This research explores the opportunities and limitations of image-based AI tools for industrial design. In collaboration with designers at Royal Gazelle, several tools were tested. AI tools can broaden and speed up the creative processes but also lack control for the user. AI models and designers use different terms to articulate perceptions and control output. This misalignment stems from misalignment among humans which translated to the training datasets of AI. This report proposes co-creative image labelling sessions to align perception and articulation of perception in design teams. The aligned perception creates a strong vibe. Collectively labelling an image dataset increased alignment of the perception of images by 30.8% when comparing post- to pre-session questionnaire results. Additionally, participants used more of the same vocabulary after the session. Training a low-rank adaptation model on these labelled datasets could externalise tacit design knowledge and can be used for further development on AI models that align with designers.