Research & Insights

Generative AI can help executives make better decisions by helping them think of additional alternatives and decision criteria. We are integrating generative AI into IMD’s own decision-making app—called Dragon Master—to help executives sharpen their decision process by freeing up some of the cognitive demands of decision making so that they can focus on what humans can do best: frame the decision, ask better questions, critically assess alternatives and make sense of the implications of choosing one alternative over another.

If you are interested in this topic or want to explore opportunities for your company to take part in our research project, please get in touch with Arnaud Chevallier.

Recent advances in Generative AI are already showing how organizations can improve their internal operations and product offerings with it. Yet, many organizations still hesitate to adopt Generative AI applications, arguing that it poses privacy and security threats. In this study, we develop a typology of the different risks associated with the use of Generative AI. We also outline a series of solutions that can aid organizations in mitigating these risks.

If you are interested in this topic or want to explore opportunities for your company to take part in our research project, please get in touch with Öykü Işık or Amit Joshi.

Generative AI has taken the world by storm, and we are only starting to understand its implications for employee performance. We contribute to this line of work by examining the impact of using Generative AI on individual productivity and job quality. We also identify additional factors that influence this relationship and provide a series of recommendations to maximize the benefits that come with using Generative AI applications.

If you are interested in this topic or want to explore opportunities for your company to take part in our research project, please get in touch with Lazaros Goutas.

AI is undoubtedly transforming modern organizations. However, the results concerning the success of introducing AI in the workplace remain inconclusive. Moreover, uncertainty exists with regards to how AI should be introduced in strategic decision-making processes, i.e., whether AI should augment decision-makers, or if part of decision-making should be fully delegated to AI algorithms. We addresses these problems initially by examining whether the introduction of AI improves strategic decision quality. Second, we join the automated/augmented AI debate by arguing that in order to maximize benefits, the different types of AI should be adapted to the diverse cognitive styles of decision-makers. Last, we provide insights and recommendations into to the optimal configurations for introducing AI in the workforce.

If you are interested in this topic or want to explore opportunities for your company to take part in our research project, please get in touch with Lazaros Goutas.

As AI systems are becoming more widespread, the business interest seems to be continuously growing. Yet, the issues stemming from biased data sets, lack of governance and bad model design are still rampant. In an effort to provide control, many governments are working on regulating AI, and Europe is leading the pact with the AI Act. In response to all this, many organizations have started initiatives to establish AI governance and ethical AI guidelines. This research looks into the practices put in place by the front runners, and draws conclusions on what still needs to be done.

When it comes to creating value with AI, healthcare applications are leading in terms of their proven effectiveness and efficiency. Despite overall optimism and enthusiasm shared among both the healthcare providers as well as receivers, the underrepresentation of certain groups is especially problematic when it comes to training machine learning models as this is likely to amplify biases present in unrepresentative healthcare data as well, further worsening the healthcare inequalities. This project investigates this issue, specifically within the context of Africa, and looks into how real world data collection can be fostered to better understand epidemiology and the patient journey and to improve AI-powered healthcare in Africa.