human intelligence. However, this development poses multiple social and environmental concerns relevant to business managers and policy makers. Researchers working for this sub-area investigate questions pertinent to these issues and business solutions to such problems.
Responsible AI Business