When ChatGPT launched a global corporate conflict in late 2022, Walid Mehanna was already busy rebuilding the digital backbone of the world’s oldest pharmaceutical company. As group data officer at Germany’s Merck KGaA (also known as Merck Group in the US and unrelated to US pharmaceutical company Merck) at the time, Mohanna spent two years overhauling the company’s data and analytics systems for its 62,000 employees. About eight months into the GPT era, his title was elevated to Chief Data Officer and AI Officer. The creation of the role “was an evolution by design, not a reaction to a development, hype or trend,” he said.
Merck KGaA’s roots go back to the 17th century, when pharmacist Friedrich Jacob Merck laid the foundation for the family business in Darmstadt, Germany. Today, its businesses include pharmaceuticals, medical equipment manufacturing and electronics. Leading an AI strategy within a 350-year-old company is a unique kind of challenge. Muhanna, who was born in Egypt, likens it to building a pyramid.
At the base are everyday AI tools that improve personal productivity across the workforce. The goal is to build digital fluency, save time, and reinvest those gains into growth. The middle tier – where the company is located today – is focused on integrating AI into core workflows, such as R&D, supply chains, and business operations over the next several years. At the top of the “pyramid” is the AI product, where machine learning becomes part of what the company sells, helping to accelerate drug discovery and innovation.
“Philosophically, AI at scale is not a technological challenge,” Mehanna said. “You still have to do your homework on the technology side, but the real transformation is about leadership.” This happens when strategy, culture and ambition come together with clear commitment.”
Choose a local startup partner
Merck KGaA moved early into generative AI by rolling out an internal platform called MyGPT to its employees in June 2023. The tool was initially built in-house, and gave employees a safe space to experiment. But Mohanna quickly realized the limits of relying on one setting.
Within a year, Merck KGaA replaced its original setup with a partnership with LangDock, a then-Berlin-based startup. At the time of their initial conversations, LangDock had four clients and about $50,000 in annual recurring revenue.
Choosing a young European company may seem political in today’s AI landscape. Mohanna says it wasn’t like that.
“The thinking behind the collaboration was about optionality, speed and sovereignty, not the sentimentality of working with a German startup,” he said. “LangDock gave us the ability to build something fully GDPR compliant that we could host in our own environment. We were able to work with the company to achieve enterprise-grade security while maintaining the agility of the startup.”
The setup adds a buffer between employees and key AI service providers, giving Merck flexibility as models evolve. “It was important for us to create a flexible, model-agnostic user layer, which gave us access to the best models in the world, regardless of who made them, without vendor lock-in,” Mohanna said.
Build AI-powered guardrails like Mercedes brakes
Deploying AI across a global workforce the size of Merck KGaA requires navigating stringent regulatory frameworks and business relationships, particularly in Europe. Mohanna chose to integrate governance into the strategy from the beginning, working closely with the company’s works council.
Since deploying the AI, Merck KGaA has internally generated more than 12 million claims, a goldmine of data containing insights into how employees work, what’s on their minds, and what they need AI to help with. Muhanna said the company carefully analyzes these claims on an aggregate level and never monitors individual employee activity.
“We learn from claims patterns, not from individual employees or their uses,” he said. “All uses are privacy protected, and we do not monitor individuals.”
Before joining Merck KGaA, Mehanna served as Chief Data Officer at Mercedes-Benz for five years. The ethics of AI are often compared to the brakes on a high-performance car: easy to overlook, but necessary.
“My point of view is that I want to have the best brakes in the world so I can drive fast,” Mohanna said. “Mercedes AMG, Porsche or Ferrari have some of the best brakes because they also go at very high speeds.” “Governance and ethics work the same way. I want to implement the best governance and ethics because the right barriers allow us to move quickly while maintaining safety and security.”
Putting this philosophy into practice, Merck KGaA has established a Digital Ethics Advisory Committee guided by five principles: autonomy, benevolence, non-maleficence, fairness and transparency. Rather than simply approving or rejecting projects, the committee pushes teams to identify risks early and design safeguards before launch.
“Our outside experts ask tough questions and make us think carefully about the consequences of potential actions. “We then identify the guardrails needed to move forward in a safe and compliant manner that aligns with our values,” Muhanna explained.
Why models are no longer the edge
Like many technology executives, Mohanna’s vision for the market has matured along with technology. It is no longer believed that the race of institutions will be determined at the level of the foundational model, where frontier models constantly jump over each other.
“Two years ago, I was fairly convinced that the race for artificial general intelligence or enterprise AI would be decided at the model level – and that whoever had the best model would win,” Mohanna admitted. “I’ve definitely changed my mind about that.”
“The models are still crucial, but they jump each other every few months. So, it’s not a permanent advantage,” he added. “I believe the most sustainable advantage lies in your context, your data, your processes, the fluency of your people, and most importantly, the trust you build with your organization and your customers.”
This shift has changed the way Merck KGaA builds its systems. The top-down semantic data layer across the entire company proved too slow. Instead, teams now integrate data incrementally — process by process and use case by use case.
Regarding the inevitable question of the impact of AI on jobs, Mohanna rejects the idea that AI will simply eliminate human roles. Instead, it rewrites the job requirements.
Muhanna said: “We do not see roles disappear, but rather we see roles evolving.” “AI is becoming a dynamic tool to accelerate and scale work. The leader of the future will also have to lead a hybrid workforce made up of people and agents.”
Thinking about leadership through industrial transformations, Mohanna points to an unexpected source: Arnold Schwarzenegger’s Management Handbook, Be Helpful: Seven Tools for Life. Across disparate careers in athletics, entertainment, and politics, the basic constant remains clear.
“The basic basic principle is to always be helpful, and that resonated deeply with me,” Mohanna said.
