AI Survey 2026 · L4WB Group
In April 2026, 40 of 42 colleagues across the L4WB Group (95%) 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.
The Current Picture
Q3How often do you use AI for work?
Q1What do you use AI for? (% of 40 respondents)
% of 40 respondents. Respondents could select multiple tasks.
ChatGPT leads, with four in five colleagues (82.5%) using it. Gemini (45.0%), Claude and Copilot (25.0% each) follow at some distance. Most colleagues use more than one tool. A handful use L4WB’s own AI tools: LitSearch and AutoMap.
Six Scenarios · Where Colleagues Stand
The survey presented six real-world AI use cases and asked colleagues to rate each: acceptable, acceptable with caveats, or not acceptable. Four produced broad agreement. Two produced genuinely split responses; these are the ones the AI policy needs to address directly.
Scenarios are ordered from most broadly accepted (top) to clearest “no” (bottom). The two with the most divided responses (flagged) show where colleagues hold different views and where a clear policy position is needed.
n=40 respondents. Hover over segments to see precise counts.
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.
The policy needs a clear position, with a worked example.
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.
The key question is not whether to use AI for translation, but whether it has been properly reviewed before sending.
The policy needs to confirm these as firm boundaries: they reflect what most colleagues already believe.
Risks We Recognise
Two concerns were raised equally: accuracy and over-reliance, each cited by 65% of colleagues. 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)
Top concerns (multi-select). Accuracy and over-reliance are tied at 65%.
Environmental impact was raised by 35% of respondents, reflecting a genuine awareness of AI’s energy footprint. L4WB’s dedicated Environmental Policy already commits the organisation to integrating environmental considerations across all operations and decision-making, including digital tools and services. AI use sits within that commitment: choosing tools thoughtfully, avoiding unnecessary use, and supporting the Foundation’s broader sustainability goals.
Q17Should staff disclose when AI was used in outputs?
60% say "in some cases": disclosure is context-dependent, not a blanket rule.
Disclosure is not only about external outputs. Sending AI-generated content to a colleague (a draft, a summary, a translation) also warrants a note. The policy defines when disclosure is required for both internal and external audiences, based on the extent of AI involvement and the context.
Draft Policy · Survey Findings
The survey found broad alignment with the positions already in the draft policy. It also surfaced points that needed clarifying or that the draft had not yet addressed.
On the core questions, survey responses and draft policy point in the same direction. The positions colleagues already hold on data protection, accuracy, and disclosure are the positions the policy takes. This report explains each in context.
What Colleagues Need
Three survey questions asked what colleagues need from a policy. The answers point in the same direction: practical over procedural, examples over rules, enabling over restrictive. Together they shaped specific choices in the draft policy.
Q18What support would help you use AI responsibly? (% of 40, multi-select)
The top ask: practical examples (70%). Written guidance and training (50% each). Approved tool list (47.5%).
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. Clarity in the policy removes this friction.
One response could cover multiple themes. Counts reflect the number of respondents who raised each theme.
Thematic analysis of Q20 open-ended text responses.
Verbatim responses from the open-ended survey question, some condensed for length.
Survey Into 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.
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.
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. Partner and funder requirements on AI use apply in addition to this policy and must be checked before beginning any AI-assisted work on funded projects.
Accuracy is the survey's joint top concern. AI generates plausible-sounding content that can be factually wrong. AI tools are also designed to be agreeable, which means they may confirm whatever the user suggests rather than correct it. Every AI contribution to a deliverable is reviewed by the person responsible for it.
The amount of disclosure required depends on a number of factors: the extent to which AI shaped the content, whether the output is going externally or to a colleague, the nature of the audience, and the sensitivity of the subject. Disclosure is not only about external deliverables: sending AI-generated work to a colleague also warrants a note. The policy sets clear criteria for each context.
70% of colleagues asked for examples of good practice. That is this policy's primary aim: giving staff the clarity and confidence to use AI effectively across all roles and contexts, not creating compliance anxiety.
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 remains essential alongside AI adoption.