Find the signal
Discover
Reveal unmet needs, behavioural patterns, and opportunities for growth.
AI Product & Experience Strategist
Designing for people Thinking in data Building for AI
Everything I do begins with human behaviour: I use research and data science to shape product strategy and design human-centred AI solutions.
What I offer
Sounds familiar
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Customers
“We have 100,000 customers and cannot say why some segments leave”
The data shows who leaves. It does not show the behaviour behind it.
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Product
“Feedback, analytics, feature requests, and still no clear priority”
Every source points somewhere different, and the loudest request tends to win.
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Operations
“Claims take weeks and nobody can say where the time goes”
Each team owns one piece of the process and nobody sees the whole. So it is unclear where AI agents would actually take manual work off people.
The data is there, the decision is not
Each of these starts the same way: with what your data and research already say
How it works
Every engagement starts with Discover. It begins with questions like why some segments leave, where claims lose time, or which opportunity comes first.
Gather the evidence
Customer data, research, feedback and process data: what you already have, brought into one place.
Find the patterns
Behavioural segments, what drives them, where work gets stuck. AI-assisted analysis, checked by people.
Size the opportunities
Where the value sits, ranked by impact, effort and risk.
Decide what comes next
A shortlist and a recommendation: act now, test it first, or change how the work runs.
Then, if the findings call for it
Simulate
When a decision is expensive to get wrong
Test the shortlisted options with synthetic segments grounded in your data, before you commit.
Transform
When the evidence points to changing how work runs
Redesign the workflow with people and AI agents, then pilot it and measure the change.
What you have afterwards
- Behaviour map
Segments and their drivers, or where the work loses time
- Opportunity shortlist
Ranked by value, effort and risk
- Recommendation
What to do first, and why
What this looks like in practice
Automotive
Synthetic Users for GEO
Starting situation
People now ask language models which car to buy. Customer research was abundant, but not in a form an agentic framework could use.
Result
An agentic framework that turns qualitative research into brand-neutral synthetic personas and a prompt catalogue by journey phase, used to audit brand visibility across AI engines.
Explore caseRetail
Feedback Intelligence
Starting situation
Thousands of customer reviews with praise and problems in the same sentence. Sentiment analysis flattened them to "neutral", and the fixable issues stayed buried.
Result
A single-pass GenAI decision layer that says what to fix, how urgent it is and who should act, on the same day.
Explore case
AI Product & Experience Strategist
Caro Milkez
During eight years at Accenture Song, I led experience and transformation work across automotive and enterprise products, and initiated research into synthetic users and agentic workflows.
Based in Munich, working with European and global clients. Happiest in the mountains, or with an espresso on the rocks.
- Industry experienceAccenture Song, OgilvyOne, OgilvyHealth spanning automotive and enterprise
- CertifiedCSPO Product Owner and Salesforce Business Analyst
- 15+ yearsProduct, UX and Design Leadership
- Data ScienceCombining machine learning and statistical analysis. MIT Professional Education
Ready to move from AI pilots to AI in operations?
When real value, redesigned work and the people who own it fit together, scaling stops being a leap of faith.
We check together whether a workflow assessment makes sense for your organisation.