In financial services since 2001 · Zurich, Switzerland
Enterprise AI Architecture & Transformation Leader
Turning AI ambition into governed, scalable business value.
StrategyArchitecturePlatformsGovernanceTransformation
Business, technology and transformation in financial services. Today the focus is turning AI ambition into enterprise capability — connecting strategy, architecture, platforms, governance and execution with a relentless focus on business value.
I do not treat AI as a technology question. The starting point is a business problem worth solving; technology that solves nothing is an expensive prototype with good slides. Every initiative has to earn its place against that test before it earns a budget.
From there the constraints are structural. Architecture has to make scale possible rather than block it. Governance has to make controlled innovation possible rather than function as a gate at the end. Platforms have to be orchestrated deliberately, not accumulated one vendor decision at a time.
And the organisation has to move with all of it — skills, processes, ways of working. A technical go-live changes nothing on its own. Deployment is not adoption, and adoption is not yet value, which is why cost and realised value have to stay transparent long after the launch announcement.
The field keeps moving. Agentic AI, new models, new vendors, new platforms: all of it has to be absorbed into the enterprise ecosystem without fragmenting it further. That is an architectural responsibility, and it is where a lot of AI landscapes quietly go wrong.
A lifecycle for turning AI opportunities into governed, scalable enterprise value. Not a project method — these are the questions an organisation has to answer before AI becomes something more than a promising demo.
A continuous loop — Discover follows Evolve. The highlighted segment follows your reading position.
Find the opportunities that matter.
Where can AI create meaningful value — and where is AI not the answer?
Separate potential from hype.
Which ideas deserve to become initiatives — and which deserve a clear no?
Outcome: a deliberate decision. Pursue · Explore · Park · Stop. Knowing when AI is the wrong instrument is part of the discipline.
Invest where value and strategic relevance meet.
The unit of decision is no longer the single use case but the whole portfolio. Outcome: investment decision and portfolio roadmap.
Turn the opportunity into an enterprise solution.
Not which AI tool — what is the right enterprise solution for this problem?
Answering that requires experience on both sides: internally built platforms and capabilities, and vendor-based solutions.
Design trust and control into the solution.
A phase and a constant. Governance begins at Discover and does not end at go-live — it runs horizontally through everything above and below it.
Move from concept to working capability.
Strategy and architecture must survive contact with delivery.
Turn deployment into changed behaviour.
Deployment is not adoption. Adoption is not value.
The organisation has to move with the technology: demystifying AI, building literacy, keeping human oversight where it belongs.
Turn successful solutions into enterprise capabilities.
One successful use case is not a capability. PoC → solution → reusable capability → enterprise scale.
Prove value. Control economics.
Are we creating the value we expected — and is that value worth the cost and complexity?
Technical performance is the easy half. This is where most AI portfolios stop measuring, and where the economics actually get decided.
Adapt without creating fragmentation.
Evolve the ecosystem without losing architectural coherence.
New agents, tools, models and vendor offerings must not accumulate into an uncontrolled AI landscape. Fragmentation management is an architectural responsibility, not a cleanup task.
The through-line
Making AI decidable.
Turning complex questions across AI, architecture, governance and technology into decisions business and leadership can understand and act on.
Not a skills list. These are the areas in which enterprise impact is created.
From enterprise AI ambition to architecture, governance, platforms and execution.
Shaping internally developed capabilities alongside strategic vendor ecosystems.
Identifying, qualifying, prioritising and accompanying AI initiatives throughout their lifecycle.
Creating the structures required to scale AI responsibly in a regulated environment.
Connecting AI investment, cost transparency and prioritisation to measurable business outcomes.
Demystifying AI, building literacy and skills, and enabling business adoption.
Today's AI role did not begin with generative AI. It rests on an unbroken run in financial services since 2001 — building systems, then data capabilities, then architecture, then transformation, and now enterprise AI leadership.
Five mandates, one objective: make AI a governed, scalable enterprise capability in a regulated financial-services environment.
Established Data & AI as strategic enterprise capabilities across all business units — setting the direction for enterprise AI adoption, governance and platform evolution, and aligning business, technology, risk and compliance behind it.
Built the platform foundations that later made enterprise AI possible: an event-driven architecture for scalable data integration and consumption, self-service provisioning, automation and standardised delivery — accompanied by the operating model needed to run it across more than ten stakeholder groups.
Cross-functional transformation across Wealth Management, digital channels and investment solutions — from business case to rollout — while introducing and scaling agile operating models across multiple organisational units.
Platform modernisation across digital banking, client onboarding and investment advisory — moving from legacy systems to a modern architecture, including a greenfield digital onboarding platform and an advisory solution.
Regulatory and product integration initiatives — compliant reporting solutions, process automation and operational efficiency on the core banking platform.
Core banking replacement programmes for financial institutions.
Migration projects, software implementation and new product development.
Where it started: building banking applications, data anonymisation and client reporting interfaces.
Where the depth actually sits.
Deep enough to define architectures, platform strategies and governance models across technologies and vendor ecosystems. Technology is evidence of competence, not the identity.
Lecturer and speaker across leading Swiss professional and higher-education institutions.