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Interview: Differentiating with AI in pet care

We speak to Mars Pet Nutrition’s head of digital innovation about making artificial intelligence relevant across its brands to support pet health

Cliff Saran
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Published: 19 Aug 2025 10:55

Over the past year, Kate Balingit has been leading the digital health initiative at Mars Pet Nutrition, reporting to the company’s pet care chief information officer, where she is focused on commercialising and deploying artificial intelligence (AI) through the Mars Pet Nutrition brands. These include well-known pet food brands such as Pedigree, Iams, Sheba and Whiskas.

“Even though we’re building tech products, Mars is a non-tech company,” says Balingit, whose official job title is head of digital innovation. “We kind of abide by the same standards of scientific credibility and scientific rigour that apply to our primary business of food.”

A former Googler, who was also involved in Waze, Balingit joined Mars Pet Nutrition in 2022 to head up Whistle, the “FitBit for dogs” company acquired by Mars in 2016 (see box: Career at Google).

She says Mars has made a large commitment to digitising the pet care business. This includes everything from upskilling staff to digitising factories and its supply chain, as well as elevating the e-commerce experiences. Digitisation also covers the use of emerging technologies such as agentic AI for automating workflows and mining digital health data.

On the AI front, rather than rely on existing large language models (LLMs), Balingit says the business is focused on building the computer vision algorithms itself.

“We’re building image classifiers to detect signs of emerging health conditions and enterprise software components that enable us to create user experiences that can safely live on our brand digital properties. It comes down to differentiated assets – our proprietary datasets bootstrap an image database and then we work with vets to label the images and train the algorithm,” she says.

According to Balingit, these algorithms go through the same kind of scientific governance rigour as the food part of the business. “We have to be able to say where we sourced our data. We’re also very explicit about publishing how we train the models,” she says.

This, she adds, is a differentiator: “You don’t get a free pass just because you’re working with algorithms. At a non-tech company, you have to abide by the same quality standards that apply to the entire business.”

• Listen to the full interview with Kate Balingit in this podcast •

One of the challenges the company aims to address is how to build products and digital experiences that meet the unique needs of individual brands, individual business units and offer a unique differentiator. A lot of the work involves its data architecture for structuring all the data the company collects from pet parents who use the apps and applications the company develops.

“We’re working with emerging technologies like computer vision and trying to build products with a platform approach to enable us to repurpose these assets in different types of applications,” she says. “My team takes a very component-based approach. I don’t see us building products. Instead, we are building a series of capabilities.”

Digitising pet care

There are around 200 people working in the digital transformation organisation at Mars Pet Nutrition. Balingit’s role involves orchestrating initiatives across three core functions: science, data science and software engineering.

“The digital health initiative starts with science. We’re building scientific instruments,” she says. “I start by partnering with the global R&D [research and development] science function, which includes specialists in oral health, skin health, gut health and healthy ageing.”

These algorithms are being designed to detect the emerging presence of health conditions in pets.

The team puts together a specification for the product, such as deciding on the symptoms of a health condition that the software and AI it produces will be able to detect. The data science team is used to build the algorithm to detect the health condition.

“In the case of a canine dental check, we’re detecting plaque, tartar and gum irritation. I work with our data science team to build the algorithm – we have to acquire the training data and label it, then we build the computer vision models using Azure developer tools.”

Career at Google

Prior to her role at Mars Pet Nutrition, Kate Balingit worked at Google, joining the company in 2010 at a time when the search engine giant was beginning to commercialise its paid media products.

“When I started, Google was about a 15th of the size that it is now, so I feel privileged to have been there in the early days when you could be creative about commercialisation strategies,” she says.

During her nine-year tenure at Google, Balingit worked on the automotive vertical team, which helped carmakers such as Toyota, Audi and Ford use Google’s advertising products in innovative ways.

“It was digital transformation before I really knew what that term meant,” she says. “We worked with global enterprises that had to transform a lot of their internal processes to accommodate for the realities of the new digital economy.”

Balingit then joined the part of Google’s diversification strategy looking at mobility services, and later had a role at Waze. “I learned the principles of building a massive structured data flywheel [at Waze], collecting user annotations about the state of the road, curating those into services like navigation, intelligence for transit agencies and eventually building our own ride-sharing platform,” she says.

But after being at Google and Waze for almost a decade, she says: “I was itching for a really big change, so I moved to Tel Aviv, where I joined a deep tech startup specialising in computer vision services.”

In 2022, she joined the food company Mars, initially to head up Whistle, the “FitBit for dogs” company acquired by Mars in 2016. 

The algorithm is made available via an application programming interface (API). Balingit then works with the software engineering team on the product experience. “It’s a truly cross-functional effort,” she says.

The software not only needs to meet the high standards associated with the brand, but a high bar is also set for the enterprise architecture, data security and data privacy. With these high standards, Balingit says: “Data science and software engineering can do something really special, which is to scale scientific understanding and put these capabilities into the hands of pet parents around the world through our biggest brands.”

Greenies is an example of one of its brands with an AI tool. “Our use of AI in the Greenies Canine Dental Check tool started with a pet parent insight. We know that 80% of dogs have signs of periodontal disease by the age of three, but 72% of pet parents think their dog’s oral health is fine,” she says.

The team wanted to address this awareness gap among pet owners using AI to, as Balingit puts it, “make the invisible visible and help people to understand that their dog is experiencing an oral health issue”.

Portrait photo of Kate Belingit

“We’re very explicit about publishing how we train the models. You don’t get a free pass just because you’re working with algorithms”

Kate Balingit, Mars Pet Nutrition

The Greenies Canine Dental Check required a computer vision algorithm trained on more than 50,000 images of dogs. The algorithm was built to identify whether a smartphone photo shows a dog, and if so, whether the dog’s mouth and teeth are visible. It then analyses the image to determine whether the dog’s teeth have visual signs of oral disease.

When asked about the ease of capturing photos of teeth in a dog’s mouth, she says: “We always encourage caution, but when I’ve looked at the data, the average user captures about 10.2 teeth in the photo.” So, while it may seem a major undertaking for pet owners to attempt taking smartphone photos of their dog’s mouth with teeth visible, in Balingit’s experience, pet parents are “very capable”.

Another consideration is the level of accuracy. “No algorithm is going to be 100% accurate,” says Balingit. “A human is not 100% accurate. What’s really important is that we are not building a diagnostic device. Our goal was to build a health screening instrument that could find visual indicators of an emerging disease.” As such, the level of accuracy it can achieve of 97% is good enough.

An approach to business AI

As Balingit notes: “AI is just top of mind for everybody right now.” Like many businesses deploying AI applications, she points out that the past two years have been “a whirlwind”, which means companies such as Mars Pet Nutrition need to figure out what they should be doing with AI.

“It’s important to be intentional about what we’re doing, and the key question for me is, ‘What do we at Mars Pet Nutrition have that an AI company in Silicon Valley doesn’t have? What are our unique assets and how do we build an AI innovation agenda on top of them?’”

Looking to the future and advances in digital technologies, Balingit believes the world of internet of things (IoT) sensors and AI offers a tantalising opportunity for the business and pet owners alike. While people talking to their pets like Dr Dolittle may seem a bit far-fetched, she says: “Our pets do talk to us with their movements, their facial expressions.”

Inevitably, many pet owners may miss these subtle signs, but AI could offer a way to spot them.

Balingit sees an opportunity to use sensor data to help quantify animal behaviour and then apply AI to translate the sensor data into something humans can understand. In a world where digital technologies have made people ever more disconnected from the real world, tech innovation may one day offer a way for pet parents to have a closer relationship with their furry friends.

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