AI adoption, AI enablement and human-centred AI transformation. Based in Berlin, working in English and German.

AI transformation is a human systems problem.

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.

Organisations are introducing copilots, agents and automation. The technology can work and still fail, because people don't understand it, don't trust it, don't know when to use it, or don't know what their own role is once parts of their work are automated. My work sits in that gap.

See how I work Bring me the messy AI problem

Jenny Simonds-Spellmann at her desk, smiling, chin resting on her hand, with a mug that reads 'Curiosity makes a better future', a notebook and a laptop. Hand-drawn circles for people, organisations and technology float behind her. — The human checkpoint in the middle of it all.

Human checkpoint
System or AI step
My path across the gaps

Where I work

AI transformation involves three groups of people. Most programmes only look at one.

Making AI

Product, technical and governance teams building or introducing AI.

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.

How I work across all three

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 detail

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

What else changes, and how the value shows up

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.

Jenny smiling beside a sandcastle with an 'AI Sandbox' flag and a cat peering over a wooden drawbridge. Her notebook links 'human judgements' to four questions: what can it do, which tools and systems, when is human approval needed, and what level of risk is OK.

The AI Sandbox Needs a Gate

Experimentation matters. So does deciding what should leave the sandbox.

Making AI real. 2 min read

Jenny cooking at a busy stove, sprinkling herbs into pots labelled AI agent prototypes (Cursor), AI literacy learning paths, workshops and enablement, user research, strategy and communication, governance and ethics, and iteration and testing. Her apron reads 'design, code, learn, strategise, stir, repeat', a chalkboard lists 'Today's AI menu', and a cat watches from beside a laptop.

My Role Is No Longer One Thing

And I'm starting to think that might be the point.

Human + AI, Making AI real. 2 min read

Jenny at a flipchart titled 'False negatives and my role'. A false positive panel shows a cat triggering an intruder alert; a false negative panel shows a burglar dismissed as 'probably the cat'. A third panel, 'My role: I connect the bigger picture', links model behaviour, data and context, security risk, human behaviour, decision design, and governance and ethics to human-centred AI.

When the AI Thinks the Burglar Is the Cat

Accuracy isn't enough. We need to understand which mistakes matter.

Trust + responsibility. 2 min read

Six 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.

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