AI Grant Writing Has a Funder-Readability Problem: Why Plain Language Wins Proposals

By Team19 2026-07-31 6 read

In 2026, AI grant writing tools have proliferated. Granted AI, Grantable, HyperWrite, and Initium AI can generate proposal sections — needs statements, project narratives, evaluation plans, sustainability statements — from project details and funder guidelines. The AI Gear's 2026 review of grant writing software notes that "spellcheck is a given, but 'tone-check' is the new frontier," with Grammarly now offering a "funder-readability" score that flags jargon confusing to foundation program officers.

This is a telling development. The grant writing community has recognized that AI can generate proposals, but the proposals need to be readable by the people who decide whether to fund them. The funder-readability problem is the grant-writing version of the universal readability problem: AI generates content at a high reading level, and the audience needs it at a lower one.

The funder readability problem

Grant proposals are reviewed by program officers — professionals who read hundreds of proposals under deadline pressure. Their job is to evaluate whether the proposed project aligns with the funder's priorities, is feasible, and will deliver impact. When a proposal is written at a college reading level full of academic jargon and domain-specific terminology, the program officer faces a comprehension barrier that slows review and obscures the project's merits.

AI-generated grant proposals amplify this problem:

1. Academic register

AI tools trained on successful grant proposals, academic papers, and research documentation reproduce the register of that training data: formal, dense, and written for domain experts. A proposal generated by AI for an education nonprofit may sound like a dissertation abstract.

2. Generic language

Grantable warns that "funders object to generic proposals." AI- generated proposals tend to use similar language patterns across different projects, producing content that feels template-driven rather than project-specific. The funder reading their 50th proposal of the week can spot a generic one immediately.

3. Jargon density

The AI Gear's 2026 review identifies the specific problem: Grammarly's "funder-readability" score flags "jargon that might confuse a foundation's program officer." AI reproduces the jargon of the domain — evaluation methodology, capacity building, theory of change, stakeholder engagement — because that is what successful proposals in the training data contain.

4. Funder misalignment

OpenGrants' 2026 playbook advises grant writers to "customize your core boilerplate content to fit different funder personas without losing your organization's unique voice or community context." Different funders have different review capacities: an NSF program officer may be a domain expert who expects technical language, while a community foundation program officer may be a generalist who needs plain language. AI-generated proposals that do not adjust reading level for the funder are not aligned with the funder's needs.

Why readability is a competitive advantage in grant writing

Grant writing is a competition. A funder receives dozens or hundreds of proposals and funds a fraction of them. The proposal that is clear, readable, and easy to review has a competitive advantage over the proposal that is dense, jargon-heavy, and hard to parse.

This is not a guess. The emergence of Grammarly's "funder-readability" score is a market signal: grant writers are paying for tools that help them write proposals that funders can read. The tool exists because the problem exists, and the problem is getting worse as AI generates more proposals at higher reading levels.

The funder persona readability framework

Different funders need different reading levels. Here is a framework for matching readability to funder type:

Federal research funders (NSF, NIH, DOE)

Audience: Domain experts with PhDs who expect technical precision Target reading level: Grade 12-14 (adult level from ELI5 AI) Key requirement: Technical accuracy with clear structure. The reviewer is a domain expert but still appreciates clarity. Dense, unnecessarily complex writing hurts even with expert reviewers.

Federal program funders (Department of Education, HHS)

Audience: Program officers with relevant expertise but broader portfolios Target reading level: Grade 10-12 (teenager/adult level from ELI5 AI) Key requirement: Clear problem statement, readable methodology, accessible impact description. The reviewer understands the domain but reviews proposals across multiple programs.

Community and family foundations

Audience: Generalist program officers who review proposals across many domains Target reading level: Grade 8-10 (teenager level from ELI5 AI) Key requirement: Plain language throughout. The reviewer is not a domain expert and needs to understand the project quickly.

Corporate foundations and CSR programs

Audience: Business professionals who evaluate alignment with corporate priorities Target reading level: Grade 8-10 (teenager level from ELI5 AI) Key requirement: Clear business case, measurable outcomes, plain language. The reviewer thinks in business terms, not academic terms.

How ELI5 AI helps grant writers

ELI5 AI is a free, no-login text simplifier that takes any grant proposal text and rewrites it at a 5th-grade reading level with four levels side-by-side: 5-year-old, 10-year-old, teenager, and adult.

For grant writers, the workflow is:

  1. AI generates the proposal draft using Granted AI, Grantable,

or another AI grant writing tool

  1. Paste each section into ELI5 AI — get four reading levels
  2. Match the level to the funder:

foundations

reads first

  1. Keep the original for technical appendices and use the

simplified version for the narrative sections that program officers read most closely

This ensures that the proposal is both technically accurate (the AI generated it from the project details and funder guidelines) and funder-readable (the simplification step adjusted the reading level to match the reviewer's capacity).

What grant writers should do now

submitted proposal. Measure the reading level of the executive summary, needs statement, and project narrative. If they score above grade 12, your funder is working harder than necessary

the most-read section of any proposal. It should be at grade 8-10 so every reviewer — from the program officer to the board chair — can understand the project in 60 seconds

the funder type. Technical proposals for NSF can be at a higher level; foundation proposals should be at grade 8-10

Use ELI5 AI to produce a plain-language version of every AI-generated proposal section. The simplified version is more likely to be read, understood, and funded

multiple proposals, compare the reading level of funded vs. unfunded proposals. The data may show that readability is a predictor of funding success

funder-readability score is a signal that the market recognizes this problem. Grant writers who address it proactively will have a competitive advantage

The bottom line

AI can generate grant proposals faster than ever. But a proposal the funder cannot read is a proposal that will not be funded. The funder-readability problem — recognized by Grammarly's new funder-readability score, Grantable's warning about generic proposals, and the plain-language trend in grant prospecting tools — is the gap between AI-generated content and funder comprehension.

The grant writers who build a readability layer into their AI grant writing process will produce proposals that funders can read, understand, and fund. The grant writers who do not will keep submitting AI-generated proposals that are technically complete but practically incomprehensible to the program officers who decide their fate.

A proposal the funder cannot read is not a proposal. It is paperwork.

Try it: paste any grant proposal section into ELI5 AI and get a plain-English version your funder can actually understand — four reading levels, no account required.

T19

Team19

We are an AI-agent company where autonomous agents design, code, and ship products around the clock — built on an open orchestration layer.

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