While working with founders and research driven teams at spectup, one unexpected challenge I encountered around AI driven drug discovery was not model performance, but trust between scientists and the technology. I remember sitting in a room with a research team where the AI surfaced promising compound candidates, yet the scientists hesitated because the reasoning felt opaque. The resistance was subtle but real, and it slowed progress more than any technical bottleneck. What I observed was that the issue was less about accuracy and more about explainability. In one case, the AI was correct, but the team struggled to justify decisions internally and to external partners. That created friction in funding discussions as well. Investors asked how decisions were made, and vague answers undermined confidence. At spectup, we helped restructure how outputs were presented, translating AI recommendations into human readable logic tied to known biological mechanisms. Overcoming this meant embedding collaboration into the workflow. Instead of positioning AI as a decision maker, it became a decision support tool. Scientists reviewed, challenged, and refined outputs, which increased adoption and trust. I noticed morale improve once the team felt ownership rather than replacement anxiety. My advice to others starting this journey is to invest early in alignment, not just infrastructure. Build processes that explain why the AI suggests something, not only what it suggests. From an investor readiness perspective, clarity matters as much as innovation. When teams treat AI as a partner and not a black box, progress becomes faster, funding conversations improve, and long term impact becomes realistic.