Using AI can save you time when trying to turn your rough notes and data into a technical narrative or a grant application. It’s no surprise that business owners and R&D teams may be tempted to use readily available AI tools to help prepare their own SR&ED claims and grant applications.
However, in order for these applications to be successful and translate into funding for your company, you need more than a good piece of writing: you need to be able to explain your work accurately and frame it around eligibility criteria, while ensuring full defensibility of your statements in the event of an audit.
When employees or business owners experiment with AI tools independently, without appropriate data-security safeguards or a thorough review of the resulting content, they may introduce risks that outweigh the time saved.
The issue isn’t necessarily using AI. It’s how the technology is used, what information is shared, and whether the resulting application accurately reflects the work performed and meets the relevant funding criteria.
Here are five considerations to keep in mind if you’re using AI to support your SR&ED and grant applications:
1. Protecting You Intellectual Property
Your company’s R&D may involve proprietary processes, technical data, product designs, and commercially sensitive information. Before entering this information into an AI tool, it’s important to understand whether the tool has been approved for business use and how it handles the information you provide.
Different AI tools and account configurations have different data-handling practices. Using AI assistants without understanding its retention policies, access controls, or data-use terms could expose your company to unnecessary confidentiality and intellectual property risks for both free and paid tiers.
This is particularly important when preparing funding applications, which may require access to detailed descriptions of your technical challenges, experimental results, internal processes, and future development plans.
2. Generic Applications That Fail to Tell Your Specific Story
AI can produce polished, professional-sounding language, but may not be specific enough to the project or the funding opportunity.
For SR&ED claims, the technical narrative needs to accurately reflect the work performed by your company, the technological challenges you encountered, the approaches you investigated and the results achieved. Grant applications need to connect your proposed activities, objectives and expected outcomes to the specific program requirements. When AI is used without sufficient project context or careful review, it can produce generic descriptions that lack the technical detail, project-specific information, or alignment needed to properly address the requirements of a particular claim or funding application. The result may read well but fail to provide the information needed to effectively communicate the work that you conducted or demonstrate how this specific project aligns with the funding opportunity.
3. Accuracy, Evidence, and Defensibility
AI-generated content can sound authoritative even when it contains errors. It may misinterpret technical data, introduce unsupported conclusions, or generate explanations that don’t reflect what actually happened.
Part of that challenge is that large language models rely on the data they are trained on. While they can draw on broad technical knowledge and general writing samples, they do not have access to CRA-submitted SR&ED claims, grant applications, or their assessment outcomes. As a result, AI may not reliably recognize the level of technical detail, evidence and explanation needed to support a specific claim.
This matters because a funding application must be supported by the underlying facts, not simply by a convincing narrative. For SR&ED claims, technical records, experiment results, project notes, and other supporting documentation may be needed to substantiate the work performed. Grant programs may also require evidence that project activities, expenditures, and reported outcomes meet their requirements. Documentation should be developed throughout the project and be available when the application is prepared. Relying on AI to reconstruct technical details after the fact can introduce inaccuracies or leave gaps that are difficult to resolve once the work has been completed.
Similarly, a project can be innovative, commercially valuable, and technically complex without necessarily qualifying for SR&ED or a particular funding program. Each program has its own eligibility requirements, definitions, and assessment criteria. AI can help identify relevant programs and summarize their requirements, but it may overlook important conditions, rely on outdated information, or apply eligibility criteria too broadly. The project’s circumstances and the applicable program guidelines must be carefully reviewed to determine whether it qualifies.
If an application contains statements that cannot be supported by the available records, or that contradict what the team actually did, the company may face additional scrutiny and potential financial consequences.
4. Can You Defend Your Claim in an Audit?
Consider what happens if your claim is audited.
Submitting an application is not the end of the process. For SR&ED claims, CRA may review the work and ask questions about the technological uncertainties you faced, the experiments you conducted, the approaches you tried, and the conclusions you reached. Someone from your company needs to be able to explain the work, answer follow-up questions, and connect the statements in the claim to the underlying technical evidence. This is why contemporaneous documentation and the involvement of the people who performed the work are so important.
AI can help prepare a narrative, but it cannot replace the people who performed the work or take responsibility for defending the claim. If your team cannot substantiate what has been submitted, polished AI-generated language won’t make the claim defensible.
The same principle applies more broadly to funding applications: the company is responsible for the accuracy of the information submitted and must be prepared to support its statements with appropriate evidence.
5. Oversimplifying the R&D Process
Research and development rarely follows a perfectly linear path. Teams form hypotheses, test different approaches, encounter failures, analyze results, and adjust their methods as they learn.
These details can be critical to explaining the work undertaken. When AI is asked to summarize a complex project into a concise, polished narrative, it may unintentionally remove the failed approaches, iterative experimentation, and technical reasoning that help demonstrate the nature and progression of the work.
The result can be a simplified success story that sounds clear but doesn’t accurately represent how the team arrived at its conclusions. In an SR&ED claim, this can be particularly problematic when the omitted details are relevant to demonstrating the technological uncertainties and systematic investigation involved.
A strong application doesn’t need to include every detail of a project, but it does need to preserve the information that matters to understanding the work and assessing eligibility.
Using AI Effectively
AI can be a valuable tool when used appropriately. It can help organize information, identify gaps, improve readability and reduce time spent on repetitive tasks.
The important distinction is between using AI to support your application process, and relying on it to determine what qualifies, interpret your technical work, or establish facts without verification.
When using AI in a business setting, the tools, data-handling practices and review processes matter just as much as the technology itself. Appropriate safeguards and expert oversight help ensure that efficiency doesn’t come at the expense of accuracy, confidentiality, or defensibility.
Ultimately, a strong application still requires a clear understanding of the project, a careful assessment of eligibility, and a defensible connection between the work performed and the funding criteria.
