Human-Robot Collaboration has gained significant attention, particularly within manufacturing scenarios. Collaborative robots (cobots), though designed to work closely with humans, often struggle due to their limited ability to predict and adapt to human intentions, which hampers effective and fluid interactions. To address this challenge, the presented study investigates the impact of equipping cobots with human intention prediction capabilities. 18 participants volunteered in the experiments, in which humans and cobot collaboratively solved the TOH game using three different cobot strategies: Optimal, Random, and Intention Prediction. The results indicate that while the Optimal logic ensures the highest efficiency, the Intention Prediction logic significantly enhances the human experience by anticipating moves and fostering smoother collaboration, despite a slight reduction in performance. These findings underscore the potential of intention prediction in improving human-cobot interactions and highlight the need for further research on adaptive and intuitive cobot systems.