{"id":2284,"date":"2026-03-04T17:01:34","date_gmt":"2026-03-04T17:01:34","guid":{"rendered":"https:\/\/leyton.majjane.agency\/ca\/post\/even-as-ai-standardizes-experimental-work-continues-to-qualify-for-sred\/"},"modified":"2026-03-04T17:01:34","modified_gmt":"2026-03-04T17:01:34","slug":"even-as-ai-standardizes-experimental-work-continues-to-qualify-for-sred","status":"publish","type":"article","link":"https:\/\/leyton.com\/ca\/en\/insights\/articles\/even-as-ai-standardizes-experimental-work-continues-to-qualify-for-sred\/","title":{"rendered":"Even as AI standardizes, experimental work continues to qualify for SR&ED"},"content":{"rendered":"

For years, Canada\u2019s SR&ED program<\/a><\/strong> has been an effective way for companies to reduce the costs of experimental AI innovation. <\/p>\n

It was designed to reward businesses that take real risks in research and development, and for a while, artificial intelligence (AI) <\/a><\/strong>projects were a perfect fit. After all, early AI work often meant solving problems no one had tackled before.<\/p>\n

AI tools are now standardized<\/h2>\n

But things are changing. <\/p>\n

AI has matured quickly, and many of the once cutting-edge tools are now widely available off the shelf. Pre-trained models, open-source frameworks, and cloud-based machine learning services have made it possible to deploy AI without the same level of experimental work. <\/p>\n

That means a lot of AI work today looks more like applying known solutions than pushing scientific boundaries. And when the work isn\u2019t experimental, it\u2019s harder to make the case that it qualifies for SR&ED.<\/p>\n

Where AI work is still experimental<\/h2>\n

This doesn\u2019t mean AI is out of the picture entirely. <\/p>\n

There are still plenty of areas where businesses are breaking new ground: think AI in industries with scarce or messy datasets, developing more efficient large-scale models, or creating custom systems that can operate in real time. <\/p>\n

Projects like these still face genuine technological uncertainty and rely on experimentation, which are the key factors the Canada Revenue Agency (CRA) looks for when deciding eligibility.<\/p>\n

What companies need to show CRA<\/h2>\n

For companies, the takeaway is clear: it\u2019s not enough to say you used AI, you need to show how your project involved challenges that existing tools couldn\u2019t solve. <\/p>\n

Careful documentation of the technological uncertainties you faced and the experiments you ran will only grow more important as the CRA scrutinizes claims in this space.<\/p>\n

How SR&ED\u2019s role may evolve<\/h2>\n

As AI continues to become part of everyday business, the role of SR&ED<\/a><\/strong> will likely shift too. Instead of broadly supporting AI across the board, the program may become more selective, focusing on projects that truly stretch what the technology can do.<\/p>\n

For Canadian companies, that raises an important question: how can we demonstrate innovation when the tools in use have become standard?<\/strong><\/p>\n

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Sources<\/h3>\n