By now, artificial intelligence-enabled tools should’ve begun helping us develop our understanding of cause-and-effect relationships between ingredients and technical or functional performance. But it’s going more slowly, and with spottier results, than many had hoped.
Why is it taking so long to realize? And what can companies be doing today to articulate and scale their AI strategy?
This white paper will detail:
- The importance and challenges of science-based product development
- Why actionable artificial intelligence remains elusive in the applied sciences
- Why lab data capture infrastructure is critical to achieving positive outcomes through AI — and how so many get it wrong
- The path to become AI-enabled for applied science companies
