Transparency separates successful AI nutrition innovation from hype

Amid a growing use of AI in ingredient and product innovation, industry experts caution that transparency is key to building trust with customers, regulators, and consumers. They urge companies to communicate clearly how AI was used and reiterate the essential role of research to validate insights. 

In the final installment of our series on AI in nutrition, Nutrition Insight meets with Brightseed and independent marketing and R&D experts to explore how successful companies separate genuine innovation from hype. 

In previous articles, we unpacked how the technology reshapes innovation from discovery to commercialization and highlighted the role of scientific validation of AI insights. 

Palak Uppal, an independent nutraceutical innovations and AI strategy expert, calls on companies to use AI more. “It’s not taking jobs; it’s providing the flexibility to go quicker and smoother,” she says. “AI should increase confidence and innovation, not reduce it.”

“It cannot take away our clinical leads who design the experiments. But AI gives such a big framework for start-ups and entrepreneurs, which would never be possible otherwise, because they need a lot of resources.” 

Jenny Mason, managing director at BDB Global, a B2B marketing agency in the nutrition and food ingredient industries, adds that transparency and validation are essential to using AI successfully.

“On the one hand, transparency means being open about where AI contributed to the process. Did it help analyze data, identify a potential application, predict an outcome, or support formulation?”

“On the other hand, validation is what turns a promising AI-supported insight into something people can trust,” Mason details. “Any conclusions or claims still need to be supported by credible evidence and reviewed by the right scientific and technical experts.” 

Transparent use

Uppal urges companies to be clear about where they use AI and human expertise. For example, AI could provide a new idea that companies may turn into a solution.

Uppal calls on companies to use AI more, noting that it should increase confidence and innovation, not reduce it.“It can give you an assumption or the benefit of the doubt, but to prove that doubt, you need human expertise,” she adds.

Furthermore, Uppal notes that companies should highlight what evidence supports claims, whether an ingredient is lab-tested or supported by clinical data, and not projected by AI.

“You can predict something, but you need the evidence for it. Consumers increasingly want to understand not just what’s in the product, but also how it was developed and what led to that discovery.”

Lee Chae, Ph.D., co-founder and CEO at AI-powered company Brightseed, also believes that transparency needs to be meaningful. 

“Consumers, regulators, and industry partners do not simply need to hear that AI was used. They need to understand what role AI played, what the science shows, and how the ingredient or product was validated.”

He says that the message to consumers should focus on the health benefit, the evidence, and the process’s integrity. “AI can be exciting, but it should not become a substitute for clear communication about what an ingredient does and why people can trust it.”

“For regulators and industry partners, transparency needs to go deeper: data provenance, mechanism of action, substantiation, safety, claims support, and validation pathways all matter.”

Successful AI-led innovation

Uppal underscores that successful companies don’t simply want to access AI, but will combine it with high-quality data and experts who know how to interpret that data and AI-generated insights. 

“Organizations will have to combine particular components: strong scientific exposure; robust validation; regulatory understanding; and commercial insight,” she adds. “And of course, disciplined project management, which helps navigate all these different components and strategically understand them.”

Chae highlights proprietary data, clinical validation, transparency, and continuous innovation models as key to a successful use of AI.BDB Global’s Mason highlights that successful AI-led ingredient brands will use the technology to strengthen operations, without letting it replace too much of the thinking behind them.

“Before long, the AI tools themselves will be a commodity,” she predicts. “The proprietary knowledge, technical expertise, and the depth of understanding built through close customer relationships will remain harder to copy.”

“The brands that bring these strengths into how they use AI will have a much stronger foundation than those relying mainly on the same tools and public information as everyone else.”

Mason underscores that this difference should also show in how brands communicate: in the specificity of their positioning, the quality of their proof points, and the relevance of the stories they choose to tell.

“There is also a temptation to turn every gain in speed into more output. The more strategic move is to instead use that time to ask better questions, challenge assumptions, and make clearer choices. In ingredient marketing, where credibility and relevance matter so much, volume has never bought influence, and it will not start now.”

Evidence-based differentiation

Rather than a “magic answer,” Chae at Brightseed says successful companies will position AI as a technology that helps accelerate discovery and improve decision-making. He underscores that an ingredient’s credibility still depends on the strength of the science behind it. “Transparency is what helps the industry move from novelty to trust.”

“The companies that create real value will be those that can connect AI-generated discovery to validated mechanisms, clinically meaningful outcomes, defensible claims, and commercially viable products.”

He says that companies positioning AI as a shortcut will not be successful. “Real leadership will come from companies that use AI to make innovation more rigorous, more traceable, and more likely to succeed.”

Proprietary data is another differentiator for companies, says Chae. Although general-purpose AI can be useful, he notes that functional ingredient innovation requires deep biological context, structured scientific data, and domain-specific expertise.

“The companies that win will have differentiated datasets, scientific depth, and platforms that preserve learning across discovery, development, and commercialization.”

According to Mason, human judgment is key in the distinctive use of AI, providing clarity, technical insight, understanding of customer needs, or a new perspective.“The final separator is whether AI changes the innovation model itself,” he continues. “If AI is used only as a faster search tool, its impact will be limited. If it becomes part of a continuous innovation platform — one that helps teams discover, validate, develop, and make stronger decisions over time — it can fundamentally improve how the industry brings new health solutions to market.”

Distinctive marketing

Although speed and cost are important considerations for any organization, Mason notes that they make weak marketing briefs. She says that a brand and what it represents are more effective starting points for distinctive marketing, where AI can support.

“In our work, the most distinctive material on which to build a brand is almost always already inside the business. It may sit in technical teams, customer conversations, formulation experience, application data, or the problems its people solve every day.”

She explains that AI can help connect those threads, uncover patterns, and explore different ways of expressing ideas. “That is a world away from asking a tool to conjure a campaign around broad themes such as ‘innovation’ or ‘sustainability,’ the very words every competitor is reaching for at the same moment.”

Mason adds that the quality of input for AI matters “enormously” for the tool to support distinctive marketing.

“Feed a model generic input, and it will hand back generic output every time. Give AI richer context to work with, including proprietary insight, technical evidence, audience understanding, and a clear point of view, and the results will significantly improve.”

She believes that the most underused application of AI is using it to challenge ideas rather than generate them. When used well, AI can become a critic, for example, by helping to identify familiar category language, test how a message may land with different audiences, or explore alternative routes.

“The final decisions still need human judgment, though,” she emphasizes. “Distinctive marketing comes from expressing something specific with greater clarity: a technical insight, a customer problem the business understands deeply, or a perspective others have missed. While AI can help surface and shape those ideas, the experience and conviction behind them have to come from the people that make up a business.”