What the study analysed
The study, Sustaining Agility: How Agile Coaches Ensure Changes Endure, examined how agile coaches help organisations sustain change after their engagements end. It drew on 16 semi-structured interviews with professional agile coaches across six European countries and the United States, co-authored with The Open University and approved by the university’s ethics process.
Transcripts were analysed using Braun and Clarke’s six-phase inductive thematic analysis in NVivo 14. The publication describes Evidano as "an artificial intelligence driven qualitative analysis tool."
A supplementary check, not a replacement
In the study, transcripts were anonymised before upload, AI use was disclosed in the data analysis section, and the human research team made all final decisions about the themes. Where the AI overview overlapped with the manual structure, around building self-sustaining teams and managing post-exit relationships, it increased the team’s confidence in those patterns. Where it suggested additional groupings, the team revisited the data and judged them already captured in subthemes, so the final structure stood.
How Evidano fit the workflow
- 1Manual analysis first
The team coded all transcripts using inductive thematic analysis in NVivo 14.
- 2Anonymised upload
After manual analysis, anonymised transcripts were uploaded to Evidano.
- 3Independent overview
Evidano was configured to generate an independent thematic overview of the dataset.
- 4Systematic comparison
The Evidano themes were compared systematically with the manually developed themes.
- 5Researcher judgment
Overlaps increased confidence in the patterns. The team discussed differences and kept all final decisions.
“AILYZE was configured to generate an independent thematic overview of the dataset. The AILYZE-generated themes were then compared systematically with the manually developed themes.”
“In several instances, the tool surfaced clusters that overlapped with the existing thematic structure (e.g., around building self sustaining teams or managing post-exit relationships), which increased confidence in these patterns.”
“AILYZE thus served as a supplementary check and stimulus for reflexive discussion, rather than as a primary analytic tool.”
Triangulation, two ways
A second coder repeats analysis by hand. Cross-checking the full theme structure is slow and resource heavy.
An independent thematic overview is generated, then compared with your manual themes to corroborate the structure, with you in control.
Add an AI-generated thematic overview to cross-check your own coding.
Overlapping themes corroborate your structure, as reported in the study.
In the study, AI use was disclosed in the data analysis section.
A supplementary check that supports judgment rather than replacing it.