Recent work on acceptability of AI-enabled professional tools has focused on the consideration of Human Factors such as user confidence as well as technical factors like model explainability as determinants of user-adoption and usage. These links are mainly established in simulated contexts or environments with low deployment constraints. However, the acceptability of AI tools in professional settings seems to be often restricted to the acceptance of performance and short-term return on investment (ROI) without questioning the technical appropriateness of AI capabilities to user's needs (and fears) or tasks at hand. Three deployment sites of AI-assisted decision-making tools were observed where AI acceptability was limited to the artefact's measurable time-based efficiency. In this article, we question the other UX design considerations in these ML-enhanced dashboards with rushed production cycles that produced AI gadgets rather than user-friendly tools. These AI gadgets ultimately resulted in negative user experiences, inconclusive ROI and/or sidelining of projects. We then suggest ways of limiting risks of end-user-rejection through a more holistic approach of UX design considerations. These include ascertaining needs of the stakeholder network, avoiding technical solutionism, optimizing user interfaces to reduce unnecessary complexity, and dimensioning technical solutions appropriately to the task.