Unilever R&D head lifts lid on AI, robots and beating the ‘grease gap’

For many people, the personal-care aisle may be the last place you associate with machine learning. But for UK-based Unilever, decades of hair-protein data and more than 500 live AI projects have turned those shampoo and skin care shelves into a proving ground. Creating that foundation has been something of a “journey” over the years, according to Tiffany Yizar, the company’s head of beauty and wellbeing R&D in North America. The company has fed decades of hair-protein experiments, for instance, to understand individual and regional nuances of textured and straight hair types. Unilever boasts one of the highest concentrations of robots doing material chemistry in the world in a facility in Liverpool, UK.

The company’s R&D digital transformation journey has worked with its scientists to provide structured data capture, feeding everything from early development experiments to pre-production confirmation batches into its central data lake. It’s also worked to move away from manual data entry while connecting bench tools directly to this lake. “Similarly, our engineers, who are responsible for scaling up our formulations to be ready for production scale, are years ahead of making sure that they have digital twin pilot plant facilities,” Yizar said. This allows them to correlate data from scaled-up production simulations directly with what scientists experimented on years prior at the bench scale. The ambition, Yizar explains, extends further: “Our factories are also on that same journey, and we are looking at how we can integrate them even more so that we get a full closed loop,” she said.

This helps us to correlate what we looked at often three to five years prior as an initial hypothesis… to how we’re able to deploy that at scale

The task of connecting decades of experimental data across different scales and even incorporating post-launch consumer feedback, is changing how Unilever approaches science. In fact, the company’s digital muscle played a role in the 2024 launch of Vaseline ProVitaB3 Serum-Burst Lotion.  “Removing the silos of data really helps us unlock even more ‘aha’ moments where we can look at the data and results that a scientist in our Trumbull, Connecticut lab is generating, and compare it to results that a scientist in our Mumbai or Shanghai labs are generating and see if they are confirmatory,” she said. The approach “gives us the confidence to run an innovation to market, or see if it’s conflicting, and we need to dig deeper.” The digital focus also extends to tracking consumer signals to help the company gauge their satisfaction with a particular product. “If there is any gap in that, it enables us to respond pretty quickly,” she said.

Those “aha moments” have a decidedly human touch at Unilever’s Liverpool facility, where robots with names like Shirley and Ariana work alongside 250-plus R&D experts. “Shirley and Ariana are my robot besties for hair care,” Yizar said. In the lab, the robots help redefine workflows.

Mining the voice of the consumer to craft Vaseline’s new lotion

One recurring complaint on sites such as Amazon, Reddit (especially r/SkincareAddiction), and product feedback sites is lotions that leave the skin feeling “greasy,” “heavy,” leave “second layer coating.” Unilever decided to dig into the broader feedback on the subject. The company’s initial consumer research confirmed the matter was in fact a widespread barrier, but that broad insight alone wasn’t actionable enough for product development. “We’ve been able to use our digital voice of the consumer to get even more specific about not just that general insight, but specific cohorts of consumers who are raising that insight at specific times of year,” Yizar said. Logically, such complaints were more tightly correlated with consumers in hot and humid environments, and in summertime.

Unilever’s R&D team then turned to what Yizar calls extensive product profiling. “We look at sensory as a multi-factorial experience. We profile over 50 different attributes for a given lotion, and then we can model out where those products sit on those different attributes,” she explained. The Vaseline team used this mapping approach to identify an opportunity. “Our Vaseline team did that in earnest and identified this white space specifically that overlapped with that lighter sensory, faster-absorbing product,” Yizar said. The team used “our modeling tools and our mapping tools to be able to define a white space that the formulators could then develop a winning product for.” That technical brief became ProVitaB3 Serum-Burst Lotion, launched in 2024.

The real test came on a hot, humid day in New York City at the product launch event. “It was the first time I went to a Vaseline launch event where people were slathering lotion on themselves, because it’s just such a nice sensory, very fast-absorbing, very refreshing,” Yizar recalled. The serum-burst formula addressed the persistent “second-layer coating” complaint that had surfaced in the initial consumer research.

Global R&D lab lessons

Yizar emphasizes that by breaking down data silos across global R&D labs, for instance, in Trumbull, Connecticut; Mumbai or Shanghai, Unilever is now able to identify both confirmatory and conflicting results from experiments performed in parallel at different sites. Even conflicting data can be useful, as it helps the firm answer questions such as: “‘Is this a patentable space?’” Yizar said. “Is this somewhere where we might own something that is an ‘aha’ for us and not yet an ‘aha’ for somebody else?” In sum, the ability to break down the silos to make even more discoveries is “quite exciting right now, and cannot be done without our digital tools.”

This strategic approach to R&D, where even divergent findings are mined for unique insights, is reflected in the company’s substantial patent portfolio, which numbers over 20,500 globally.

Robots Shirley and Ariana tackle the tedious work

At Unilever’s Materials Innovation Factory in Liverpool, a team of over 250 R&D experts works alongside some unique colleagues with names like Shirley, Ariana, and Gwen. But while she refers to them as “besties for hair care,” they aren’t human. They’re robots. “What they really do unlock for me, or for the team there,  is to cut down the time for some of the routine work that is necessary for us to do biological research, scientific research in hair care,” she explains.

Take Shirley, for instance. The robot does “extensive washing of hair swatches, which for a regular scientist to even get through 10 washes a day would be quite laborious,” Yizar said. Shirley can process up to 120 samples every 24 hours. Her counterpart, Ariana, assists with detailed hair fiber evaluations while Gwen focuses on analyzing foam. This high-throughput, consistent work is “fully integrated into the information supply chain that we have in R&D,” Yizar said.

The robotic assistance translates directly into tangible R&D gains. For example, the TRESemmé Colour Radiance Booster range uses technology Shirley helped identify through persistent hair-washing experiments. “As I think of the multi-year landscape for us to get to our bio protein care in Dove, leveraging that type of robotic capability helped us to get deeper and move a bit faster early on when we were proving out the technology that went into that product,” Yizar said.

The robots and data lakes represent just the current phase of Unilever’s digital transformation. Following a large cloud migration with Microsoft and Accenture, The company is using Microsoft’s Azure Quantum Elements, a system that aims to accelerate the discovery of novel chemicals and materials. Yizar points to the discovery of unique molecules that support its sustainability and microbiome programs.

Through the UK’s £210 million Hartree Centre collaboration with IBM, Unilever also has gained access to IBM’s Quantum Network, which provides more than 150 organizations with access to IBM quantum computers and development tools.

​​Pull beats push

Across its digital R&D efforts, Unilever is creating an ever-tighter closed loop, one that seamlessly connects decades-old bench data with real-time customer feedback to continuously refine products and drive innovation. The value of this integrated approach has grown so clear that, as Yizar highlights, it has achieved momentum: “The benefits that everyone from the chemist at the lab bench all the way up to those of us at leader level have been able to see really has made that adoption something that we’re getting a full organizational pull from, instead of having to push at this point.”