I lead AI-driven business models at Swiss Post today: sizing the opportunity, scoping the build, rolling it out and proving it moved a number. More than a decade in product before that, much of it as a venture builder. This site is my application for AI Transformation Manager, Strategy & Operations at Property Finder, and I'm ready to move to Dubai.
The role is to own the AI transformation roadmap end to end: prioritise the use cases, scope them into real projects, ship products like conversational agents and AI-written property narratives, roll them out to agents, and report the adoption and ROI. That's the work I do now. Here's how it lines up, point by point.
I'm doing exactly this today: owning AI-driven business models from sizing the opportunity to a prioritised roadmap with clear metrics and timelines. I scope to the smallest thing that proves value, then scale what works.
Proof: AI Project Lead for business development at Swiss Post Advertising, building and running the AI roadmap and KPIs.
I'm a practitioner, not just a sponsor. I prompt, retrieve, ground and chain. I can sit with engineers and talk evaluation, hallucination, latency and cost as real constraints, and own quality and launch governance through to live.
Proof: build daily with Claude, GPT and n8n; built Pedal Peak end to end with AI workflows; the AI portfolio further down this page is mine.
I've taken products from MVP into the hands of real users and partners, built the enablement, run the testing phases and the feedback loops. For Property Finder that means rolling AI out to the agents who use it every day.
Proof: full go-to-market at Sparrow Ventures and WePractice; led agencies and rollout at ifolor; ran market pilots from MVP to launch at Die Mobiliar.
I define success up front and let the evidence decide, comfortable in analytics, SQL-level questions and A/B design. I track adoption, accuracy and ROI, and report the wins and the misses honestly to leadership.
Proof: +9% conversion and +15% checkout lift through research, A/B testing and analytics on a CHF 100M+ business at ifolor.
Being the connective layer across product, tech, data, commercial and compliance is the part I'm best at. I've led mixed teams and external partners to one outcome, owning the budget, the KPIs and the hard prioritisation calls.
Proof: led a cross-functional team and agencies at ifolor; founding team that grew WePractice to 23 people across 10 locations.
Much of my career has been turning ambiguity into a clear plan and selling it upward. I've made the case to C-level and investors, owned enterprise partnerships, and held the line on scope. Storytelling with data is how I move decisions.
Proof: owned UBS and Baloise partnerships at Brixel; reported to C-suite at ifolor; raised two funding rounds as a founding team.
AI and product lead in Zurich with over twenty years of experience, more than a decade in product, open to relocating to Dubai. I turn ambiguous problems into shipped products and measured results, increasingly with AI at the core. German and Swiss German native, English fluent, French conversational.
Jan 2026 to present
Swiss Post, Advertising · Zurich
Oct 2024 to Jul 2025
Ifolor Group · Zurich
Jun 2023 to Sep 2024
Brixel · Zurich
Mar 2020 to May 2023
WePractice · Sparrow Ventures (Migros Group) · Zurich
Sep 2019 to Sep 2022
Sparrow Ventures · Zurich
Jan 2017 to Aug 2019
Die Mobiliar · Bern
Not a slide of buzzwords. This is how I'd actually run the role: put the candidate AI use cases on an impact-versus-effort board, pick where to start, then scope each one into a real project with a metric, a timeline, a rollout to agents and an adoption target. Click a number on the board, or a use case in the list, to see the full treatment.
Four AI initiatives, scored on impact and effort. I'd open with the quick win that proves the model and the rollout, then earn the bigger bets. Prioritisation is the job.
Roughly how I'd spend my first three months as AI Transformation Manager at Property Finder: learn the ground truth, build the prioritised roadmap, and ship the first use case to real agents.