Global enterprise transformation
Led consolidation of more than 90 Salesforce instances into 10 strategic platforms at Johnson Controls.
AI Strategy & Technical Leadership
AI adoption without theatre
I help organisations turn AI from scattered experiments and unused licences into practical workflows, better decisions and technically sane implementation.
I can take responsibility for AI direction and stay involved through adoption and delivery. Engagements range from a focused diagnostic to longer-term consulting; I also welcome permanent AI leadership opportunities.
Practical AI adoption sits between strategy, systems and people.
AI strategy
Workflow design
RAG and APIs
Fractional AI leadership
Executive enablement
Implementation review
Experience behind the advice
Built across enterprise transformation, product leadership and customer solutions
Regulated product leadership
Led product and technology at LiUU, managing five programmers and a designer within a team of 16.
C-level solution design
Led discovery, demonstrations and enterprise opportunities typically worth USD 30k–50k at Frontify.
Where AI initiatives need direction
The next step is often connecting existing tools and experiments to a clear business purpose, a workable process and someone responsible for delivery.
Tools are available, but the priority workflows are unclear.
Teams need guidance on appropriate uses and data boundaries.
Pilots need a clear owner and a route into everyday operations.
Proposed use cases need a realistic assessment of value and effort.
Outputs need quality checks and a clear review process.
Leaders need evidence to decide what to continue, improve or stop.
My role is to connect those decisions to practical implementation and adoption.
Where I can help
AI adoption reality checks
Assess current AI use, identify blockers, and separate useful opportunities from expensive distractions.
Use-case triage
Review proposed AI ideas and decide what to pilot, what to park, and what to kill before it wastes budget.
Workflow and tool design
Design AI workflows around real business processes, not around whatever tool is fashionable this quarter.
Technical feasibility
Evaluate whether an AI idea can actually work safely, affordably and reliably in production.
Executive AI fluency
Help leadership teams understand capabilities, limitations and trade-offs well enough to make informed decisions.
Implementation support
Support practical implementation across RAG, APIs, internal tools, SaaS workflows, automations and AI-assisted products.
Better judgement first. Automation second.
AI should improve thinking, not replace it blindly. Good advisory work starts with the workflow, the people involved, the decision being made, and the cost of being wrong.
Not every task should be automated.
The right model matters.
Cost, privacy, security and maintainability matter.
Human adoption matters as much as technical capability.
Good AI work starts by understanding the actual workflow.
The question is not whether AI can do the task. The question is what happens when it does the task badly.
Ways to work with me
AI Adoption Reality Check
A short diagnostic for teams that have started using AI but are not seeing meaningful results.
Includes
- • leadership and team interviews
- • current tool review
- • use-case assessment
- • risk and feasibility mapping
- • 90-day roadmap
AI Use-Case Clinic
A focused session to evaluate proposed AI ideas and decide which ones deserve time, budget and technical effort.
Includes
- • use-case scoring
- • risk review
- • automation suitability
- • model/tool fit
- • pilot recommendations
Ongoing AI Leadership
Take responsibility for direction, coordinate specialists and the technical team, and stay involved through implementation and adoption. Available through consulting, including longer-term mandates.
Includes
- • AI priorities and delivery planning
- • architecture and technical decisions
- • implementation and team coordination
- • stakeholder alignment and rollout
- • ownership, user feedback and improvement
How I work
Good AI advisory starts with the actual work: the people involved, the decision being made, the systems already in place, and the cost of being wrong. Tools come after that.
1
Initial context call
Understand the organisation, pressure points, current tools, and what leadership is trying to achieve.
2
Workflow and use-case review
Map where AI might help, where it creates risk, and where the current process is unclear.
3
Feasibility and risk assessment
Review data access, privacy, model and tool fit, cost, integration complexity, and failure modes.
4
Practical roadmap
Produce a focused 30, 60, and 90-day plan with recommended pilots, parked ideas, and next steps.
5
Implementation support
Help review architecture, vendors, prompts, workflows, prototypes, internal rollout, or production implementation.
90-day adoption roadmap
A practical 90-day AI adoption checklist
Use the first 90 days to move from scattered AI activity to evidence, governance and workflows people can actually use.
Days 1-30
Diagnose and decide
- Inventory tools, licences, experiments and shadow usage.
- Score use cases by value, risk, data sensitivity and feasibility.
- Pick one or two low-risk pilots with clear business value.
Baseline, risk map and pilot shortlist.
Days 31-60
Pilot and govern
- Design pilot workflows around real users and decisions.
- Define success metrics before rollout.
- Test privacy, security, human review and failure cases.
Pilot evidence and implementation decision.
Days 61-90
Operationalise
- Turn successful pilots into repeatable workflows.
- Assign owners for monitoring, updates and escalation.
- Plan the next quarter from evidence, not hype.
Operating model and next use-case backlog.
Pricing guidance
Most AI advisory engagements start from CHF 1'500.
Focused use-case clinics are suitable when you need to decide whether an AI idea is worth pursuing. Broader AI adoption reality checks typically start from CHF 4'500 and include discovery, use-case triage, risk and feasibility review, and a practical 90-day roadmap.
Ongoing fractional AI or CTO advisory can be arranged hourly or as a monthly retainer.
After an introductory call, I provide a clear proposal with scope, deliverables and pricing.
Questions before we talk
The useful questions usually come before anyone writes code or buys another licence.
Do you only advise, or can you also help implement?
Both. Some engagements end with a decision roadmap. Others continue into workflow design, prototype review, architecture support, vendor selection, or hands-on implementation guidance.
Is this useful if we already use ChatGPT, Claude, Gemini, or Copilot?
Yes. Existing licences are often the starting point. The work is to decide where those tools genuinely help, where they create risk, and where a custom workflow or integration would be more useful.
How do you handle sensitive data and privacy?
Data privacy is part of the feasibility review from the beginning. We look at what data is involved, where it can be processed, which tools are acceptable, and where human review or stricter controls are needed.
What do we receive after an AI Adoption Reality Check?
You receive a practical assessment of current use, recommended and rejected use cases, key risks, feasibility notes, and a focused 90-day roadmap.
How long does an engagement usually take?
A focused use-case clinic can usually be handled quickly. A broader adoption reality check normally takes a few weeks, depending on stakeholder availability and the amount of workflow discovery required.
Can you work with our internal technical team?
Yes. I can work with leadership on priorities and with technical teams on architecture, integration, model selection, implementation tradeoffs, and review of AI-generated code or workflows.
What if AI is not the right answer?
Then that should be said clearly. A useful engagement may recommend improving the process, data, training, or tooling before introducing AI.
Do you recommend specific vendors or models?
When useful, yes. Recommendations depend on the workflow, data sensitivity, cost profile, integration needs, and the level of reliability required.
Best fit
The mandate matters more than company size: a defined problem, access to the people doing the work and a commitment to putting useful outcomes into practice.
Swiss and European SMEs
SaaS and technology companies
healthcare, pharma, medtech and regulated teams
leadership teams with AI pressure but no clear plan
organisations using ChatGPT, Claude, Gemini, Copilot or custom AI systems
teams that need someone who can talk to both executives and developers
The strongest engagements give strategy, adoption and implementation a clear owner.
Why work with me
I combine business, sales, product and technical experience. That means I can help leadership teams make better decisions while still being useful to the people who have to build, integrate and maintain the systems.
I have worked across enterprise CRM, SaaS, AI systems, regulated contexts and implementation-heavy projects.
I can speak to leadership without drowning them in jargon.
I stay close to architecture, implementation and the people building the system.
I understand both the promise and the failure modes of AI.
I use practical outcomes and user feedback to guide the work.
Discuss your AI priorities
Tell me what you are trying to achieve, what tools you already use, and where the friction is. I will help you work out whether AI is the right answer, and what to do next if it is.
Discuss an AI mandate