14 interviews, analysed anonymously, change personas derived

Request: „Analyse the 14 interviews anonymously: patterns in ways of working, role models and acceptance — and build change personas from them.“

1. The AI anonymises the interview notes before analysing them
2. It codes statements along the focus areas: ways of working, role models, responsibilities, acceptance, team structures
3. A reusable template spans a matrix of seven roles and five dimensions; findings per role are condensed into an ADKAR heatmap
4. It condenses the patterns into change personas and names the change need per persona
5. It proposes measures per persona for the team to prioritise — in the first pass five key insights and ten measures prioritised by impact, with a change roadmap

Interview notes are usually coded by hand: one person, several weeks — and anonymisation is a separate step before that.

People conduct the interviews; which measures to pursue is agreed in the team — still being decided.

Any transformation that runs interviews or surveys. Anonymising before analysis makes candid answers usable — and the personas give the change a face without a name.

Change starts with listening. For the transformation the team ran 14 interviews across roles — on ways of working, role models, responsibilities, acceptance and team structures. The analysis was agentic and anonymous: the AI first strips personal references, then codes the statements and condenses them into change personas, each with its own change need. From these come proposed measures — in the first pass five key insights and ten prioritised measures; which ones to pursue is being decided now. One insight stands out: AI shifts work from producing to providing context and reviewing. Anonymisation is also method: patterns stand out more clearly once nobody is looking for the sender. What is missing is the measurement — we did not time how much faster it was.