How further training opens up real prospects
In this testimonial, Martin Marincel explains the strategic reasoning behind his decision Haufe Akademie complete three Future Job Classes at the Haufe Akademie —Machine Learning Engineer, AI Manager, and Business Automation Manager—and how these qualifications have specifically impacted his work as an IT consultant in the field of digital transformation.
“My goal was not only to understand AI conceptually, but also to be able to analyze it from a technical perspective and evaluate it reliably within a project context. Today, I am able to systematically integrate business requirements, data logic, and technical feasibility.”
Martin Marincel
IT Consultant at Bültel Fashion Group
Martin Marincel
Machine Learning Engineer
41 years
2025
IT Consultant at Bültel Fashion Group
“The three Future Jobs Classes gave me the full picture—from strategy to automation to technology. For me, that was the best decision I could have made to prepare myself for the future.”
In his professional practice, Martin observed that artificial intelligence should not be viewed in isolation as a purely technological issue, but rather has far-reaching implications for process architectures, decision-making models, and organizational structures. For him, therefore, the focus was less on the tools themselves and more on a systemic understanding of AI within the corporate context.
“I wanted to understand the structural framework of AI projects—from the data foundation and modeling all the way through to integration into existing system landscapes.”
Combining the three Future Jobs Classes was a deliberate strategic decision. The goal was to bring together perspectives on governance and management, process automation, and technical implementation expertise.
The structure of the programs allowed him to address the topics not in a fragmented manner, but within a coherent framework.
The Machine Learning Engineer Class was technically challenging and methodologically well-structured. Martin delved deeply into data preparation, model logic, training processes, and evaluation mechanisms.
“Getting started required a deep dive into programming logic, statistical fundamentals, and model architectures. For me, the focus was less on the actual coding and more on gaining a solid understanding of the underlying mechanisms, assumptions, and systemic limitations of data-driven models.”
By combining theory, practical application, and critical discussion, he developed a solid understanding of how data-driven systems work.
It became clear to him that sustainable AI projects can only succeed if strategic objectives, data quality, and technical feasibility are consistently aligned.
In his role as an IT consultant, Martin acts as a liaison between business units, process owners, and software development. The Future Job Classes have strengthened his ability to analyze requirements systematically, evaluate technical options realistically, and validate solution architectures at an early stage.
He makes strategic use of AI-powered tools and digital prototyping methods to visualize complex process logic, establish a basis for decision-making, and accelerate iterative coordination.
“Instead of describing concepts solely through documents, I can now develop robust prototypes in a short amount of time. This enables more informed feedback, reduces room for misinterpretation, and improves the quality of implementation.”
In addition, he helps departments gain a deeper understanding of technological concepts and translate ideas into practical applications.
For Martin, the key benefit lay in the integrated development of his skills. The training in AI management sharpened his understanding of governance, risk assessment, and strategic integration. The Business Automation Manager program focused on process design, efficiency potential, and operational scalability. The in-depth technical training in machine learning complemented these perspectives by providing implementation and evaluation skills.
“It is only when these three levels work together that it becomes clear just how closely strategy, process architecture, and technology are intertwined. Sustainable digital transformation does not occur in isolation, but within a systemic context.”
This integrated approach shapes his consulting methodology in transformation and digitalization projects today.
Martin views continuing education as a strategic tool for developing skills—especially in dynamic technological fields.
“What matters is not the formal degree, but the ability to critically analyze content and apply it effectively within one’s own area of responsibility. Those who are willing to engage with technical fundamentals in a structured and intensive manner will significantly expand their scope of action in a project environment.”
From Theory to Practice – Implementing AI Projects
The Machine Learning Engineer training program covers the entire lifecycle of AI projects—from data analysis and modeling to production deployment. You’ll learn how to develop practical machine learning models and implement them profitably within your organization.
