Cohort 1 · 8 organisations · 31 employees surveyed

What 8 impact organisations taught us about AI adoption

Before the first mentorship session, we asked every organisation in our cohort where AI meets their daily work. Eight organisations is a small sample, so read everything below as a promising signal, not proof.

8/8organisations name privacy as a top concern
5/8lose hours every week to manual data entry and duplicate systems
#1confidence builder: “knowing how my data is handled”
Where the friction is

Eight themes kept coming up

How many of the 8 organisations raised each theme in their intake and early mentorship sessions. A theme only counts when an organisation named it explicitly.

Manual data entry & duplicate systems5 of 8
Repetitive writing at scale4 of 8
Screening, triage & intake4 of 8
Literature & research synthesis4 of 8
Knowledge management & findability3 of 8
Reuse-with-variation (same content, many formats)3 of 8
Meeting transcription & synthesis3 of 8
Translation3 of 8
What we’ve learned

Honest observations from the first weeks

01

AI gets you to 90%. The last 10% is still handwork

Drafts, summaries and translations arrive fast. Making them right for your audience still takes a person who knows the work.

02

The blocker is rarely the technology

Tools are the easy part. Time, ownership and permission to experiment are what actually decide whether anything sticks.

03

Trust starts with data, not features

Teams engaged only after they understood exactly what happens to their data. Skipping that conversation costs you the whole programme.

04

Small and boring beats ambitious and abandoned

One working tool for a weekly chore outperforms a grand roadmap. Every organisation that shipped something small kept using it.

Methodology

Tallied from intake surveys, mentor briefings and session notes across the 8 organisations of cohort 1, plus an employee survey (n=31), conducted before and during the first mentorship weeks. n=8 at organisation level: a promising signal, not proof yet. All responses are anonymised; no individual organisation’s answers are identifiable in the data above.

Recognise your organisation in these findings?

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