Human‐Centred AI Teammate Design: A Participatory Method for Eliciting Business Needs in Complex Problem‐Solving
TL;DR
Results from the proof‐of‐concept and proof‐of‐value evaluations provide initial evidence that the IATReBS method can help participants identify AI teammate requirements in complex problem‐solving contexts, particularly regarding feasibility, usability and perceived usefulness.
Abstract
Introducing artificial intelligence (AI) teammates into organizations and enabling human–AI collaboration can improve responses to complex problems. However, current methods for identifying AI teammate requirements in complex problem‐solving situations often overlook the experience and knowledge of business staff and lack a human‐centred perspective. Therefore, this study proposes a method for identifying AI teammate requirements for business staff (IATReBS), with a particular emphasis on leveraging their experience and knowledge. We use the design science research methodology to combine theoretical insights from participatory design with practical insights from user interviews (15 participants) to develop IATReBS. The method guides business staff in identifying the requirements of AI teammates for complex problems in specific business contexts. Results from the proof‐of‐concept (15 interviews) and proof‐of‐value (a two‐month experiment yielding 24 questionnaires) evaluations provide initial evidence that the IATReBS method can help participants identify AI teammate requirements in complex problem‐solving contexts, particularly regarding feasibility, usability and perceived usefulness. Our research provides new insights and a method for helping organizations introduce AI teammates from a human‐centred perspective, thereby contributing to the externalization of tacit knowledge. Our research offers methodological guidance for organizations to acquire AI teammates that are aligned with clear application scenarios and meet user expectations.