Editor's note: This interview is part of a series with executives leading GenAI adoption. Read the second and third installments.
Ipek Ozsuer, Chief Digital & Information Officer of dsm-firmenich—an innovator in nutrition, health, and well-being —leads the company’s AI adoption as part of a broader digital transformation. With 25 years in tech, she explains the company’s strategic and controlled approach, focusing on transformative areas like customer engagement, innovation, and operational efficiency, while emphasizing training and change management to support this shift.
What concrete steps have you taken to integrate GenAI into your organization?
While we were not early adopters of GenAI, over the past year we’ve gained valuable insights from our partners, peers, and other industries, which helped us learn from others and leapfrog in our approach. We have approached GenAI with both optimism and caution, starting with a guided pilot of our internal ChatGPT and Copilot, which significantly increased adoption and accessibility. We also educated executives on GenAI to establish a common understanding.
We decided to focus on specific areas while building key foundational components: data, algorithms, and responsible AI use. Our focus areas align with where we aim to differentiate as a company:
- Customer Obsession: We aim to deeply embed ourselves in the consumer industry, distinguishing ourselves through co-creation and innovation. GenAI helps us gain deeper insights into consumer needs, enabling us to offer more tailored solutions.
- Innovation in Product Development: Together with our Chief Science Officer, we recognized GenAI’s potential to discover new ingredients. With evolving regulations and sustainability needs, GenAI supports us in formulating products and leveraging scientific knowledge in co-creation.
- Operational Efficiency: GenAI brings significant productivity gains, helping us streamline operations.
What strategies have you found effective in encouraging your team to embrace new technologies?
We empowered our employees with the right tools and allowed them to experiment. Staying focused and identifying the right use cases drove interest from our business units, increasing adoption and value creation.
After selecting our priority areas—Customer Obsession, Product Innovation, Operational Efficiency, and Employee Empowerment—we aligned with each business unit and began identifying key use cases. We assessed shared use cases across our three businesses—Human Nutrition Care; Taste, Texture, and Health; and Perfumery and Beauty—that allowed us to pilot in one area and scale across others.
Initial use cases include marketing content creation and customization, accelerating formula development, and enhancing efficiencies in operational and sales processes, such as shortening proposal cycles.
With agent AIs like ChatGPT and Copilot, we provided extensive training and guidance. Employees who followed the training instructions reported the greatest benefits. At this stage, we’re moving toward a full rollout.
Our vision is a connected ecosystem where all agents can communicate seamlessly through a single interface. With both AI and GenAI use cases, our guidance is to avoid creating another interface, but rather to make our existing digital products more intelligent and augmented.
What has been one challenge throughout this process?
One of our key challenges has been ensuring access to the right and high-quality data to train our systems. We need to become more disciplined in managing our data, especially now.
Additionally, we must ensure responsible AI usage, which includes understanding potential biases, assessing harm, and carefully selecting training data.
What strategies have you found effective in driving behavioral change toward AI adoption?
Driving AI adoption requires significant change management efforts, starting with educating employees on using GenAI effectively—training them on how to ask the right questions and understanding the tools’ capabilities. Employees need training not only on tools but also on adopting new ways of working. If tasks continue to be performed the same way as before, employees won’t fully transition. For example, we no longer use traditional translation services for certain domains and now exclusively rely on AI agents. Sometimes, stopping old practices is necessary to adopt new ones—it can be challenging but highly effective.
We also emphasize the “human factor.” Tools are designed to empower humans, and we ensure our employees feel confident and supported by technology.
Senior management support and accountability are vital for adoption. While financial benefits may not be immediate, we are emphasizing shorter project cycles, faster delivery, and improved customer satisfaction. Consistent follow-ups on KPIs by senior management have proven effective.
Do you also see implications of AI on the organizational structure and leadership?
Yes, AI will impact both. AI is particularly suited for repetitive, transactional tasks, especially those requiring language processing and information retrieval. This prompts a reevaluation of organizational roles. In this sense, I definitely see an organizational impact on how work is done and by whom.
Leaders who drive AI adoption will succeed in the long run, as they reshape capabilities and embed AI into daily functions. Effective leadership also involves knowing when and where to use AI and when human judgment should take precedence. Encouraging openness, experimentation, and curiosity is crucial in this journey.
How have you engaged the board throughout this process?
We’ve engaged the executive committee and board through multiple sessions, and many board members are personally using AI, which has been helpful. The board brings diverse perspectives from other industries, fostering meaningful discussions on AI’s responsible use. Additionally, we have a scientific advisory board that includes an MIT expert specializing in data science and AI. Their insights have been particularly valuable for Science and Innovation.
