Grant Writing With AI: What It Does Well

A grant application is two documents wearing one cover. Most of it is material you have written before in slightly different words: who you are, who works here, what the timeline looks like, what could go wrong. A smaller part is the specific, evidenced case for why this project in this business deserves the money. AI is good at the first and actively dangerous in the second.

Split the document

Section	Who writes it
Company background and history	Assistant, from your own past documents
Team and capability	Assistant, from real CVs you supply
Timeline, milestones, risk register	Assistant, from your plan
Why this project, in this business	You, entirely
Any number or projected outcome	You, from records

The top three rows are usually more than half the word count and almost none of the decision. Getting them drafted in twenty minutes from material you already have is the whole benefit, and it is a large one when the alternative is a Saturday.

Three uses that work

Reusing your own past applications. Feed in two previous submissions and ask for the background sections rewritten to fit the new programme's word limits and language. This is the highest-value use and the one people skip, because it feels like cheating rather than like reusing your own work.

Reading the guidance back to you. Paste the programme guidelines and ask what the fund is actually trying to achieve, what evidence it says it wants, and what would disqualify an application. Assessment criteria are frequently buried in a forty-page document and an assistant will pull them into a checklist in a minute.

Reviewing your draft as an assessor. Ask it to score your draft against the published criteria and say where the evidence is thin. It is a better critic than a writer here, because criticism only needs the document in front of it. That is the same asymmetry described in contract templates from AI, where reading a document you were sent is far safer than generating one.

The section that decides it

Every programme exists to produce some outcome: more productivity, more exports, more adoption in a region, more jobs. Assessors read a great many applications that describe a perfectly reasonable project and never connect it to that purpose.

Making that connection needs three things a model does not have. What specifically is slow or broken in your business right now. What you have already tried. And what changes, measurably, if this is funded. All three come from you, and an application that contains them is immediately distinguishable from one that does not.

This is where the process mapping exercise pays for itself twice. A business that has timed its own bottleneck can write two sentences that no generated text can match: the task takes four hours, it happens eleven times a week, and here is what we measured when we tried to fix it.

Never let it produce a number

Ask a model for projected job creation, expected efficiency gains or a sector benchmark and it will give you confident figures with no basis. Some of them will look plausible enough to submit.

An application containing invented numbers is worse than a late one, because funding agreements carry reporting obligations and you may be held to a figure nobody checked. Anything quantitative comes from your own records or from a calculation you can show, and any statistic you cite gets verified at source before it goes in.

Our ROI calculator produces defensible figures from your own timings, which is the right kind of number for this.

Decide whether to apply at all

The question people skip. Application effort varies enormously, and so does fit.

A two-hour application to a programme that helps pay for something you were going to buy anyway is straightforwardly worth doing. A forty-hour application with a low success rate, for a project you would not otherwise run, usually is not, and the real cost is the forty hours that did not go into the business.

Before drafting anything, use an assistant for the cheapest possible test: paste the eligibility criteria and your basic details and ask whether you plausibly qualify and what would disqualify you. Ten minutes there saves the applications that were never going to land. Which programmes exist and what they fund is covered in Canadian AI companies and the funding attached to them and in our guide to Canadian AI grants.

Frequently Asked Questions

Can AI write a grant application?

It can write the parts that repeat between applications, which is usually more than half the document: company background, team descriptions, project timelines, risk sections and the summary. It handles the section that decides the outcome badly, because that section needs specific evidence about your business that the model does not have and will invent if asked. Split the document and you get most of the speed without the risk.

Which part of a grant application decides the outcome?

The case for why this project, in this business, produces the outcome the programme exists to fund. Assessors read a lot of applications that describe a reasonable project without connecting it to the fund’s stated purpose. Generic text is what they are trained to discount, so the section written to sound impressive is the section least likely to work.

Will assessors know I used AI?

Often, and it matters less than what the text says. A section that could describe any business in your sector reads as filler regardless of who wrote it, and assessors were discounting boilerplate long before generative tools existed. The risk is not detection. It is producing something fluent, plausible and unspecific, then submitting it because it reads well.

What should I never let AI do in a grant application?

Generate numbers, outcomes, or claims about your business. A model asked for projected job creation or expected efficiency gains will produce confident figures that have no basis, and an application containing invented numbers is worse than a late one. Anything quantitative comes from your own records or from a calculation you can show. Never let it cite a statistic you have not checked.

Is it worth applying for AI adoption funding as a small business?

It depends on your time more than the odds. Application effort varies enormously between programmes, and a two-hour application to a fund that pays for something you were going to buy anyway is straightforwardly worth it. A forty-hour application with a low success rate for a project you would not otherwise run usually is not, and the cost is the forty hours you did not spend on the business.