The shift reflects how one of the world’s largest consumer packaged goods companies is building internal platforms that let employees deploy AI assistants while applying generative models to research and development.
Rather than centralizing AI in a single innovation lab, Colgate has built an internal operating model to let employees identify problems, build assistants and deploy solutions at scale. At the same time, the company is using generative AI to accelerate biomaterials development and sensory design.
From Productivity Tools to an Internal AI Platform
Early AI adoption at Colgate focused on productivity, including copilots for writing, analytics and workflow automation. That phase quickly evolved into something more structural with the launch of the Colgate AI Hub, an internal platform that allows employees to build, test and deploy their own AI assistants tailored to specific business pain points.
Rather than pushing a single enterprise-wide chatbot, the AI Hub functions as a governed development environment. Teams can create AI assistants for specific use cases, such as analyzing sales performance, generating regulatory documentation or summarizing research insights, while operating within centralized security and data controls. The model shifts AI development closer to business users without sacrificing oversight.
This bottom-up approach is designed to solve a common scaling problem in enterprise AI adoption. MIT Sloan Management Review has described Colgate’s approach as a pivot from using generative AI primarily for efficiency toward using it as an engine for innovation, noting that the focus is increasingly on how AI reshapes product development and long-term growth rather than short-term cost savings.
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Agentic AI in Operations and the Supply Chain
Beyond individual assistants, Colgate is also deploying agentic systems that can operate across workflows, particularly in supply chain and operations. These systems monitor demand signals, inventory levels and logistics constraints, surfacing recommendations and coordinating responses across planning and execution teams.
Forbes has reported that Colgate’s supply chain AI strategy emphasizes decision intelligence over full autonomy. AI agents simulate scenarios, flag risks and recommend actions, but humans remain accountable for final decisions. The focus is on resilience and speed rather than replacing operators outright.
The same philosophy carries into manufacturing and quality control, where AI systems analyze sensor data, identify anomalies and support preventive maintenance. As these agents become more integrated, Colgate is building an AI layer that spans operations, linking data, decisions and execution in near real time, essentially a dual-track approach.
AI-Driven Materials, Biomaterials and Sensory Innovation
The most strategic use of AI at Colgate is unfolding in R&D and product innovation. The company is applying generative models to oral care research, biomaterials development and sensory design, moving AI upstream into the innovation funnel.
According to the European Federation of Periodontology, Kli Pappas, the senior director of predictive analytics and head of AI for Colgate-Palmolive mentions that the company uses machine-learning models to analyze global search engine data to identify what consumers are actually asking about oral health. Those queries are clustered into unmet needs and fed directly into early-stage product development and clinical strategy, allowing teams to design solutions grounded in real consumer concerns.
The company has partnered with biomaterials company Erthos to use its AI-powered platform Zya, giving Colgate direct access to the generative materials design tool to virtually design and optimize sustainable biopolymer packaging that meets performance and environmental goals, with the platform slated for commercial rollout starting exclusively with Colgate before broader availability in early 2026.
In parallel, Colgate is also using AI to rethink sensory design, including scent creation. The company’s Palmolive Aroma Essence initiative uses AI to analyze fragrance components and consumer preferences, helping perfumers design scents that align with emotional and sensory responses. Rather than replacing human creativity, AI augments it by narrowing options and revealing patterns that are difficult to detect manually.
