What Your Peers Are Already Doing

What Your Peers Are Already Doing

If you’re wondering whether “real” grant professionals use AI, the answer is yes — and they’ve been doing it longer than most people realize.

The Quiet Adoption

AI adoption among grant-seeking organizations hasn’t been loud. There’s no conference where everyone suddenly announced they were using ChatGPT. It happened quietly, one person at a time:

• A development director uses Claude to brainstorm needs statement language at 10pm
• A consultant pastes an RFP into ChatGPT to identify the key requirements quickly
• A grant writer asks AI to rewrite a paragraph in a more formal tone
• A program officer uses AI to summarize a stack of progress reports

Most of this happens without organizational knowledge or approval. That’s a problem we’ll address in Module 3, but the point here is: the adoption is already widespread, even in organizations that haven’t formally acknowledged it.

Where AI Is Working Well

Based on what’s emerging across grant-seeking organizations, AI is delivering real value in several areas:

Drafting and editing. This is the most common use. AI produces a first draft that a human refines. It’s faster than starting from a blank page and often surfaces language or framing the writer wouldn’t have considered.

Research and prospecting. AI can process large amounts of funder data and surface relevant matches faster than manual database searches. It’s particularly useful for identifying funders outside your usual networks.

Document review. Comparing a draft against an RFP’s requirements, checking for consistency across sections, flagging missing elements. AI is good at systematic comparison tasks.

Administrative tasks. Formatting budgets, generating reporting templates, summarizing meeting notes, drafting routine correspondence. Low-risk tasks where speed matters more than nuance.

Where People Are Getting Burned

The wins are real, but so are the failures:

Tone deafness. AI sometimes produces language that’s technically correct but culturally wrong — too corporate, too clinical, or condescending to the communities being served.

Privacy violations. Staff paste client data, beneficiary stories, or proprietary program details into tools that may store or train on that data. This is often unintentional.

Over-reliance. Some organizations hand entire proposals to AI and submit with minimal review. The proposals read like AI wrote them — because AI did. Funders notice.

The Pattern

The organizations getting the most from AI share a few characteristics:

1. They’ve acknowledged AI is already being used
2. They’ve created clear (even if simple) guidelines
3. They started with low-risk tasks and expanded gradually
4. They treat AI as a tool that requires human oversight, not a replacement for human judgment

That’s exactly the approach this track teaches.

### Next Lesson

You’ve seen the case for engagement and what peers are doing. Now let’s talk about the cost of waiting — what happens to organizations that delay.