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Colgate-Palmolive, the consumer products giant whose origins date back to 1806, has long been a data- and insights-oriented company. It has gathered consumer market research for decades and has employed innovative analytics to make sense of that data. It has also been a regular user of analytical artificial intelligence — traditional machine learning — to address areas like product pricing, promotion, and assortment, as well as marketing and media effectiveness.

Like almost every big company these days, Colgate-Palmolive has also embraced generative AI to enhance the productivity of its employees. “We didn’t want to simply dabble in various aspects of generative AI — it’s all about driving value,” Diana Schildhouse, the company’s chief analytics and insights officer, told us. “We focused on a few key enterprise priorities with this technology, such as innovation and marketing content creation.” These are areas with measurable business value.

The company has been able to bring together traditional AI and generative AI in ways that weren’t previously possible, Schildhouse added. Below, we detail some of the value they’ve gotten from the new technology so far.

Generative AI as a Repository of Market Knowledge

There are several applications of generative AI at Colgate-Palmolive that fall into the “innovation and growth” category. One is synthesizing consumer insights using GenAI to provide instant answers to business questions. Synthesis is based on the full history of insight reports across all geographies and categories that are available internally, as well as trusted external or trusted third-party sources.

The large language models (LLMs) that Colgate-Palmolive’s teams use have been augmented with retrieval-augmented generation (RAG) content of various types — proprietary research that the company conducts, Google search trend, syndicated data sources, and more. RAG-based systems draw more on company-specific content than on public internet materials, so there is a lower likelihood of hallucinations. Generative AI can quickly go through such material and describe market trends and unmet consumer needs. That means that instead of downloading, reading, and notating a broad collection of market research reports when they want consumer insights, employees can just write the question they want answered in a prompt and immediately get a response. RAG-based LLMs also can report on the source documents that were consulted to inform an answer.

Generative AI to Enhance Innovation

Colgate-Palmolive saw an opportunity to enhance its innovation processes with generative AI. “We wanted to use AI to grow the business, not just drive efficiencies,” Kli Pappas, senior director of predictive analytics and global head of AI at Colgate-Palmolive, told us. “Innovation is the center of our growth strategy and is a great match for what generative AI models excel at today: being creative and enabling fast iteration cycles where more people are able to participate.”

Colgate-Palmolive’s teams found that they could combine one AI system that surfaces unmet consumer needs with another proprietary AI system that develops new product concepts to meet those needs. In minutes, with human guidance, it can produce copy and imagery for a new concept, such as a new flavor of toothpaste. While there are always humans in the loop to guide the workflow, using the GenAI-enhanced system is much more efficient than having humans page through market research materials. The breadth of ideas generated also creates a broader product funnel for the company to pursue.

While that use of generative AI is an innovative idea, what happens to the concepts after the model outputs them and human marketers review them is just as innovative. The company has created cohorts of “digital consumer twins” that suggest what real consumers might think about the new product concepts. Unlike focus groups, the digital twins are indefatigable, and Colgate-Palmolive can test scores of concepts and ideas at a time with ease. Previously, innovators needed to whittle their options down to just a few concepts before testing. Product concepts still need to be validated against real consumers as a gold standard, but the digital twins allow for more rapid early-stage iteration.

Early in the company’s use of digital consumer twins, researchers compared the opinions of the virtual consumers with those of real humans and found a high level of concordance between the two groups. They have also tested the AI-augmented product concepts against those developed by humans alone and found that the ones augmented with AI support are just as good or better on many measures. It’s still early days for this approach, but the combination of AI-augmented concept generation and digital twin testing seems to be a felicitous one.

Democratizing AI, Responsibly

Colgate-Palmolive is encouraging the use of AI through its AI Hub, which hosts internal versions of OpenAI and Google LLMs as well as image-generating models. As at many companies, these tools reside behind the corporate walls so that employees are free to employ prompts that include proprietary knowledge.

All Colgate-Palmolive employees are required to undergo training on AI use in order to access AI Hub. They learn about guardrails, the importance of keeping a human in the loop, and the company’s responsible AI principles. They also learn practical tips about selecting use cases and crafting effective prompts. Schildhouse’s organization previously sponsored data analytics and literacy badges (for successful skills acquisition), which were also popular.

Given the focus on providing measurable impact with AI, AI Hub automatically tracks the value it provides across multiple KPIs through a system of built-in surveys and prompts for user feedback. Pappas said that thousands of users have reported increases in both the quality and creativity of their work with AI.

Generative AI is no doubt a powerful tool, but it has primarily been employed thus far for individual productivity and experimentation. While these activities undoubtedly provide value, they are often difficult to monetize. We think it’s a big step forward when companies like Colgate-Palmolive begin to use GenAI for market research, concept generation, and semi-autonomous task performance. We hope that more organizations will begin to develop and implement such use cases.