AI writing tools aren’t new anymore. Most mission-driven leaders have had at least one encounter with them by now — a late-night deadline or a board member asking why you’re not using them yet. What hasn’t kept pace is organizational thinking: the norms, the policies, and the honest conversations about where AI belongs and where it doesn’t.
One of the calls that tends to go undiscussed is: when does someone need to know I used AI to make this?
It’s a fair question, and it deserves a more nuanced answer than “never” or “always.” Disclosures about AI use – or any other topic, for that matter – can help build trust with people when done with care and intention. You can leverage your AI use to build trust with your desired audience by understanding how to use it well and how to talk about using it.
The Ghostwriter Comparison (And Where It Breaks Down)
When AI writing tools popped up in everyone’s workflow, an AI “ghostwriter” became the go-to analogy. While it’s not entirely wrong, it isn’t right either. Ghostwriting has a long history: speechwriters, book collaborators, executive communications consultants. Nobody raises an eyebrow when an Executive Director’s year-end letter turns out to have had professional writing help.
The real concern is authenticity: do the words actually reflect the person or organization behind them? A good ghostwriter earns that trust — they interview you, study your history, understand your relationships, and work hard to sound like you on your best day. AI doesn’t do that by default. Without significant guidance, it sounds like a competent stranger who skimmed your website.
Two kinds of authenticity matter here:
Personal voice is about style. Does this sound like the human who supposedly wrote it? That’s real and worth tending to, but it’s recoverable. A reader who notices slightly flatter prose than usual is mildly disappointed. They’ll live.
Organizational voice is higher stakes. It’s about whether your institution’s values, relationships, and judgment are present in the communication. A donor who reads your year-end letter doesn’t just want it to sound like you wrote it. They want to feel that you thought carefully about what they needed to hear, that you made considered choices about what to share and what to acknowledge, that the organization they give to is genuinely behind the words. AI can approximate a voice. It cannot supply judgment it was never given.
This is why how much direction you give AI determines almost everything about the quality of what comes back. That upfront context-setting is how your organizational voice gets into the document.
A More Honest Spectrum of AI Involvement
Not all AI use carries the same authenticity question. The trust implications shift depending on how involved AI actually is in the work.
AI as thought partner: You bring the raw material — last year’s program highlights, your funder’s stated priorities, the question you’re trying to answer — and AI helps you think through what belongs where. You decide. You write. A grant writer using AI to figure out which program outcomes best match a specific proposal is using it as a thought partner. The judgment is yours throughout, and the authenticity question barely arises.
AI as editor and organizer: You write the brain dump, everything you know, in whatever order it comes out, and AI helps structure it, tighten the language, and find the through-line. Your voice goes in. A cleaner version comes out. This is probably the most common use of AI for external communications, because the judgment, context and content are genuinely yours.
AI as draft generator: AI produces a first version, and you revise from there. This only works if you’ve loaded AI with enough context, constraints, and direction upfront — which on a complex communication can take as much effort as writing a rough draft yourself. The revision has to be genuine and word-by-word. If your values and voice aren’t shaping the output, you’re not really in the driver’s seat and the authenticity of the final product depends on how diligently you revise.
AI as unsupervised intern: Minimal direction in, a quick glance before it goes out. You’re left with technically coherent output that probably doesn’t reflect your relationships or represent your organization’s position with any precision. This is where authenticity risk is highest, and where stakeholder trust is most exposed.
Some communications carry higher authenticity stakes than others — grant applications, major donor asks, public statements on difficult issues. In those cases, the reader isn’t just absorbing information; they’re evaluating whether your organization is the right steward of their trust or resources. AI can help you think through what to say. It can’t tell you what your organization actually believes.
Transparency Isn’t All-or-Nothing Either
The disclosure question is really an authenticity question in disguise: are you being authentic with your stakeholders about how the work gets made? Before you decide what to tell them, it helps to get clear on which kind of transparency you’re actually talking about.
Active disclosure means proactively telling your stakeholder you used AI — in the document, in conversation, or in your organizational policy.
Passive honesty means not hiding it if someone asks directly.
Ambient transparency means building a visible organizational practice so stakeholders know, generally, how your work gets made without a disclaimer on every email.
Most mission-driven leaders don’t need to attach an AI note to every donor communication. But operating without any practice at all — no policy, no disclosure, hoping no one asks — is unwise and short-sighted.
At Brevity & Wit, our approach is lightweight ambient transparency: AI-assisted pieces we publish carry a disclosure at the bottom. Publishing that disclosure says something more useful than “we used AI”: it tells readers our AI philosophy and how the work gets made. Over time, that kind of consistent practice earns trust more reliably than any single disclaimer would.
How you disclose matters too. “This was written by AI” and “This was drafted with AI assistance and reviewed and revised by our team” land very differently. The second indicates more responsible use and is more honest about where human judgment is incorporated.
So What Should You Actually Do?
When you’re not sure what transparency looks like for a specific piece of work, this chart can help you think it through:
| Audience | AI Role | Disclosure Approach |
| Funder / grantor | Thought partner on strategy; human-written prose | Active disclosure recommended |
| Major donor | Editor and organizer; heavy revision required | Passive honesty; org policy as baseline |
| Board | Thought partner or editor; human judgment on all conclusions | Note AI’s role if it synthesized data or analysis |
| General donors / community | Editor and organizer; voice must be yours | Ambient transparency at org level |
| Internal communications | Full range, lower stakes | Good judgment applies |
Across all of it, authenticity is the real standard: not whether you used AI, but whether your organizational judgment was present when you did. That’s what your stakeholders are trusting, whether they know it or not.
If you’re building an organizational AI practice from scratch — one that addresses transparency alongside data privacy, environmental impact, and bias — our Conscious AI Guide is a good place to start. And if you want to see what ambient transparency looks like in practice, take a look at the bottom of this post.
This article was written by Sage Eubanks with the help of claude.ai. As a small business, we thoughtfully engage artificial intelligence to improve our efficiency, while also using human experts and reviewers to ensure quality and mitigate bias.