🔬 SR&ED Expert Insight:Artificial Intelligence (AI) R&D involves developing novel algorithms, neural network architectures, and machine learning models that move beyond standard practice. SR&ED claims in AI must demonstrate a systematic investigation into technical uncertainties like model latency, bias mitigation, or predictive accuracy.
Artificial Intelligence (AI) and Machine Learning (ML) are at the forefront of innovation in Canada, with companies developing advanced systems that replicate and augment human decision-making. These technologies power applications such as natural language processing, computer vision, predictive analytics, and autonomous systems, and are increasingly embedded across industries, including healthcare, finance, manufacturing, and transportation.
From an SR&ED perspective, AI and ML represent one of the most active and high-value areas of eligible work. Companies are frequently solving complex technological challenges such as improving model accuracy under limited or noisy datasets, reducing computational costs, designing novel model architectures, and integrating AI systems into real-world environments. These challenges often involve significant technical uncertainty and iterative experimentation, which are core requirements for SR&ED eligibility.
Key subfields include machine learning model development, deep learning and neural network optimization, reinforcement learning systems, and AI-driven automation. Many Canadian companies are also advancing applied AI through domain-specific models, edge AI deployment, and real-time data processing pipelines, all of which can qualify when standard approaches are insufficient.
As AI adoption accelerates, businesses are not only leveraging existing tools but pushing the boundaries of what these systems can achieve. This creates strong opportunities to claim SR&ED tax credits for eligible labour, subcontractors, and development work. Proper documentation of hypotheses, testing iterations, and technical challenges is critical to support these claims.
For innovative companies building AI-driven products or integrating machine learning into their operations, SR&ED can be a powerful source of non-dilutive funding to support continued development and scale.
Predictability of model behavior when fine-tuning Large Language Models (LLMs) on sparse, domain-specific proprietary datasets.
Achieving real-time inference latency (<100ms) on low-power edge devices without significant loss in model accuracy.
Developing novel algorithmic architectures to mitigate "catastrophic forgetting" in continuous learning systems.
