AI-Powered Grant Applications: What Actually Works

Everyone in the charity sector has heard that AI can help with grant writing. Most have tried it. According to the Charity Digital Skills Report 2025, 76% of UK charities have used AI tools at work, with grant fundraising and communications among the most common use cases (Charity Digital Skills Report 2025). But here is the uncomfortable truth: most of them are doing it badly.

The typical approach goes something like this. You open ChatGPT, paste in the funder's criteria, type "write a grant application for a youth mentoring programme," and get back 800 words of fluent, confident prose that says absolutely nothing specific. It sounds professional. It reads well. And any funder who has reviewed more than a dozen applications in 2026 can spot it immediately. Funders have reported that reviewers are now noticing a rise in generic, AI-generated language across applications — and it is not helping applicants.

The problem is not that AI is useless for grant writing. It is that most charities are using it the wrong way. They are treating AI as a writer when it should be treated as a drafter — one that works from your actual data, your real outcomes, and your documented case studies. That is the difference between AI slop and a genuinely strong application. This guide explains exactly how to get it right.

What you will learn

• Why most charities are using AI for grant writing incorrectly — and how to fix it
• The critical difference between generic AI writing and data-connected AI drafting
• A practical comparison of ChatGPT-style approaches versus purpose-built grant writing tools
• How to build the data foundation that makes AI grant writing actually effective

Who this is for

• Charity fundraisers and grant writers looking to use AI without producing generic applications
• CEOs and managers at small to medium charities who want to apply for more grants without hiring more staff
• Anyone who has tried ChatGPT for a grant application and been disappointed with the results

Why Does Generic AI Grant Writing Fail?

Generic AI grant writing fails for a simple reason: large language models produce text based on patterns, not evidence. When you ask ChatGPT to write a grant application without feeding it your specific data, it generates plausible-sounding claims that are not grounded in anything real. "Our programme has demonstrated significant positive outcomes for young people in the community" is the kind of sentence AI produces easily — and it is exactly the kind of sentence that makes funders move on to the next application.

The Association of Charitable Foundations (ACF) has consistently highlighted that funders cite "lack of clear evidence of need" as one of the primary reasons for rejecting applications. AI without data makes this problem worse, not better, because it creates an illusion of completeness. The application looks finished, reads professionally, and covers all the sections — but none of the claims are backed by anything.

Charities that include structured outcome data in their applications consistently outperform those submitting narrative-only proposals. The issue is not whether you use AI. It is whether your AI has access to anything worth writing about.

What Is the Difference Between ChatGPT and Data-Connected AI for Grants?

This is the most important distinction in AI grant writing, and most charities have not grasped it yet. The table below illustrates the practical difference.

The difference is stark when you look at actual output. A generic AI prompt might produce: "Our mentoring programme has helped dozens of young people improve their confidence and life skills." A data-connected AI draft from the same charity's actual records might produce: "In the 2024-25 programme year, 47 young people aged 14-18 completed our mentoring programme. Pre- and post-programme surveys showed a 34% average improvement in self-reported confidence scores, and 89% of participants reported feeling more prepared for employment or further education."

The second version is not better written. It is better informed. And that is what funders are looking for.

The applications that stand out are the ones where the charity genuinely knows its numbers. Whether the text was drafted by AI or by hand, if the claims can be verified against reality, that builds funder confidence. Specificity signals competence — and competence signals that the funding will be well used.

How Should Charities Actually Use AI for Grant Applications?

The effective approach to AI grant writing treats AI as a drafter, not an author. You remain the expert on your charity's work. The AI's job is to take the evidence you have already collected and organise it into the structure the funder wants. Here is a practical workflow that works.

Step 1: Build your evidence base first. Before you touch any AI tool, ensure your outcome data is current. This means up-to-date attendance records, recent survey results, documented case studies, and clear programme descriptions. Charities that maintain ongoing data collection spend significantly less time preparing grant applications than those that gather evidence retrospectively.

Step 2: Match your data to the funder's priorities. Read the funder's criteria carefully and identify which of your existing data points are most relevant. A youth employment funder wants employment outcomes and progression data. A community wellbeing funder wants beneficiary feedback and demographic reach. The same charity might use completely different data subsets for each.

Step 3: Let AI draft from your data, not from a blank page. Use a tool that can access your records directly. Plinth's AI grant writer pulls from your stored outcomes, case studies, and programme information to draft each section. You are not asking AI to invent — you are asking it to compose from facts.

Step 4: Edit for voice, accuracy, and nuance. AI does not know the political context of your local area, the personal relationships you have with partner organisations, or the subtle reasons your approach differs from competitors. These are the things you add in editing. A good first draft should need refinement, not rewriting.

Step 5: Verify every claim. Check that every statistic, case study reference, and outcome figure in the draft matches your actual records. Data-connected AI dramatically reduces fabrication risk, but human verification is non-negotiable. The Charity Commission's CC20 guidance on fundraising explicitly requires that claims made to funders are accurate and verifiable.

Which Parts of a Grant Application Can AI Handle Best?

Not all sections of a grant application benefit equally from AI. Understanding where AI adds value — and where it does not — helps you allocate your time effectively.

AI handles well:

• Statement of need — AI can synthesise local deprivation data, ONS statistics, and your own needs assessments into a compelling case. The Office for National Statistics makes extensive datasets freely accessible via its open API, making it increasingly easy for AI tools to incorporate verified external data.
• Programme description — If your programme model is documented, AI can describe it clearly and consistently across multiple applications.
• Evidence of impact — This is where data-connected AI excels. Pulling outcome figures, survey results, and beneficiary numbers directly from impact reports into the application narrative.
• Case studies — AI can format and summarise case notes into funder-appropriate narratives, maintaining anonymity while preserving the specificity that makes stories compelling.

AI handles poorly:

• Organisational strategy — Why your charity exists, what makes your approach distinctive, and how this application fits into your broader mission. These require human insight.
• Partnership and relationship context — AI cannot explain the nuances of your local partnerships or stakeholder relationships.
• Budget justification — While AI can format budgets, the reasoning behind specific cost allocations needs human input. The Directory of Social Change notes that unrealistic budgets remain one of the top five reasons applications are rejected.
• Sustainability planning — What happens after the funding ends requires strategic thinking that AI cannot replicate.

What Are the Risks of Using AI for Grant Applications?

Ignoring the risks would be dishonest. AI for grant writing carries real pitfalls, and charities need to navigate them deliberately.

Fabrication and hallucination. Generic AI tools will invent statistics, fabricate quotes, and create plausible-sounding evidence that does not exist. Research consistently shows that large language models produce factually inaccurate claims at significant rates when not grounded in source data. Data-connected AI reduces this significantly but does not eliminate it entirely.

Homogenisation. When many charities use the same AI tools with similar prompts, applications start to sound identical. Funders reviewing 200 applications notice. The National Lottery Community Fund has published guidance warning that AI-generated applications often "produce generic content or include buzzwords that don't capture your unique perspective" (TNLCF, AI Guidance). Funders reviewing hundreds of applications notice when text follows the same structures and phrases.

Over-reliance. AI should augment your grant writing capacity, not replace your understanding of the funder relationship. Charities that delegate the entire process to AI — including reading the funder's guidance — tend to produce applications that technically answer every question but miss the funder's underlying priorities.

Data protection. Pasting beneficiary information into public AI tools like ChatGPT raises serious GDPR concerns. The Information Commissioner's Office (ICO) issued updated guidance in 2025 making clear that charity data shared with AI processors must meet the same standards as any other data processing arrangement. Purpose-built tools like Plinth process data within controlled environments with appropriate data processing agreements in place.

Funders are not anti-AI. But a clear split is emerging between applications where AI has been used thoughtfully — with real data behind it — and those where someone has clearly just prompted ChatGPT and submitted the output. The latter group is not doing themselves any favours, and experienced assessors can tell the difference almost immediately.

How Much Time Does AI Actually Save on Grant Applications?

The time savings are real, but they depend entirely on your starting point. If you have no outcome data, no case studies, and no documented programme model, AI cannot help you much — because it has nothing to work from. If you have a solid evidence base, the savings are substantial.

Based on aggregated data from charities using Plinth and comparable platforms, here are realistic time estimates:

The critical insight from this table is that generic AI barely saves time overall. You gain speed on the first draft but lose it in editing, because generic AI produces text that sounds right but needs extensive correction to be accurate. Data-connected AI saves time at every stage because the underlying information is already verified.

For a charity submitting 15-20 grant applications per year — typical for a medium-sized organisation according to the Chartered Institute of Fundraising — the difference between 20 hours per application and 6 hours per application is the equivalent of reclaiming roughly 280 hours annually. That is more than seven full working weeks.

How Do You Build the Data Foundation for AI Grant Writing?

The charities that get the most from AI grant writing are not the ones with the best prompts. They are the ones with the best data. Building that foundation is not as daunting as it sounds, but it does require intentional effort.

Start with outcome tracking. Define 3-5 core outcomes for each programme and measure them consistently. Use pre- and post-programme surveys where appropriate. Many charities still lack a documented outcomes framework, even though funders increasingly expect to see outcome data in applications.

Capture case studies as you go. The biggest barrier to strong applications is not having recent, relevant case studies when you need them. Rather than scrambling before each deadline, build case study collection into your regular workflow. Plinth's AI case notes let frontline staff record a short conversation with a beneficiary and automatically generate a structured, anonymised case study that is stored and ready for use.

Keep programme information current. Your programme descriptions, staff qualifications, partnership agreements, and safeguarding policies should be up to date and accessible. AI can only draft from what it can access.

Centralise everything. The reason most charities struggle with grant applications is not lack of evidence — it is that evidence is scattered across spreadsheets, email inboxes, shared drives, and individual staff members' memories. Bringing all your data into a single platform like Plinth means your AI grant writer can draw on everything, not just what you remember to paste in.

Can Small Charities Use AI for Grant Writing?

Absolutely — and in many ways, small charities have the most to gain. Organisations with annual income under £500,000 typically have one or two staff members responsible for all fundraising, alongside everything else they do. Small charities typically spend a disproportionate share of available staff time on grant applications compared to larger organisations with dedicated fundraising teams.

AI levels the playing field. A small charity with good data and a data-connected AI tool can produce applications of equivalent quality to a larger organisation with a full-time grant writer. The key requirements are:

• Some form of outcome tracking — even basic pre/post surveys or attendance records
• At least 3-5 documented case studies — refreshed within the last 12 months
• A clear programme description — what you do, who you serve, and what changes as a result

Plinth offers a free tier specifically so that small charities can start building this foundation without needing budget approval. The AI grant writer, impact reporting, and case study tools are all available from the outset.

The Charity Commission's register shows there are over 170,000 registered charities in England and Wales, and the vast majority have annual incomes below £100,000. These are the organisations that can least afford to spend 20 hours on each grant application — and the ones that benefit most from getting that number down to five or six.

What Should Charities Look For in an AI Grant Writing Tool?

Not all AI grant writing tools are created equal. Some are essentially ChatGPT wrappers with a grant-specific prompt template. Others are deeply integrated with your data. Here is what to evaluate:

Data integration. Can the tool access your existing outcome data, case studies, and programme records? If it requires you to copy and paste information in each time, it is not meaningfully different from using ChatGPT directly.

Funder-specific tailoring. Can the tool adjust its output based on the specific funder's requirements, word limits, and assessment criteria? Different funders want different things, and a good tool adapts accordingly.

Accuracy and source citation. Does the tool reference specific data points from your records, or does it produce ungrounded claims? Can you trace each statistic in the draft back to its source?

Data protection. Where is your data processed? Is there a GDPR-compliant data processing agreement? The ICO's 2025 guidance is clear: charity data shared with AI tools must be treated with the same rigour as any other data processing.

Sector-specific design. Tools built specifically for charities understand the language, structure, and expectations of UK funders. Generic AI writing tools do not know the difference between a Lottery application and a trust fund submission.

Cost. For small charities, cost is a deciding factor. Some AI grant tools charge per application — which can add up quickly if you are submitting 15-20 per year. Platforms like Plinth include AI grant writing as part of a broader toolset, with a free tier available.

Frequently Asked Questions

Will funders reject applications they think were written by AI?

Most funders have not introduced explicit AI policies for applicants. The National Lottery Community Fund and the ACF have both stated that they assess applications on quality and evidence, regardless of how they were produced. However, funders are increasingly able to recognise generic, AI-produced text — and those applications tend to score poorly because they lack specificity, not because they were flagged as AI-written.

Is it ethical to use AI for charity grant applications?

Yes, provided you use it responsibly. The key ethical requirements are accuracy (every claim must be verifiable), transparency (some funders now ask whether AI was used), and data protection (beneficiary data must be processed in compliance with GDPR). Using AI to draft from verified data is no different from using a template or asking a colleague to help write a section.

Can ChatGPT write a good grant application?

ChatGPT can produce a structurally competent draft, but without access to your specific data, it will fill sections with generic claims that do not stand up to scrutiny. If you must use ChatGPT, feed it your actual outcome data, recent case studies, and programme details as context. Even then, expect significant editing. A purpose-built tool like Plinth's AI grant writer that already holds your data will produce a stronger first draft with far less manual effort.

How do I avoid AI hallucinations in grant applications?

The single most effective measure is to ensure the AI drafts from your actual data rather than generating from nothing. Data-connected AI tools significantly reduce hallucination because they are composing from verified facts, not inventing. Beyond that, always verify every statistic, case study, and claim in the final draft against your source records. Never submit an AI draft without human review.

What data do I need before using AI for grant writing?

At a minimum: attendance or participation records for your programmes, 3-5 recent case studies or beneficiary stories, basic outcome data (even simple pre/post survey results), and a clear description of your programme model. The more structured and up-to-date your data, the better the AI output. This is why platforms like Plinth that combine data collection with AI writing tools are more effective than standalone writing tools.

Does AI grant writing work for statutory funding applications?

Yes, though statutory applications often have more rigid formats, specific compliance requirements, and detailed financial reporting expectations. AI is particularly useful for the narrative sections of statutory applications, where you need to present evidence of need and impact within tight word limits. Budget sections and compliance declarations still require careful human input.

How much does AI grant writing cost?

Costs vary widely. Using ChatGPT directly costs approximately £16-20 per month for the Plus subscription. Specialist AI grant writing tools range from £30 to £200 per month, and some charge per application. Plinth includes AI grant writing within its broader platform, with a free tier that gives small charities access without upfront cost. The return on investment depends on your application volume — charities submitting 10 or more applications per year typically see substantial time savings that far exceed the tool cost.

Can AI help with grant reporting as well as applications?

Yes. In many ways, AI is even more valuable for grant reporting than for applications, because reports draw heavily on data that should already be collected — outputs, outcomes, financial summaries, and case studies. Tools like Plinth's impact reporting feature generate funder-specific reports from your underlying data, which means you maintain one dataset and produce multiple tailored reports rather than writing each one from scratch.

Recommended Next Pages

• How to Write Grant Applications That Actually Get Funded — the evidence-led approach to structuring winning applications
• AI for Charities: What Actually Works in 2026 — broader guide to practical AI adoption across charity operations
• Grant Application Best Practices for UK Charities — detailed checklist and section-by-section guidance
• Why Your Grant Applications Keep Getting Rejected — common mistakes and how to diagnose them
• How to Prove Your Charity's Impact to Funders — building the evidence base that makes grant writing easier

Last updated: February 2026