The Institute of Food Technologists (IFT), a nonprofit scientific organization dedicated to advancing the science of food, is hosting a free webinar that will explore how artificial intelligence is transforming product development workflows.
Leveraging AI for Faster Product Development Cycles, scheduled for August 26, 2025, from 11:00 a.m. – 12:00 p.m. CDT, will highlight how AI accelerates R&D and formulation, shortens speed-to-result timelines, and enhances production efficiency. While the focus is on food systems, the strategies and technologies discussed have strong parallels to drug development, formulation optimization, and personalized product design in pharma.
The webinar will examine how AI can:
- Accelerate early-stage innovation by mining scientific literature and historical datasets to guide formulation and discovery.
- Overcome technical challenges through predictive modeling, helping scientists identify and solve stability, compatibility, and scalability issues before costly lab trials.
- Enable customization at scale using consumer data to design tailored formulations.
The session is sponsored by CoDeveloper, IFT’s proprietary AI-powered R&D platform, which connects users to 85+ years of peer-reviewed scientific content to streamline formulation development and address technical roadblocks.
Panelists include:
- Mohamed Badaoui Najjar, PhD, R&D Senior Director of Digital Transformation & Global Specifications at PepsiCo, who integrates AI solutions across R&D, supply chain, and operations.
- Michael Slater, Technical Director of Consulting at Improving, bringing expertise in advanced software development, predictive analytics, and digital infrastructure.
- Jay Gilbert, PhD, Director of Scientific Programs & Product Development at IFT, who leads new product innovation and serves as the product lead for CoDeveloper.
While the focus of this event is food science, the webinar’s insights into AI-driven R&D efficiency, digital transformation, and formulation optimization could offer cross-industry learnings that can be applied to life-sciences.
