FormuSense is a B2B SaaS company that sells AI tools to industrial formulation teams in coatings, personal care, and specialty chemicals. The product helps chemists predict formulation outcomes such as stability, viscosity, and performance before running expensive lab experiments. The company has 120 enterprise customers and is expanding from pilot programs into multi-year platform contracts.
Adoption is stalling after initial sales because formulation chemists do not trust the model's recommendations. Internal data shows that while 78% of customer accounts upload historical lab data, only 24% of chemists use model predictions weekly. In user interviews, chemists repeatedly say: "The scores may be accurate, but I don't understand why the model is suggesting this formula." Sales wants a more technical explanation layer, while design wants a simpler confidence and rationale experience.
You are the product manager for the prediction workflow. Your challenge is not to improve model accuracy directly, but to define how the product should explain the model's core mechanism to a formulation chemist in a way that increases trust, usability, and decision quality.