My background combines engineering, technical project ownership, international commercial leadership, end-to-end process work and AI management. The common thread: understand the technical reality, identify the economic benefit and shape change so that it works in daily practice.
Technology → customer value & business case → commercial & operations → digital & AI → a system landscape that holds.
I came from engineering, led technical development and customer projects as well as technical experts, and then moved consistently into commercial responsibility – up to leading international sales organisations including P&L accountability.
At the same time, the technical and operational side stayed an active part of my work: among other things through leading technical project management and shaping customer journeys, customer-to-order and order-to-cash processes. As a certified AI Manager (IHK), I add the structured assessment and responsible integration of artificial intelligence.
That experience is also where one clear conviction comes from: the usual route – choose a system first, then adapt the business to it – produces exactly the fragmentation it was meant to remove. Digitalisation only holds up when processes, roles and data definitions are clarified first, and when the result is one shared set of data instead of many isolated tools.
Engineering background, technical development and customer projects, and leadership of technical experts.
15+ years of international experience in sales, marketing and business development; leading international sales organisations up to VP level with P&L accountability.
Head of technical project management; hands-on design of customer journey, customer-to-order and order-to-cash processes.
Certified AI Manager (IHK); AI opportunity & readiness, data-driven processes, governance, risk and responsible use.
Strong customer orientation, international market development, partner and network building, and a consistent focus on the business case.
A structured approach, clear process thinking, unambiguous ownership and a focus on impact rather than concepts for their own sake.
Technology and industry determine what a process actually looks like. I know both from projects, not from presentations.
Roles at companies whose product is automation itself. That shapes how you judge what is worth automating – and what is not.
Industries with high demands on traceability, on-time delivery and quality – that is where you first see whether a process holds up.
Across every role in the value chain and every size of company – from the owner-managed supplier to the corporate division. This is the engine room of European industry: where things are developed, produced and delivered. And that is exactly where it is decided whether digitalisation holds up in daily work or only on paper.
For me, skilled trades are not a side field of industry but a focus in their own right – with a very direct link between process, people and outcome.
What draws me to the trades is the immediate closeness to real value creation. Improvements do not have to prove themselves in complex strategy models first, but in daily work: less time spent searching, less paperwork, faster workflows and more time for customers and skilled work.
Under skills shortages in particular, there is significant potential in using existing expertise more productively. Automation and modernisation can help reduce routine work, make knowledge more available and structure long-standing workflows so that a business can grow healthily even with limited resources.
This combination of technical practice, entrepreneurship and visible impact fits my own way of working: understand the real process first, identify the economic lever second, and only then apply the right technology.
In the trades you can see immediately whether a change makes work easier or merely adds complexity.
When skilled time is scarce, every unnecessary administrative task becomes a genuine barrier to growth.
Decisions sit close to the business. That allows pragmatic change with clear impact and short paths.
Digitalisation and AI have to be measured against concrete benefit – which is exactly the AMcelerate approach.
Placing technical feasibility alongside operational reality.
Putting the customer problem and the business case before the technology.
Not optimising the market side and the process side in isolation.
Applying automation and AI where they create impact – and steering the system landscape towards one shared set of data instead of multiplying isolated tools.
Then a conversation about your specific bottleneck is worth having – and about the order in which to tackle it.