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This paper aims to managerial decision styles, following the General Decision-Making Style Inventory, as potential predictors of individual bias awareness and bias blind spots, with a focus on the rational decision style.

The research is based on a survey of 500 C-1 level managers within Forbes 2000 companies. It explores their decision styles and their assessments of their own and others’ decision behavior.

The results show that the awareness of one’s own susceptibility to biases and bias blind spots is highly dependent on an individual’s (self-declared) decision style and type of cognitive bias; decision-makers with a strong tendency toward a rational or spontaneous decision style see themselves as less vulnerable to cognitive biases but also show a much stronger bias blind spot than those with a tendency toward other decision styles. Meanwhile, decision-makers with a strong tendency toward an intuitive decision style tend to recognize their own vulnerability to cognitive biases and even show a negative blind spot, thus seeing themselves as more affected by cognitive biases than others.

To date, decision styles have not been used as a lens through which to view susceptibility to cognitive biases and bias blind spots in managerial decision-making. As demonstrated in this article, decision styles can serve as predictors of individual awareness and susceptibility to cognitive biases and bias blind spots for managers.

The current gap between the required and available artificial intelligence (AI) professionals poses significant challenges for organisations and academia. Organisations are challenged to identify and secure the appropriate AI competencies. Simultaneously, academia is challenged to design, offer and quickly scale academic programmes in line with industry needs and train new generations of AI professionals. Therefore, identifying and structuring AI competencies is necessary to effectively overcome the AI competence shortage.

A probabilistic topic model was applied to explore the AI competence categories empirically. The authors analysed 1159 AI-related online job ads published on LinkedIn.

The authors identified five predominant competence categories: (1) Data Science, (2) AI Software Development, (3) AI Product Development and Management, (4) AI Client Servicing, and (5) AI Research. These five competence categories were summarised under the developed AI competence framework.

The AI competence framework contributes to clarifying and structuring the diverse AI landscape. These findings have the potential to aid various stakeholders involved in the process of training, recruiting and selecting AI professionals. They may guide organisations in constructing a complementary portfolio of AI competencies by helping users match the right competence requirements with an organisation's needs and business objectives. Similarly, they can support academia in designing academic programmes aligned with industry needs. Furthermore, while focusing on AI, this study contributes to the research stream of information technology (IT) competencies.