AI Can Write Your Grant, It Cannot Manage Your Compliance
- HiQuity Solutions
- 6 days ago
- 7 min read
POST 9 of 10
Series - Grant Ready: A Compliance Readiness Framework for Federal Award Recipients
Introduction
If you are writing federal grant proposals this spring, chances are AI tools are already part of your process, whether for drafting narratives, creating outlines, checking formatting, or scanning requirements. That isn’t necessarily a problem, but it can become one when organizations use AI in ways that create regulatory risks they do not foresee, or when AI-generated content replaces the organizational knowledge, strategic judgment, and compliance infrastructure needed to win and sustain a federal award.
This post is not an argument against AI in grant work: it is a guide to where AI adds real value, where it creates risks, and what the current federal policy environment around AI use looks like, because its many uses are advancing faster than most organizations realize.
HHS Grantee Note: The regulatory citations throughout this series reflect 2 CFR Part 200, which fully replaced 45 CFR Part 75 as the governing framework for HHS awards effective October 1, 2025. If your organization's compliance policies still reference Part 75, see early posts in this series for what changed and what your organization needs to do.
What AI Does Well in the Grant Context
Used appropriately, AI tools can make the proposal development process meaningfully more efficient. Where they add the most legitimate value are in tasks that are time-consuming but not judgment-dependent.
Research and landscape scanning: AI can accelerate the process of identifying funding opportunities, summarizing program guidelines, compiling demographic and needs data, and surfacing relevant evidence base for program models. These are tasks that previously required hours of database work and literature review, but AI compresses that time provided the outputs are verified against primary sources before they appear in a proposal (!) | |
Document organization and formatting: Federal proposals have strict formatting requirements when it comes to page limits, font specifications, section headers, and attachment checklists. AI tools are genuinely useful for ensuring a nearly complete draft meets those structural requirements, identifying gaps against the Notice of Funding Opportunity, and organizing content into the sequence the funder specifies. | |
Editing and plain language revision: Federal reviewers, and increasingly federal agencies themselves, favor clear, jargon-free language that connects program design to measurable outcomes. AI can be a useful editing tool for tightening dense technical prose, flagging passive constructions, and restructuring paragraphs for clarity as long as a human with subject matter expertise reviews the result (!) | |
Compliance flagging: AI can be used to cross-reference a draft proposal against a funding opportunity announcement and identify sections where the response does not directly address a stated requirement, or where terminology may not align with current federal language priorities. |
Where AI Introduces Risk
The risks are specific and, in some cases, already producing consequences for organizations that have not taken them seriously.
Fabricated citations: As of 2025, generative AI tools remain unreliable sources of research citations. They produce well-formatted references that appear authoritative, and also frequently do not exist, or cite real publications that do not contain the findings attributed to them. Federal grant proposals that cite fabricated or inaccurate research expose the organization to findings of misrepresentation. Every citation in a federal proposal must be verified against the actual source before submission; and this step cannot be delegated to the AI tool that generated the citation. | |
Generic and misaligned narratives: AI tools generate text based on patterns across large bodies of content, which means that AI-generated grant narratives tend toward the generic. A proposal that sounds like every other proposal in a competitive pool is not going to be a winning proposal. More critically, AI tools do not know your organization's service history, your community's specific documented need, your data, or your program model's evidence base. Any proposal built primarily on AI-generated narrative substitutes plausible language for organizational specificity, and experienced grant reviewers can easily identify the difference. | |
Prohibited and flagged terminology: The federal funding landscape has shifted significantly since 2024. Terminology that was standard in federal proposals two or three years ago now triggers scrutiny during both application review and post-award monitoring at some agencies. AI tools trained on older content will generate language that is currently disfavored or flagged under current federal guidance. Using AI to draft narrative without reviewing the current terminology environment for the specific funding opportunity is a risk organizations are taking without realizing it. | |
Data privacy exposure: Uploading organizational data, program records, client information, or proprietary program design documents to public AI platforms creates data privacy risks that organizations frequently overlook, that down the road can open the door to legal risk. Information submitted to general-purpose AI tools, even in paid subscription format, can be retained, used for model training, or accessible in ways that are not consistent with organizational data governance obligations or, in some cases, federal award requirements. |
What Is Happening at the Federal Level Right Now
The federal policy environment around AI use in grant applications is not static, and is moving in a direction organizations need to track weekly now, if not daily.
The National Institutes of Health issued a significant policy change effective September 25, 2025, prohibiting grant applications that are substantially developed by AI, stating that such applications are not considered original ideas of the applicant and will not be funded. NIH now uses AI-detection software, and post-award detection of AI-generated content can result in misconduct referrals, cost disallowances, grant suspension, or termination.
The National Science Foundation preceded NIH with a disclosure requirement: researchers must disclose any use of AI tools in their proposals, a policy that signals the direction of travel even where an outright prohibition has not yet been implemented.
These are research-focused agencies, and their policies do not automatically extend to the human services, public health, behavioral health, and community development grants that many readers of this series work with. However, given the trajectory, the regulatory signal reads clearly: federal agencies are paying attention to AI use in the proposal process, and the organizations that treat AI as a substitute for original organizational thought and subject matter expertise are taking a risk that is only growing.
At the same time, federal agencies have themselves been using AI-assisted review tools, including, as documented in 2025 litigation involving the National Endowment for the Humanities, keyword-screening systems that flagged and terminated grants based on terminology patterns rather than programmatic merit.
That development underscores a point that runs through this entire series: the language in your proposal, your budget, and your compliance documentation has regulatory consequences. AI can help you write more efficiently, and it cannot substitute for knowing what you are writing into a federal record and why.
A 5-Point Framework for Using AI Responsibly in Federal Grant Work
Use AI for process, not substance: AI is most useful in the grant context for tasks that are structural or mechanical, like formatting, organizing, checklisting, and editing for clarity. The substantive content of a federal proposal (the documented need, program model, organizational capacity, data, and the evidence base) has to come from the organization; AI cannot supply what only your organization knows. | |
Verify everything AI generates before it becomes a federal record: every citation, every data point, every regulatory reference, every claim about program outcomes that AI produces must be checked against a primary source before it appears in a submission. Why? The proposal itself is a federal record. What goes into it is your organization's representation to the federal government, and not the AI tool's. | |
Know the current terminology environment for each funding opportunity: before using AI to draft narrative for a specific federal opportunity, review the current funding priority language in that NOFO and* at that agency. The terminology landscape is shifting, and as of now, AI tools do not have real-time awareness of what is currently appropriate for a specific program. | |
Do not upload sensitive organizational data to public AI platforms: if your grant development process involves program data, client information, or proprietary design documents, keep that material out of general-purpose AI tools (!) Use organization-controlled tools with appropriate data governance or work from anonymized or aggregated information. | |
Read the funding opportunity announcement for AI disclosure requirements: before submitting any proposal, check whether the funding agency has disclosure requirements or restrictions related to AI use. This is a new but growing compliance obligation that should be part of every pre-submission checklist. |
The Larger Point
AI is a legitimate and useful tool in federal grant work when it is used to accelerate tasks that do not require organizational judgment, and when every output it produces is reviewed and verified by someone who understands both the subject matter and the regulatory context.
What AI cannot do is substitute for the compliance infrastructure this series has been building toward: the procurement policies, budget discipline, cost allowability judgment, subrecipient structures, and the closeout planning. Those obligations belong to your organization and are also enforced against your organization; they cannot be delegated to a technology tool regardless of how capable that tool becomes.
The proposal you write with AI assistance is only as strong as the systems behind it, and building those systems is what this Grant Ready Compliance Framework series has been about from the beginning.
HiQuity Solutions integrates AI tools into our grant research and writing process alongside the subject matter expertise, regulatory knowledge, and compliance infrastructure that federal awards require. If you want to know how we do it, reach out. We're happy to hear from you.
References
[1] Grant Writing & Funding. "How AI is Changing Grant Writing and What You Need to Know" (July 2025). https://grantwritingandfunding.com/how-ai-is-changing-grant-writing-and-what-you-need-to-know/
[2] Millionaire Grant Lady and Associates. "AI for Grant Writing: Use It Without Losing the Human Touch" (November 2025). https://millionairegrantlady.com/ai-for-grant-writing/
[3] Grant Writing & Funding. "How AI is Changing Grant Writing and What You Need to Know" (July 2025). https://grantwritingandfunding.com/how-ai-is-changing-grant-writing-and-what-you-need-to-know/
[4] National Institutes of Health. "The Use of Generative Artificial Intelligence Technologies is Prohibited for the NIH Peer Review Process," NOT-OD-23-149 (June 2023). https://grants.nih.gov/grants/guide/notice-files/NOT-OD-23-149.html
[5] Gordon's STEM Blog. "Federal Agencies Are Not on the Same Page About AI in Grant Proposals" (February 2026). https://www.gordostuff.com/2026/02/federal-agencies-are-not-on-same-page.html; see also Akela Consulting, "Can I Use ChatGPT for SBIR Grants?" (July 2025). https://www.akelaconsultants.com/post/can-i-use-chatgpt-for-sbir-grants
[6] Granted AI. "Inside the DOGE-ChatGPT Fiasco: How AI Killed 97% of NEH Grants" (March 2026). https://grantedai.com/blog/doge-chatgpt-neh-grants-lawsuit-ai-government-decision-making-2026
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