I’ve been writing grant applications and business proposals for a long time. I’ve been a consultant helping small businesses and nonprofits compete for state and federal funding, a grant manager assisting neuroscience professors with their large-scale grant proposals and reporting, and a proposal writer embedded in a commercial business development team. Consistent among all of these roles was a sense of too much to do, too little time, and inadequate tooling to help.
Naturally, as AI tools became more advanced, I wondered whether they would make a substantial difference in the nonprofit grant-writing sphere. After spending the last several months neck-deep in these tools, I have thoughts.
The short version: AI is genuinely useful for grant writing, but not in the ways most people assume. It’s a process tool, not an intelligence tool. It can help you do the work faster and better once you know what work to do. It cannot tell you what work to do. To complicate matters, it isn’t self-aware enough to know what it can help you with and what it cannot — that’s on you.
Where AI can help
Writing and rewriting. AI is a strong drafting partner for the narrative sections of a grant application — project descriptions, organizational capacity statements, statements of need. If you need to reframe existing language for a new funder, or if you want a second opinion on whether your argument is landing, AI is there to help. It won’t write a compelling grant for you out of thin air, but it will help you think through what you’re trying to say and get it onto the page faster. When using AI, you never have to start with a blank page, and even if it did only that, that would absolutely be something.
Synthesizing source material. This is where I’ve found AI most genuinely impressive. You can feed it your organization’s website, your most recent annual report, a prior grant narrative, and your strategic plan, and ask it to pull out the most relevant language for a specific application, and it is more than adequate. What used to take hours of hunting through documents can happen in minutes. For small organizations that have accumulated years of materials but don’t have the staff bandwidth to mine them, this is significant.
Reading and summarizing RFPs. Grant guidelines are often long, dense, and written in the kind of language that requires three reads to fully absorb. AI is good at extracting the key requirements, eligibility criteria, and submission instructions from a complex RFP and presenting them in plain language. With AI help, you can build quick-reference checklists from lengthy guidelines, which makes the whole application process easier to manage.
Budget and detail work. Budgets, workplans, timelines, logic models — the heavily formatted, detail-intensive deliverables that eat up hours — are areas where AI can take a real load off. It won’t know your numbers, but it can build the scaffolding quickly and help you think through what you’ve missed. It’s also aces at nitpicky little formatting quirks, which have always been my very least favorite element of the grant preparation process.
Project management. Grant applications are projects, and AI is useful for the project management side: building out a timeline working backward from a deadline, drafting a workplan, thinking through roles and responsibilities. If you’re managing multiple applications simultaneously, having a thought partner for the logistics piece is genuinely helpful. While I’d still recommend using an actual project management tool (Trello, Asana, Monday, etc.) for proposals with multiple team members contributing, your AI assistant can provide that structure pretty easily for a single-player team.
Where AI can’t help, and might even hurt
Finding opportunities. Do not ask AI to find you grant opportunities. The results will be a mix of outdated, hallucinated, and irrelevant. AI doesn’t have reliable, current information about what foundations are funding, what their priorities are right now, or whether their deadlines have passed. For opportunity identification, you still need a real database — whether it’s Candid, your state’s nonprofit association, your own network, or funder websites. Finding relevant opportunities on the internet at large is not a task AI can do well, and trusting it here is a real risk.
Knowing whether an opportunity is worth pursuing. Go/no-go decisions require judgment that AI doesn’t have: knowledge of your organization’s relationships with funders, your track record, whether the fit is real or superficial, what your leadership has the capacity to actually deliver. These calls still belong to humans. I cannot stress this enough: do not let AI make your decisions. It will be wrong, and it could be catastrophic.
The bigger picture
For well-resourced development offices, AI is a productivity tool. It’s useful, but not transformative. For under-resourced organizations, particularly small nonprofits without dedicated development staff, it’s something more interesting than that. The parts of grant writing that have historically required the most time and skill — synthesizing organizational materials, drafting narratives, managing the process — are exactly the parts where AI helps most. That doesn’t mean anyone can write a winning grant with no experience or strategy, but it does mean that the barrier to producing a credible, well-organized application is lower than it used to be. For organizations that have been leaving funding on the table because they didn’t have the capacity to apply, that matters. At a time when everybody in the nonprofit world is struggling to do more with less, I encourage limited, sensible use of low-cost or free AI tools to pad staff capacity. It is worth the (relatively modest) time investment necessary to learn to use these tools in this way. Save precious staff capacity for decision-making and direction; let AI do the scut work.