My responses to the second theme of the Canada AI Survey, focusing on accelerating AI adoption by industry and government.
This is part 2 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 2: Accelerating AI Adoption by Industry and Government
Q1: Where is the greatest potential for impactful AI adoption in Canada? How can we ensure those sectors with the greatest opportunity can take advantage?
In industry and professional domains, AI adoption is wholly workflow-dependent. Many AI projects are already failing because of a failure to recognize this. Each vertical requires its own domain experts that are either well versed in AI capabilities or who can collaborate with those who are to identify areas of impactful AI adoptions.
I can speak to healthcare, where it is easy to imagine the great impact to finances, efficiency and care outcomes. However, there are many impactful technologies which don’t require AI that face steep barriers to adoption already in our healthcare systems. In other words, we must remove those barriers that prevent the adoption of standard technologies as well.
Q2: What are the key barriers to AI adoption, and how can government and industry work together to accelerate responsible uptake?
Any existing barrier to technology adoption will also slow down the adoption of AI. Many have probably already been identified by leaders in specific sectors. We should start there.
It is well known that consensus paralysis prevents many innovation projects from ever launching. A single no can stop an idea completely. We need a culture shift in this regard, especially in the public sector. Perhaps there should be a branded effort behind this effort - Elbows Up for Innovation.
Old software that stands in the way of innovation should be replaced. There will be a temptation to replace older, but still recent software with a version that is AI-enabled. But I believe there is more potential in using AI to replace software that is very old. The kind of software that only a handful of people are still touching to maintain. AI tooling should be used to rebuild it in a cost effective way, and AI should be used to interface it with other systems. Temporary stitching of such systems can now be cost effective with AI.
Multiple stakeholders, liability of rogue projects (CIOs not knowing what’s going on), accelerate update by incentivizing replacing old, old software where AI can be the glue that allows it to work with new software, or the tool that allows a replacement to be cost efficient. A lot of code is touched by the very few, let alone by someone equipped with AI. Government should procure home grown AI solutions.
Q3: How will we know if Canada is meaningfully engaging with and adopting AI? What are the best measures of success?
This is a dangerous question - we all know of metrics that became hindrances the moment they become targets.
Any metrics should be based on sector-specific measures of efficiency and satisfaction. Anything based on compute, tokens, api calls etc. will be unaligned with efficiencies. Further, there are sometimes non-AI solutions (or at least non-LLM) that should be adopted. After all, simpler but powerful statistical methods such as support vector machines were once considered AI.
There should also be metrics that relate to people as they become educated and employed in fields touched by AI - including how many leave.