Lifelong Learning as Strategic Career Development

Katja Lammert served as Chief Investment Officer (CIO) Alternative Assets and Managing Director at MEAG until the end of 2025. She has since used her time deliberately for targeted professional development. In this interview, the long-standing member of the Fondsfrauen advisory board talks about her experience with continuing education.

She could have improved her golf handicap or gone on a world trip. Instead, Katja Lammert chose a high-caliber continuing education program, followed by a language course in Paris. "Continuing education sharpens your thinking, and you learn how to learn again," says Katja Lammert. She served as Chief Investment Officer (CIO) Alternative Assets and Managing Director at MEAG until the end of 2025. She has since used her time deliberately for targeted professional development and shares her experience in this interview. Katja Lammert serves on the Fondsfrauen advisory board.

Katja, why did you use this period of time for continuing education?

I wanted to use the phase after my time at MEAG purposefully, both for my professional development and for intellectual stimulation. I started looking early for programs that were genuinely relevant in terms of content and demanding at an international level. I was inspired in part by a conference focused on lifelong learning, where it became clear: there is simply rarely time for intensive continuing education during a regular working life, and I made a conscious decision to use that gap.

What continuing education options did you look at, and how did you find them?

I looked at various executive programs at renowned international universities and ultimately chose Stanford. What convinced me was the international character of the program, both in terms of content and the makeup of participants. Continuing education is ultimately an investment in your own expertise, and I consider this one of the most impactful investments you can make.

What was the topic of the program?

The program was titled "Digital Transformation: Leading in the Age of AI" and addressed one of the core strategic challenges companies face today. It was not just about technology in the narrow sense, but about how executives can actively shape digital change and bring their organizations along in the process. Key topics included the strategic use of AI at the leadership level and the question of how to engage teams and employees in this transformation, regardless of their prior experience or generational background.

Why did you choose AI as a topic? Why do you consider it relevant?

AI will significantly influence nearly all professional activities in the future. For me, the question is not whether this will happen, but when and how. Technological development cannot be stopped. Anyone in a leadership position must be able to understand, contextualize, and strategically apply this technology. It was important to me to develop this understanding in a targeted and structured way, as preparation for future roles where I can put this knowledge to immediate use.

What did the program cover concretely?

The program was deliberately broad in scope, covering technical fundamentals as well as strategic and cultural aspects of AI adoption. I found the discussions particularly valuable on how companies implement AI in their operations and what mindset is needed to move beyond reactive FOMO thinking and become an active shaper of change.

What impressed you most?

I was struck by how differently companies from various cultures and industries approach this topic, and with how much variation in success. Often, what makes the difference is not the technology itself, but how employees are empowered to take ownership and how an organization learns to operate under uncertainty.

One compelling example is Netflix. The company invested heavily in streaming at an early stage, even though the DVD rental business was still performing well at the time. That strategic willingness to give up a profitable present for a future-ready position is, for me, a textbook case of consistent transformation.

Does using AI also carry risks?

Yes, definitely. One serious risk is the gradual erosion of critical thinking. Those who use AI without reflection risk losing the habit of independent judgment. Large language models in particular tempt users to accept results rather than question them. For executives, the ability to think critically and assess outcomes remains indispensable.

What are the biggest challenges for companies when adopting AI?

The decisive foundation is data quality. Anyone who wants to achieve valid results with AI must first clarify: what problem needs to be solved? What data is available, and what data is needed? The standards of rigor vary considerably depending on the use case. In retail, an 80 percent solution may be sufficient. In medicine or complex investment decisions, incomplete or flawed data can have serious consequences. AI does not run itself. It is only as good as the foundation it works on.

Did you also discuss broader societal questions, such as whether AI-driven work should be taxed?

That topic was not explicitly on the agenda. I think it is important not to have these discussions too early or too one-sidedly. Regulatory barriers that delay the adoption of useful technologies can do more harm than good in the long run, particularly in an international competitive context.

What do you say to those who fear losing their jobs to AI?

That concern is understandable, but the answer is more nuanced than it is often portrayed. Some activities, particularly standardized knowledge work, will change fundamentally. At the same time, new roles will emerge. What remains indispensable? Judgment, strategic thinking, and leadership competence. One important caveat, however: if no junior talent enters foundational roles because those have been automated, the pipeline for future leadership positions will eventually run dry. That is a structural risk companies should take seriously.

What technical fundamentals did you cover?

The scope was broader than I expected. In addition to AI agents, what they can do, what data they require, and where their limits lie, we also covered robotics: where and how physical automation interacts meaningfully with AI-driven systems, and what implications that has for companies. We also looked at machine learning, the logic behind large language models, and the architecture of data-driven decision-making processes. For executives, the goal is not to understand the code, but to understand the underlying data model, to critically assess outcomes, and ultimately to take responsibility for how these technologies are deployed.

What process do you recommend for AI adoption in companies?

The key question at the start of any AI project should always be: what concrete value does this technology create for the business? Does it make processes better, more efficient? Does it open up new revenue potential? If you cannot answer that clearly, you should not take another step forward. That is followed by an analysis of the existing data, because without clean, complete data even the best AI model will fail to deliver. Only then does it make sense to survey the market for existing solutions and conduct an honest assessment of opportunities and risks: what do I gain, what do I risk, and is an 80 percent solution good enough, or do the requirements for precision and reliability demand more? Technology is not an end in itself. It has to be measured by results.

Where do you see the best applications for AI?

There is hardly an industry where AI cannot add value. The most impactful use cases right now involve repetitive, data-intensive processes. But even traditional trade businesses can benefit significantly, for example by optimizing spare parts logistics or route planning based on historical wear data. The prerequisite remains the same: a clean, reliable data foundation, which, incidentally, should be a business necessity even without AI.

Are there cases where AI adoption does not make sense?

Yes. When the effort required for data collection and system setup exceeds the achievable benefit, AI adoption simply has no economic logic. Technology is not an end in itself. It has to be measured by results.

Who else took part in the program with you?

It was a deliberately diverse group of around 40 participants from a wide range of industries and regions, many of them from Latin America. All had several years of leadership experience, as this was a genuine executive program explicitly designed for decision-makers. That diversity greatly enriched the discussions.

Are you still in touch?

Yes. We set up a shared group to carry the network into our professional lives going forward. The connection is deliberately kept open, a professional network that continues to grow organically.

In your view, is it good for people to take regular breaks from work?

For me personally, absolutely, and I am convinced it also makes me a better leader. Distance creates perspective, and learning outside of everyday routines sharpens your focus on what matters.

Is the cost of such a program worth it?

Investments in your own knowledge offer the highest return, in my view. The program was not cheap, but the insights and impulses I took away were worth it in every respect. And it does not always have to be Stanford. What matters is your own commitment to quality and relevance.

I can only recommend this kind of investment. And I am certain this will not be the last executive program I complete.

Thank you for this inspiring conversation, dear Katja. We look forward to talking again about your French course in Paris!

Fotos: Katja Lammert

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