Can AI Write a Grant Proposal? What Works (and What Doesn't) in 2026

June 27, 2025 · 10 min read

Jared Klein

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The question comes up in every grant writing workshop, every nonprofit board meeting, and every research lab where a PI stares at a blank specific aims page: can AI write a grant proposal?

The answer is more nuanced than the marketing copy from AI companies would suggest, and more optimistic than the skeptics allow. After spending the past two years testing AI tools on real grant proposals -- federal, foundation, SBIR, NIH, NSF, USDA, and everything in between -- here is what I have found.

What AI Does Well in Grant Writing

AI language models have genuine strengths that translate directly to grant writing tasks. These are not hypothetical capabilities; they are things I use AI for on nearly every proposal I write.

Structural Scaffolding

The hardest part of writing a proposal is often the blank page. AI excels at generating structural frameworks that give you a starting point. Feed it a NOFO or solicitation, and it can identify the required sections, suggest an organizational structure, and produce an outline that maps to the funder's evaluation criteria.

This is not trivial. A first-time grant writer might spend days figuring out how to structure an NIH R01 research strategy or how to organize a response to a complex DOD SBIR topic. AI compresses that orientation phase from days to minutes.

First Drafts of Boilerplate Sections

Every grant proposal has sections that require competent, accurate prose but do not demand original thinking. Organizational background descriptions, facilities and equipment sections, data management plans, protection of human subjects statements, and biographical sketch formatting are all areas where AI produces serviceable first drafts that need only light editing.

These sections still matter -- a sloppy facilities section signals carelessness -- but the intellectual heavy lifting is minimal. Delegating them to AI frees human attention for the sections that actually determine whether you get funded.

Compliance Checking

One of the most time-consuming aspects of federal grant writing is ensuring that your proposal addresses every requirement in the NOFO. Miss a required section, exceed a page limit, or fail to address a scoring criterion, and your proposal may be returned without review.

AI can systematically compare your draft against the solicitation requirements and flag gaps. This is a task that humans do poorly because it requires sustained attention to detail across dozens of pages of regulations and instructions. Machines do not get fatigued or overlook the requirement buried in paragraph 4.3.2 of the NOFO.

Editing and Revision

AI is a remarkably effective editor. It can tighten prose, improve clarity, identify inconsistencies between sections, check that budget figures match the narrative, and suggest stronger transitions. I routinely paste draft sections into an AI tool and ask it to identify weaknesses from a reviewer's perspective. The feedback is often as useful as what I would get from a human colleague.

Synthesizing Background Information

Literature reviews, needs assessments, and background sections require synthesizing information from multiple sources into a coherent narrative. AI handles this well, particularly when you provide it with the specific sources you want cited. It can organize findings thematically, identify patterns across studies, and produce clear summaries of complex research landscapes.

Budget Calculations and Justifications

Budget development involves arithmetic, formatting, and narrative justification -- all areas where AI adds value. It can calculate fringe benefit rates, apply indirect cost rates, check that line items sum correctly, and generate budget justification language that explains each cost.

What AI Struggles With

The limitations of AI in grant writing are not trivial, and ignoring them will result in proposals that sound polished but fail to compete. Here is where the technology falls short.

Institutional Knowledge

The most compelling grant proposals draw on deep knowledge of the applicant organization -- its history, its track record, its relationships, its culture, its specific capabilities. AI does not know that your nonprofit's executive director spent 15 years at the state health department and has personal relationships with every county health officer in the region. It does not know that your lab's equipment was upgraded last year or that your preliminary data from a pilot study directly addresses the feasibility concern a reviewer would raise.

This institutional knowledge is what separates a competitive proposal from a generic one. AI can help you articulate what you know, but it cannot know it for you.

Relationship Context

Grant writing is embedded in relationships -- with program officers, with collaborators, with community partners, with the funder's institutional priorities. A seasoned grant writer knows that a particular NIH program officer values translational potential over basic science discovery, or that a specific foundation program director has shifted their focus from direct service to systems change.

AI has no access to this relationship context. It cannot attend a pre-application webinar and pick up on the subtle cues about what the funder is really looking for. It cannot read between the lines of reviewer feedback from a previous submission.

Novel Research Framing

For research grants -- NIH, NSF, DOD basic research -- the most critical element is how you frame your innovation. What makes your approach different? Why will it succeed where previous efforts have not? What is the conceptual advance?

This framing requires deep domain expertise and creative thinking about how your work fits into the broader scientific landscape. AI can help you articulate a research narrative once you have the intellectual framework, but it cannot generate the framework itself. The innovation in an R01 proposal comes from the investigator's unique insight, not from a language model's synthesis of published literature.

Understanding What Reviewers Actually Care About

AI can read the published scoring criteria, but experienced grant writers know that the published criteria only tell part of the story. At NIH, a proposal's impact score is heavily influenced by the enthusiasm (or lack thereof) of the assigned reviewers during study section discussion. At DOD, a proposal that perfectly addresses the topic description but misses the unstated priority of the program manager may score well technically but never get funded.

This tacit knowledge -- the gap between the official rules and how things actually work -- is precisely the kind of expertise that AI cannot replicate. It is accumulated through years of writing proposals, serving on review panels, and talking to program officers.

Accuracy and Hallucination

AI language models generate plausible text, and sometimes that plausibility masks inaccuracy. In grant writing, the consequences of factual errors are severe:

Citing a statistic that does not exist or is outdated undermines your entire needs assessment
Describing an agency requirement incorrectly signals that you have not done your homework
Mischaracterizing the state of the art in your field will be caught immediately by expert reviewers
Fabricating preliminary data -- even inadvertently through AI hallucination -- is a career-ending mistake

Every factual claim generated by AI must be verified by a human with domain expertise. This is not optional.

Voice and Authenticity

Experienced reviewers can often detect AI-generated prose. It tends to be fluent but generic, well-organized but lacking the specificity and conviction that characterize proposals written by people who deeply understand the problem and are passionate about solving it. A proposal that reads like a language model wrote it -- smooth, balanced, hedging in all the right places, but somehow impersonal -- will not inspire confidence in reviewers.

The best proposals have voice. They reflect the PI's expertise, the organization's culture, and the community's urgency. AI can help you find your voice by structuring your thoughts and polishing your prose, but it cannot substitute its voice for yours.

The Tools Available Today

The AI tools grant writers use fall into three categories, and understanding their differences matters.

General-Purpose AI Assistants

ChatGPT (OpenAI), Claude (Anthropic), and Gemini (Google) are the most widely used general AI tools. They are powerful, flexible, and inexpensive. Grant writers use them for drafting, editing, brainstorming, and research synthesis.

The limitation is workflow. General-purpose tools do not understand the grant writing process. They do not parse NOFOs, track compliance across sections, or guide you through a structured proposal development workflow. Using ChatGPT for grant writing is like using a word processor with an exceptionally smart autocomplete -- useful, but you bear the full burden of process management.

Purpose-Built Grant Writing Platforms

Tools like Granted AI are designed specifically for the grant proposal workflow. They analyze solicitation documents, identify requirements and evaluation criteria, coach you through each section, and track whether your draft addresses every requirement in the NOFO.

The advantage of purpose-built tools is that they embed grant writing expertise into the software itself. Instead of requiring you to know which section to write next and what it should contain, the tool guides the process. This is particularly valuable for less experienced grant writers or for anyone working with a solicitation they have not encountered before.

Discovery and Management Platforms

Tools like Instrumentl and Grantable focus on finding funding opportunities and managing the grant lifecycle. They are not primarily writing tools, but they complement the writing process by helping you identify the right opportunities and track deadlines.

The Ideal Human + AI Workflow

Based on extensive testing, here is the workflow that produces the best results. It is not fully automated, and it is not supposed to be. It leverages AI where AI is strong and relies on human judgment where humans are irreplaceable.

Step 1: Human Reads and Analyzes the Solicitation

Before any AI touches the proposal, a human with grant writing experience reads the NOFO or RFP from start to finish. They identify the funder's priorities, the scoring criteria, the eligibility requirements, and the unstated expectations that come from experience with that agency or foundation.

Purpose-built tools like Granted AI automate parts of this analysis -- extracting requirements, mapping scoring criteria, identifying compliance elements -- but the strategic interpretation still requires human judgment.

Step 2: AI Generates the Structural Framework

Use AI to create the proposal outline, mapping each required section to the relevant solicitation requirements. This ensures nothing is missed and provides a logical flow that tracks the review criteria.

Step 3: Human Provides the Core Content

The PI, executive director, or project lead provides the substantive content: the research innovation, the program design, the organizational track record, the partnership details, the community context. This is the material that only humans possess.

In practice, this often takes the form of a conversation -- either with a colleague, a grant writer, or an AI tool that asks the right questions. Purpose-built platforms structure this conversation to extract the information needed for each section.

Step 4: AI Drafts and Humans Refine

AI generates first drafts of each section based on the structural framework and the human-provided content. The human then revises: adding specificity, correcting inaccuracies, strengthening the argument, and ensuring the voice is authentic.

This is where the process is most iterative. A section might go through three or four rounds of AI drafting and human revision before it reaches the quality needed for submission.

Step 5: AI Checks Compliance and Consistency

Before submission, AI reviews the complete draft against the solicitation requirements, checking for gaps, inconsistencies, and formatting compliance. This systematic review catches errors that human reviewers commonly miss in the final rush before a deadline.

Step 6: Human Makes Final Decisions

A human with grant writing expertise does the final read. They evaluate whether the proposal tells a coherent story, whether the budget matches the narrative, whether the letters of support are consistent with the project description, and whether the overall package is competitive.

The Honest Assessment

Can AI write a grant proposal? Here is the most accurate answer I can give.

AI can produce a complete draft of a grant proposal that is structurally sound, reasonably well-written, and superficially responsive to the solicitation requirements. For simple foundation grants with straightforward requirements, this draft might be 70-80% of the way to a submittable proposal.

For competitive federal grants -- NIH R01s, NSF research awards, DOD SBIR topics with specific technical requirements -- the AI-generated draft is a starting point, not a destination. It might be 40-50% of the way to a competitive proposal. The remaining work -- injecting institutional knowledge, refining the innovation narrative, validating accuracy, and adding the specificity and conviction that reviewers reward -- must come from humans.

The organizations that use AI most effectively are not trying to automate grant writing. They are using AI to eliminate the low-value work (formatting, compliance checking, boilerplate drafting, budget arithmetic) so that human expertise is concentrated on the high-value work (strategy, innovation, relationship management, and the persuasive writing that wins funding).

AI does not replace grant writers. It makes good grant writers faster and helps less experienced grant writers produce proposals that are more competitive than what they could produce alone. That is a meaningful contribution, even if it falls short of the fantasy of push-button proposal generation.

If you want to see what a purpose-built AI grant writing workflow looks like in practice, Granted AI offers a 7-day free trial. Upload a real solicitation and walk through the process -- it is the fastest way to understand where AI adds value and where your expertise remains essential.

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