AI in Opportunity Identification : Perceived Organizational Challenges

Sammanfattning

Artificial intelligence (AI) is increasingly recognized as an enabler of innovation, particularly in opportunity identification, where organizations search for, interpret, and evaluate early opportunities under uncertain conditions. However, although AI offers potential benefits for early innovation activities, organizations still face practical and organizational challenges when beginning to use it. This thesis explores the perceived organizational challenges that emerge when a healthcare and medical technology company starts using AI in opportunity identification. The study follows a qualitative, abductive, single-case study design. Data were collected through semi-structured interviews with organizational actors involved in opportunity identification and related front end innovation activities. The data were analyzed thematically to identify recurring patterns in how employees perceive the use of AI in early innovation work.The findings identify six central challenges: individual averseness, conditional trust, boundless human-AI collaboration, poor AI literacy, role adaptation, and regulation uncertainty. These challenges are divided into three dimensions: the individual, collaboration, and organizational dimensions. The study contributes to the literature by showing how organizational challenges are perceived in the early use of AI in opportunity identification, thereby addressing the limited understanding of why AI use remains low despite its widely recognized potential benefits. It also challenges overly positive views of AI by highlighting that AI can support early innovation activities while simultaneously creating uncertainty around trust, collaboration, responsibility, and regulation. Finally, the thesis presents a conceptual framework showing where each challenge is most present across opportunity identification activities.