My current research topics focus on AI applications in business operations, particularly virtual assistants, large language models, and decision support systems. Together with my students, we concentrate on integrating AI into business processes by combining generative AI (large language models and multimodal models) with rule-based AI, using business process management (BPM) models.
My earlier works in the 2010s addressed technological advancements more broadly—how they affect companies, employees, and the public, and what risks are associated with them. These works are related to information technology, risk management, and human factors. Specifically, it involved the development of new machine-based methods for assessing employees’ physical and cognitive condition/capabilities, including productivity. This also encompassed occupational safety, physical risk factors, sensor networks, and smart workplaces.
Earlier still, I began my research in the field of enterprise information management systems, where I also completed my first master’s thesis in 2004. This was followed by development, implementation, and operation of business-oriented information systems in the business sector.
Research keywords
- Digitalization: digital transformation, artificial intelligence, machine learning, large language models, multimodal models, virtual assistants, rule-based approaches, decision support systems, web applications, predictive analytics, business analytics.
- Cyber-Physical Systems: Industry 4.0, Internet of Things (IoT), smart workplaces, sensor networks, fast data, big data collection, signal processing, edge computing, mobile devices, mobile connectivity, wireless infrastructure.
- Management: management information systems, business process management, operational improvement, data-driven decision-making, work organization, human factors, productivity, risk management (identifying, assessing, and responding to risks), safety, work environment, ethics, and sustainable management (ESG).
Methodology Taxonomy:
Business Process Management (BPM)
My research methodology is primarily based on business process management theories. These are systematic approaches for improving and optimizing an organization’s business processes. BPM aims to enhance a company’s efficiency, productivity, and flexibility while ensuring quality and compliance with regulations.
The following activities are covered in the research process:
- Process Modeling: Mapping the organization’s workflows and processes to understand their operations and relationships. Modeling helps visualize processes, identify bottlenecks, and discover opportunities for improvement.
- Process Analysis: Evaluating existing processes in terms of their effectiveness, costs, and quality. Analysis helps pinpoint problem areas and define areas for improvement.
- Process Design: Creating new or improved processes that align with the organization’s goals and needs. This includes simplifying, automating, and implementing innovative solutions.
- Process Implementation: Putting the designed processes into practice within the working environment. This often involves the introduction of new technologies, staff training, and managing organizational changes.
- Process Monitoring and Measurement: Measuring the performance of digitalized/automated processes. This allows real-time data collection and analysis to assess the effectiveness of improved processes and draw conclusions for further enhancements.
+-------------+ +-------------+ +-------------+
| | | | | |
| Modelling +----->| Analysis +----->| Design |
| | | | | |
+-------------+ +-------------+ +-------------+
^ |
| v
+----------------+ +-------------------+
| | | |
| Measurement +<-----+ Implementation |
| | | |
+----------------+ +-------------------+
Figure 1. The main stages of business process management. They apply to each sub-process that is automated with an AI solution.If these topics also concern you, please contact us on the CONTACT page if you wish to collaborate!