My responses to the third theme of the Canada AI Survey, focusing on the commercialization of AI.
This is part 3 of “Responses to the Canada AI Survey” - a series containing my responses to the eight themes of the Canadian government’s public consultation on artificial intelligence.
Theme 3: Commercialization of AI
Q1: What needs to be put in place so Canada can grow globally competitive AI companies while retaining ownership, IP and economic sovereignty?
If Canadian companies can have Canadian customers with whom to iterate and develop the product to achieve financial sustainability within Canada, they will be in a much better position to be globally competitive. Currently, most early stage Canadian start-ups, particularly those within regulated environments, look for traction outside of Canada because it is both easier with a better payoff in terms of both adoption and investments - most of which will then be foreign.
Q2: What changes to the Canadian business enabling environment are needed to unlock AI commercialization?
It is well known that early stage startups in Canada have an easier time raising money south of the border than in their own backyard. This needs to change. I’m unsure on how to increase the appetite of Canadian investors on this front. Someone should ask them why they wait for a company to gain traction in Silicon Valley before answering their emails.
Sectorally, opportunities should be identified by experts - from that sector. Places where money, time and other resources are wasted and where AI could play a big role. Perhaps tax credits could be granted for money saved by adopting technology - particularly if this capital is then redirected for growth or R&D.
Q3: How can Canada better connect AI research with commercialization to meet strategic business needs?
Academia is, except for rare instances, quite removed from immediate business needs. Today’s AI moment, powered by LLMs, genAI and computer vision are ready to be applied and are based on the academic work of the prior decade.
Domain experts who know everything about their domain but perhaps nothing about AI have a huge role to play. They are best positioned to identify problems that actually need solving (the nails) while they can team up with AI enablement experts to identify which hammer to wield. Many lessons can be learned from the application of CNNs to radiology, where workflow considerations were paramount, as they will be in other domains.