Unpacking how AI reshapes nutrition innovation from discovery to commercialization

Key takeaways

AI is reshaping the nutraceutical innovation pipeline end-to-end, from ingredient discovery through commercialization.
Industry experts say the technology compresses discovery timelines and shifts the challenge from generating ideas to choosing between them.
Marketing experts caution AI’s risks of industrializing sameness in commercialization unless brands structure evidence for machine readers.

AI has made its mark on the nutrition industry, from consumers increasingly using the technology for personalized advice to businesses leveraging it in product development and marketing.

In this first installment on AI’s role in nutrition, Nutrition Insight explores the tool’s potential from new ingredient discovery to commercialization with experts from AI-powered nutraceutical suppliers Brightseed and Nuritas, as well as marketing and AI strategy experts.

“AI will fundamentally change how the nutrition industry moves from possibility to proof,” states Lee Chae, Ph.D., co-founder and CEO at Brightseed. “Historically, functional ingredient innovation has been constrained by what humans could manually search, test, interpret, and connect across fragmented

“That model has made discovery slow and development risky, with many promising ideas failing only after significant time and investment have already been committed.”

He believes that the next phase will be defined by continuous, AI-powered innovation. “Instead of treating discovery, validation, formulation, and commercialization as disconnected stages, AI can help connect biological insight, evidence, and decision-making earlier in the process.”

Chae says that this means companies will be able to evaluate more bioactive opportunities, understand mechanisms of action sooner, and make stronger product innovation decisions before moving into costly downstream development

Chae argues AI’s greatest value is sharpening go/no-go decisions earlier in discovery.Dr. Nora Khaldi, founder and CEO of Nuritas, agrees that AI accelerates the discovery and development of functional ingredients.

“It helps teams compress timelines from years to months by quickly screening massive molecular libraries much faster than traditional methods. For example, Nuritas Magnifier has identified more than eight million peptides.”

“AI enables a shift from trial-and-error screening to more predictive, targeted discovery,” she highlights.

Boosting innovation

Chae notes that the most significant shift that AI can make in the nutraceuticals and functional ingredients sector is to expand the scope of opportunities.

“There are vast areas of biology and natural chemistry that remain underexplored. Our platform enables us to illuminate those spaces at a scale that would not be possible through traditional research alone, helping identify bioactives and mechanisms that can support more differentiated, science-backed products.”

He says that AI can add value across the innovation lifecycle, but that its greatest impact is in improving the quality of decisions earlier.

“In ingredient innovation, companies often face a large number of possible directions: which bioactives to pursue, which mechanisms matter, what evidence exists, what claims may be supportable, and whether a concept can ultimately become aer and with more scientific context.”

For example, Chae says that in discovery, AI can identify bioactives, biological targets, and mechanisms that may be difficult or impossible to find through manual literature review or traditional screening alone.

“In development, it can help prioritize which opportunities are most scientifically defensible, where evidence gaps exist, and how different ingredients or combinations may support a desired health benefit.”

Khaldi says AI compresses peptide discovery timelines from years to months at Nuritas.Meanwhile, in commercialization, he adds that AI can support claims substantiation, regulatory readiness, and partner confidence by making the scientific rationale more traceable and organized

Where does AI add most value?

Chae underscores that AI’s value is not simply speed. “Speed matters, but speed without scientific rigor creates risk.”

“The greatest opportunity is using AI to improve the probability of success, helping teams pursue better ideas, reduce rework, and develop ingredients and products that are more differentiated, defensible, and ready for market scrutiny.”

Khaldi sees AI’s clearest value upstream in the development process, in the identification of bioactive candidates within naturalteins and predict their phenotypic properties before lab work begins.”

“Nuritas’ Magnifier can also strengthen the predictive power of peptides as functional ingredients, helping R&D teams understand which peptides identified as promising are worth advancing based on gastrointestinal digestion survival, stability, and absorption potential. This helps cut speed-to-market in the upfront of product development.”

For example, the company used this platform to identify peptides for its PeptiSleep ingredient, derived from rice bran protein. Last year, a pilot study using sensors to measure biometrics showed that 61% of participants fell asleep faster with the ingredient.

Meanwhile, Khaldi underscores that although AI is reshaping the front end of R&D, functional ingredients and nutraceuticals still require validation (e.g., in vitro, in vivo, and human clinicals). “The reshaping will be successful for those who pair AI with deep scientific knowledge and data.”

Jenny Mason, managing director at BDB Global, a B2B marketing agency in the nutrition and food ingredient industries, adds that the technology will surface patterns that would once have taken teams far longer to identify as it becomes better at connecting scientific, formulation, sensory, and market data.

“At discovery, this shifts the challenge from finding opportunities to deciding which ones are worth pursuing. Ingredient businesses still need to consider scientific validity, scalability, economics, format, regulation, and consumer relevance, all of which influence whether an idea is ultimately commercialized.”

Uppal points to AI cutting literature review from weeks to hours in early development.She cautions that it becomes more important to choose between AI-based ideas, rather than generating them. “Bringing more signals together sooner helps businesses make sharper decisions about where to focus their time and investment.”

AI in product development

Palak Uppal, an independent nutraceutical innovations and AI strategy expert, walks us through different applications for AI in the product development process, especially for start-ups or smaller companies.

“First, AI is going to help find all the data. Good data is where your best solutions lie in the future, so I would advise identifying it in the best way you can.”

“Traditionally, scientists spend weeks reviewing literature, patents, and market data before devising their hypotheses. Now, AI reduces that effort to two hours. It’s definitely going to make things very quick, and it’s going to help synthesize scientific publications — identifying emerging mechanisms, what the gaps in the market and the research are, and surfacing novel combinations.”

“The second step is considering your regulatory red flags,” Uppal adds. “You might love an ingredient, but if it has red flags, you need to be careful — for example, if the US FDA is not clearing it.”

She notes that AI can also help gather market insights. “If you’re creating a product for which there’s no market, then it’s a beautiful creation with no outlet. You want to understand what the market requires and how to help the consumer bridge the gap.”

Uppal also details the technology’s support in helping companies with benchtop scaling — moving a new discovery to production at a pilot plant or factory.

She says R&D program leads can use AI to help brainstorm around solutions, in addition to discussions with engineers. For example, from analyzing data on what is needed to scale production to helping to find new machinery or other companies with the technology to support scale-up.

In sales or commercialization, she suggests gathering data from experts in the industry, for example, at international trade shows. “Anyone coming to these exhibitions knows what they’re talking about in supplements.”

Mason warns generative AI risks industrializing the sameness already present in ingredient marketing.“Once you gather that data, good AI can process it, giving you answers to many different aspects. This is something AI can do better — process that data and help find consumer needs, and then you can hedge your product into the commercialization plan.”

Commercialization and marketing

AI also plays a role in nutraceutical commercialization and communications, says BDB Global’s Mason. She cautions that marketing will evolve along with AI, which is becoming part of how buyers, formulators, and regulators research ingredients, products, and health topics.

“Evidence will need to be clear, well-structured, and easy to interpret,” she adds. “The brands whose evidence is clearest and best structured are the ones AI will surface, and the others are the ones it quietly passes over.”

At the same time, Mason notes there is a “real risk” that marketing language, visuals, and brand positioning become increasingly indistinguishable as more ingredient companies use the same generative AI tools.

However, she underscores that AI did not create this problem, but inherited it. “Ingredient marketing has been wrestling with sameness for a long time. Similar claims, familiar visual cues, and broad language around innovation, science, and sustainability already appear across much of the sector.”

She says that generative AI is very good at recognizing patterns and producing answers based on the input it has available. “It is effectively industrializing the sameness that was already present in the industry, making existing conventions effortless to reproduce at scale.”

When given broad prompts and little meaningful context, Mason warns that generative AI’s output will lean toward what is already comfortable. “Results are polished, competent, and entirely interchangeable, eroding the specific ideas, experiences, and beliefs that make a brand recognizable.”

“For ingredient brands, that makes an already crowded market even harder to cut through,” she adds. “As the volume of content increases, familiarity alone is unlikely to be enough to earn attention or build preference. The question every company should be asking is whether its content would still be recognizable with the logo removed. For most today, the honest answer is no.”