AI is finally being used in real-time at the bench by chemists and materials scientists, dramatically speeding product development and the overall pace of innovation. But ask 10 people what AI is, and you will get 10 different answers, ranging from pure magic to it doesn’t work at all. In fact, when polled, the vast majority of companies have yet to start rolling AI out. In this talk, we examine the root causes of this hesitation and also put forward a framework to approach the successful adoption of this new and exciting technology, which covers the objectives of both business and R&D leaders.

Business leaders want to:

1. Understand the steps that can be taken to successfully deploy AI in real-time at the bench
2. Invest incrementally and build on initial successes as they gain momentum
3. Achieve measurable ROI every step of the way

R&D leaders want to:

1. Explore problem spaces more broadly, but with fewer experiments
2. Use AI and DOE synergistically to generate recommendations with high predictive accuracy of technical and functional performance targets
3. Achieve concrete success metrics that measure the accuracy of AI and its operating impact

Learn how both constituencies can be satisfied and your company can get value from AI.