CHICAGO — The Institute of Food Technologists’ (IFT) annual event, IFT FIRST, held July 14-17 in Chicago, kicked off with thought-provoking sessions and demos surrounding innovations in the food science industry.
As part of the education sessions, Vanessa Rios de Souza, director of client solutions at Aigora, discussed the role of AI in the rapidly evolving landscape of Fast-Moving Consumer Goods (FMCG).
She opened her presentation, From Strategy to Success: Navigating Product Development Challenges with Machine Learning, by touching on the downsides of the traditional product development cycle.
“That cycle works perfectly if it’s all done properly, but the main drawback is time,” Rios de Souza said. “It’s time-consuming and resource intensive, which is going to impact a lot because it’s a very costly process.”
Exploring the integration of machine learning into product development, Rios de Souza provided a comprehensive understanding of the process, including its benefits and challenges.
“It’s not a secret that the key challenges for sensory and consumer scientists nowadays include understanding the market, including consumer needs, product space and future trends, while also supporting product development and making that not only faster and more sustainable but also more successful,” Rios de Souza said.
The presentation also considered how AI can be utilized in different stages of product development to stay competitive in an increasingly digital-driven market.
Aigora findings say that AI not only expedites product development, increasing the chances of success, but also enhances decision-making, market responsiveness and innovation in the FMCG sector. AI learning models can accelerate the product development process by utilizing relevant data stores.
“The success of machine learning models relies heavily on the quality of the data that we're using to train those models.” — Vanessa Rios de Souza | director of client solutions | Aigora
“Machine learning is a field of study in artificial intelligence that involves the development of algorithms capable of learning from and generalizing data,” Rios de Souza explained.
Machine learning has become an advantageous tool in redefining product development strategies by streamlining the process and significantly enhancing efficiency. Companies can leverage historical data, consumer data and even social media data to train their models and achieve their target results.
“The key is to connect the data, and that is what’s going to help us speed up the process,” Rios de Souza said.
Sensory profiling, the most common application of machine learning, allows companies to evaluate their product through a consumer lens before it hits the shelves. Rios de Souza said that by utilizing AI to predict consumer response, companies can seamlessly connect different aspects of product development and alter products to align with their intended consumer reception.
The AI learning model can also perform simulations, giving companies the ability to easily compare the predicted success of prototypes and select the attributes they want to change.
While the implementation is relatively simple, Rios de Souza emphasizes that it’s important to train the model and provide it with accurate data and clear objectives to guide the process more effectively.
“The success of machine learning models relies heavily on the quality of the data that we’re using to train those models,” Rios de Souza said.
By utilizing AI tools such as this, companies can continue to provide their consumers with the best quality products without compromising.
