AI Survey 2026  ·  L4WB Group

How is AI used
at L4WB?

In April 2026, 40 colleagues across the L4WB Group completed a survey on how they use artificial intelligence, what concerns them, and what they need from an AI policy. This is what they said.

Learning for Well-Being Institute

The Current Picture

AI is already part
of how we work

  • ›90% of colleagues use AI at least a few times a week; 45% use it daily.
  • ›Three tasks dominate: editing, translating, and drafting (70%+ each).
  • ›Usage extends well beyond writing: literature searches, data analysis, image generation, and code.

Q3How often do you use AI for work?

47.5%
A few times a week
19 of 40 colleagues
45.0%
Daily
18 of 40 colleagues
7.5%
A few times a month
3 of 40 colleagues

Q1What do you use AI for? (% of 40 respondents)

Editing, proofreading, rephrasing
30
75%
Translating text
29
72.5%
Drafting written content
28
70%
Summarising documents
20
50%
Brainstorming / planning
20
50%
Researching topics
18
45%
Generating images / media
15
37.5%
Literature searches
13
32.5%
Data analysis
12
30%
Writing / debugging code
6
15%

% of 40 respondents. Respondents could select multiple tasks.

Q2Which tools?

ChatGPT leads with four in five colleagues (82.5%) using it. Gemini (45%), Claude and Copilot (25% each) follow at some distance. Most colleagues use more than one tool. A handful use L4WB’s own AI tools: LitSearch and AutoMap.

Where We Agree · And Where We Differ

Where colleagues agree
and where they don't

The survey presented six real-world AI use cases. Two produced genuinely split responses: these are the areas where the policy must take an explicit position. Two others were rejected by large majorities, confirming where the firm boundaries lie.

Most divided responses

AI writes first draft of proposal rationale

You're drafting a project proposal and use an AI tool to write the first draft of the context and rationale section, which you then edit and refine.

Yes, fine
30% (12)
With caveats
45% (18)
Should not be done
17.5% (7)
Not sure
7.5% (3)

The policy needs a clear position, with a worked example.

Policy clarity needed

AI translation sent directly to a funder

You've written a report that will go to a funder. You use an AI tool to translate it into Spanish and send the translated version directly to the funder.

Yes, fine
2.5% (1)
With caveats
65% (26)
Should not be done
32.5% (13)

The key question is not whether to use AI for translation, but whether it has been properly reviewed before sending.

Where we are clear

  • Sharing a confidential partner report with a public AI tool: 82.5% say no
  • Pulling claims from AI summaries without reading original papers: 70% say no

The policy needs to confirm these as firm boundaries: they reflect what most colleagues already believe.

Risks We Recognise

What concerns
our colleagues

Two concerns were equally raised: accuracy (65%) and over-reliance (65%). They are two sides of the same risk. When output is not checked, both accuracy and skill development suffer.

Data protection and copyright are both at 50%, marking a clear expectation that the policy will set firm boundaries on what can and cannot be shared with AI tools.

Q16Biggest concerns about AI use at L4WB (% of 40 respondents, multi-select)

Accuracy or quality of responses
26
65%
Over-reliance on AI
26
65%
Data protection and privacy
20
50%
Copyright or intellectual property
20
50%
Decrease of technical skills
19
47.5%
Environmental impact
14
35%

Top concerns (multi-select). Accuracy and over-reliance are tied at 65%.

Q17Should staff disclose when AI was used in outputs?

In some cases
24
60%
Rarely
6
15%
Always
5
12.5%
Not sure
4
10%
Never
1
2.5%

60% say “in some cases”: disclosure is context-dependent, not a blanket rule.

What this means

  • Accuracy and over-reliance (65%): unchecked AI output harms both quality and skill development. AI tends to confirm what you want to hear, making independent verification essential.
  • Data protection and copyright (50%): colleagues expect firm boundaries on what can be shared with AI tools.
  • Environmental impact (35%): AI carries an energy footprint. This sits within L4WB’s Environmental Policy commitments.
  • Disclosure is context-dependent: 60% say “in some cases”, not as a blanket rule. This applies internally too: AI-generated work shared with a colleague should be flagged.

Draft Policy · Survey Findings

How the survey aligns
with the AI policy draft

The survey found broad alignment with the positions already in the draft policy. It also raised points that needed clarifying or that the draft had not yet addressed.

Where the survey aligns with the draft policy

  • 82.5% rejected sharing confidential partner drafts with commercial AI tools, a firm boundary the draft already takes.
  • 70% said using unverified AI claims in outputs should not be done, consistent with the draft’s verification requirements.
  • 60% said disclosure should apply “in some cases” rather than as a blanket rule, matching the draft’s context-dependent transparency approach.
  • The tasks colleagues already use AI for most (editing, translating, drafting, summarising) mirror the list of encouraged uses in the draft.

Points the survey raised that the draft policy needs to address

  • Over-reliance on AI tied with accuracy at 65% as the top concern: the draft does not yet name this as a principle and needs to.
  • Internal disclosure is a gap: most colleagues think of disclosure in terms of external outputs, but the survey shows that sharing AI-generated work with a colleague also deserves transparency.
  • AI-assisted translation for external audiences raised strong reservations (65% “with caveats”, 32.5% “no”): the policy needs to fill this with a worked example, not just a general position.
  • An enabling tone is not yet explicit in the draft: 70% asked for practical examples over rules, and 5 of 27 open-ended responses specifically asked for enabling rather than restrictive language. This should shape how the policy is written throughout.

What Colleagues Need

Wants, needs, and
what shaped the policy

What colleagues said they need from a policy was consistent: show us examples, not just rules, and make it something that helps rather than restricts. Those answers fed directly into the draft policy.

Q20Open-ended responses

What colleagues said they need: in their own words

27 of 40 responded

One response could cover multiple themes. % of 27 open-ended respondents who raised each theme.

Practical guidance: when and how
9
33%
Human accountability and oversight
8
30%
Data protection and privacy
5
19%
Enabling, not restrictive policy
5
19%
Ethical and responsible use
4
15%
Training and skill development
3
11%
Clear list of approved tools
3
11%
Transparency and disclosure
2
7%

Thematic analysis of Q20 open-ended responses (27 of 40 colleagues responded).

Q19What colleagues haven’t tried yet

The clarity in the policy removes this friction. Several colleagues mentioned tasks they would like to use AI for but held back from, not because they thought it was wrong, but because they were uncertain what was permitted. These include: systematic proposal writing with organisational materials, video production for project communications, and data analysis for non-research roles.

In their own words

Verbatim responses from the open-ended survey question, some condensed for length.

The policy should be enabling rather than restrictive, encouraging thoughtful and widespread use of AI across the organisation. It should provide practical, day-to-day guidance, including common use cases such as translation, meeting summaries, pedagogical writing, content development, and basic design support. At the same time, it should clearly define boundaries around sensitive and confidential information.
The policy should support a balanced and thoughtful use of AI, one that recognises its value as a tool, but keeps human judgement, responsibility, and authentic voice at the centre. AI should not replace our thinking, creativity, or relationships, but rather support them.
I think AI is really a tool that makes me a more efficient worker but I do feel stress sometimes over how I use AI and whether it is aligned with how I should be. Trainings on how we can use AI for improved efficiency would really help.

Survey Into Policy

Six positions that will
shape the AI policy

Based on what colleagues told us, these are the positions the L4WB AI Policy will take. Several were already under consideration in the draft; the survey confirmed, clarified, and in some cases strengthened them.

1
In draft policy

Accountable by default

The staff member owns every output. AI assists; it does not decide, author, or bear accountability. Using AI for a task you cannot verify is not a shortcut. It is a risk.

2
In draft policy

Sensitive data stays protected

Sensitive information about children, families, partner organisations, and beneficiaries does not enter public AI tools. This is a firm boundary in the policy, not a guideline.

3
In draft policy

Everything AI produces gets checked

Accuracy is the survey’s joint top concern. AI generates plausible-sounding content that can be factually wrong, and critically, AI tools are built to be agreeable: they validate what you seem to want to hear rather than push back. This makes independent verification essential. Every AI contribution to a deliverable is reviewed by the person responsible for it.

4
Survey clarified

Disclosure is proportionate, not blanket

How much disclosure is needed depends on a few things: how much AI shaped the content, whether it’s going externally or to a colleague, who the audience is, and how sensitive the subject is. This isn’t only about external deliverables: if you send AI-generated work to a colleague, that should come with a note too. The policy sets clear criteria for each context.

5
Survey clarified

The policy enables, it does not restrict

70% of colleagues asked for examples of good practice. That is this policy's primary aim: giving colleagues the clarity and confidence to use AI effectively across all roles and contexts, not creating compliance anxiety.

6
Survey clarified

We grow alongside AI, not dependent on it

Over-reliance is the co-equal top concern in the survey. The policy names this risk explicitly. Good judgement is not replaced by a prompt, and professional skill development still matters as AI becomes part of how we work.

This is a living document. It will be reviewed and updated as AI tools develop and as our experience with them grows. Read the current draft AI Policy.