AI is transforming how materials and chemical products are developed, but success depends as much on changing how scientists think about experimentation as it does on adopting new technology.
AI is not replacing trial-and-error. It's making every trial more informative, helping scientists learn faster and identify the next best experiment with greater confidence. Many organizations delay AI initiatives because they believe they need years of perfectly curated experimental data or highly accurate models before they can get started.
In this webinar, we'll explore three mindset shifts that enable formulation and product development teams to realize value from AI sooner: why you don't need perfect data to begin, why useful models don't have to be perfect, and why failed experiments are often the fastest path to better formulations.
You'll leave with a practical understanding of how to:
- Get started with AI using the data you already have, rather than waiting for "perfect" datasets.
- Use AI to guide iterative experimentation, reducing uncertainty and accelerating formulation development.
- Build an experimental strategy that captures both successful and unsuccessful results to improve future decisions.
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