Making AI
Product, technical and governance teams building or introducing AI.
Human–AI systems design: AI adoption, product and service design, and responsible AI. Based in Berlin, working in English and German.
The big picture,
built into the work.
People set the direction. Paved paths let people and agents move fast. Checkpoints keep it safe.
I'm Jenny Simonds-Spellmann. I work between the people building AI, the people expected to use it, and the people living with the results.
- Human checkpoint
- System or AI step
- My path across the gaps
What organisations say
I used to ask, “What should I do?” Now I can see AI compliance in my day-to-day work.
Where I work
AI transformation involves three groups of people. Most programmes only look at one.
Working with AI
Employees, managers and teams expected to bring AI into their everyday work.
Experiencing AI
Customers, citizens and users of services that increasingly contain AI.
What I bring to an AI programme
AI strategy only matters once it changes how work gets done. These four areas are how I get it there.
Strategy and use cases
I turn AI ambition into a roadmap: use cases assessed for value, data, risk and effort, with a clear call on what to build, buy, pilot or park.
In practice: How I evaluate AI use cases
Governance and risk
I translate the EU AI Act, data protection and information security into guidelines and decision aids people can use at the moment they matter.
In practice: Responsible AI and governance
Build and integrate
I prototype working AI myself and work with IT on how it fits existing cloud and on-premise systems, from first idea to rollout.
Enablement and learning
I advise leadership and teams, and design role-based learning that moves people from knowing AI exists to using it safely in their own work.
In practice: AI learning and enablement
Agentic AI, with the human kept in
Agents don't just answer; they plan, use tools and act. I help organisations decide how much autonomy each task should have, and build the safeguards that make it safe to use.
How I work
Audit, pathway, prototype, test, scale.
- AuditUnderstand what is actually happening.
- PathwayDesign how people get from here to there.
- PrototypeMake the future tangible.
- TestPut humans back into the system.
- ScaleTurn successful experiments into capability.
See the method in detailFollow one example through the method
What this changes
Organisations don't need another AI strategy. They need AI ambition to become changed work.
- From AI investmentto actual adoption
- From experimentationto repeatable practice
- From generic AI trainingto role-specific capability
- From AI use casesto working prototypes
Thinking
Notes from the messy middle of AI transformation.
AI adoption rarely fails because someone forgot to make another slide deck. The interesting problems appear when technology meets real people, real work, existing organisations and human judgement. These are some of the questions I'm exploring.
The big picture: speed with direction
Why AI needs systems thinking: paved paths, human judgement and checkpoints where they count.
8 notes on trust, agents, judgement and what changes when AI joins the work.
Explore all thinking →I help close the distance between AI possibility and human reality.
AI transformation succeeds when technology, people, workflows, learning, governance and experience start working as one system.
My role is to help organisations see that system, find where it's breaking, make possible futures tangible, and design practical pathways towards adoption.
The result isn't simply more AI. It's AI that people can understand, use, question, supervise and integrate into meaningful work.
Bring me the messy AI problem.
I'm most interested in the point where the technology exists, the strategy sounds convincing, and reality gets complicated.