With a 150-year industrial legacy and a portfolio that spans adhesive technologies and consumer brands, Henkel operates at a scale where complexity is constant and volatility is the norm. Serving both B2B and B2C markets across global supply chains, the company’s challenge is not just growth, but sustained, resilient growth across a highly diversified business.

In this environment, data, analytics, and AI are not treated as standalone capabilities. They are embedded into Henkel’s broader data strategy and digital transformation strategy, supported by a scalable data architecture that enables faster innovation, operational clarity, and smarter decision-making across the value chain.

In Part 1 of this three-part CDO Magazine interview series, Katrin Botzen, Corporate Director, Global Data and Analytics at Henkel, in conversation with Julian Schirmer, Co-Founder at OAO and Academic Director at HEC Paris, outlines how the company is applying AI to real business problems, from reducing R&D trial-and-error to improving supply chain visibility and turning fragmented data into decision-ready insight.

The business context: Volatility, growth, and the supply chain reality

Asked about the environment Henkel operates in, Botzen points to volatility as the baseline. “We are living in a very fragmented and a very volatile world,” she says.

The strategic challenge, as she frames it, is sustainable growth across a diverse portfolio. “It’s not that we serve only one customer segment or only B2B customers or only B2C customers,” she adds.

That growth imperative connects directly to research and innovation. Botzen describes the need to “find the right products for the right problems of our customers and consumers” and to “get faster to the market with them, together with the products they need.”

At the same time, supply chain constraints remain a daily operating reality. “We are heavily dependent on raw materials and are sourcing from across the globe,” she says.

Why AI matters: From data strategy to business impact

Botzen shares that while AI is increasingly embedded in everyday tools, Henkel’s approach reflects a deliberate generative AI strategy aligned with Henkel’s broader data strategy grounded in real business needs. “We start with the business problem and proceed towards the solution,” she says.

In her view, AI’s role is tied to the growth and efficiency challenge. It is applied across the value chain and across business models, from R&D to supply chain to customer-facing experiences. 

Botzen describes R&D as one of the most important arenas for data and AI, particularly when the goal is to reduce trial-and-error and waste. She frames the ambition as moving away from “trying here a bit and trying there” toward “an AI-driven process.”

She also mentions supply chain as a data-intensive environment where transparency and real-time decision-making matter. On the customer side, she highlights recommendation engines that help users make better choices.

The overall point remains consistent. Henkel treats AI as something that should be open to use across the organization, wherever there is a problem to solve or efficiency to unlock.

Turning fragmented data into insight with AI and data governance

Sharing examples Henkel is proud of, Botzen starts with a use case in finance that addresses a common enterprise pain point: information scattered across too many dashboards. She describes “a chatbot” solution that is “way more than a chatbot,” positioning it as “an assistant for the regional finance teams in the controllers.”

The problem is not a lack of data, but overload and fragmentation. “Dashboards are like mushrooms. They pop up everywhere,” she says, and the result is that it becomes “difficult to find the right KPI and the right information at the time that you need it.”

The assistant pulls information together, running SQL against databases and returning answers based on the user’s question rather than forcing users to hunt through reports. Botzen also emphasizes the importance of trusted outputs and clarity about what is AI-generated. “We say this is now AI-generated and this is trusted information,” she explains, so users know what they can confidently reuse.

For a global enterprise, accessibility matters as much as logic. Botzen notes that regional teams need information “at the same time, in their language” and describes the assistant as being available like a colleague 24/7, integrated directly into Teams.

The solution reflects a broader investment in data governance and underlying data architecture, enabling users to access trusted, consolidated information without navigating multiple systems.

Knowledge loss and the reality of digitalization

Schirmer raises a concern many global companies share: retirement-driven knowledge loss. Botzen agrees but does not frame the solution as purely technical. Technology helps, she explains, but the foundation is often basic digitalization. Knowledge may exist “in their heads,” but also “in their notebooks, or in their drawers, somewhere on paper.”

She highlights R&D as a particularly urgent area because recipes and experiments still rely on paper-based work. “That’s not helpful if you want to work with these data points, in AI or in any analytic solution,” she explains.

Botzen also describes a people-centered mechanism: “generation tandem,” where employees nearing retirement and younger employees share a role for one to two years, so knowledge transfers before the transition happens. “That’s always a combination of technology and processes, but also people,” she says.

Building a data architecture foundation for smarter R&D

Botzen’s second major example stays close to R&D. Henkel runs a broad program called the “Raw Material Hub,” designed to bring raw material information into a single system so teams can search, compare, and make substitution decisions more effectively.

The hub supports both practical constraints, such as availability, and strategic goals like sustainability. She explains that R&D teams can “look for substitutes” and receive recommendations based not only on sustainability requirements but also on availability.

She also positions this as enabling infrastructure. Once data is consolidated and accessible, it becomes the foundation for more advanced use cases. The challenge is not vision, but integration. Moving toward an AI-driven process depends heavily on strong data quality management, ensuring inputs are consistent, reliable, and usable across experiments. In her view, this unglamorous work of connecting systems is what ultimately enables AI readiness.

The program’s importance is reinforced internally. The Raw Material Hub represents a foundational shift in data architecture, supported by a structured data governance framework that ensures information is accessible, standardized, and usable across R&D teams.

Botzen notes that the initiative received internal recognition through the “Connected Labs” project, underscoring that accessible, well-structured data is what enables chemists to effectively use digital solutions.

Data sovereignty, security, and the next layer of enterprise risk

As Henkel operates across regions, data sovereignty becomes a growing concern, raising practical questions about where data resides and how it is governed. This also brings into focus the distinction between data privacy vs data security, alongside broader enterprise data compliance requirements across jurisdictions.

Botzen does not frame risk as a distant possibility. “Threats are everywhere already,” she says.

Cybersecurity is a top concern, driven by rising attack volumes and increasingly sophisticated tactics. She points to the growth of social engineering, blackmail, and ransomware, and stresses that defenses require more than tooling. “This is also not solvable with technology only. It has a lot to do with people,” she says.

Concluding, Botzen flags data sovereignty as an escalating complexity, especially for a company operating across regions with different regulations and expectations. “Where is my data? Where can my data be?” she asks, noting the practical tension of operating across Europe, the Americas, and China, each with its regulatory landscape.

CDO magazine appreciates Dr. Katrin Botzen for sharing her insights with our global community.