"Has your team adopted AI yet?"

Another spotlight put on Canadians for AI adoption. Another reminder that we have to move faster. Listen below as Steve Paikin does his best to figure out, why are Canadians so slow when it comes to AI?

If you or your organization is not sure of where to start with AI adoption, reach out to our AI bot to book a discovery call, or email me directly. We can create agents for SMS, email, and voice communications, and a whole lot more.

They key is starting small and building.

Because trying to do too much will lead to nothing getting done at all.

Or is that just the Canadian way XD?

Until next time.

Alex, Peak Demand

alex@peakdemand.ca

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A summary of the podcast is included below.

Original Podcast: Is Canada falling behind with AI? The Agenda with Steve Paiken

Link to TVO Podcast: https://www.tvo.org/podcasts/the-agenda-with-steve-paikin-audio/is-canada-falling-behind-in-ai

Summary: The podcast discussion, featuring experts from the University of Toronto and the Mars Discovery District, highlighted several key takeaways about the state of AI adoption in Canadian businesses:

1. Research Leadership but Commercialization Lag: Canada has established itself as a leader in AI research, but there's a notable lag in commercializing these innovations into successful business applications. This gap hinders the country's ability to fully leverage its advancements in AI technology.

2. Slow AI Adoption in Enterprises: Compared to the United States, Canadian enterprises are slower in adopting AI technologies. The KPMG study referenced in the discussion points out that while 72% of companies in the U.S. have adopted some form of AI, only 35% of Canadian companies have done so. This discrepancy is attributed to the structural makeup of Canadian industries, which are less focused on intangible, technology-driven markets compared to their American counterparts.

3. Challenges in Digital Transformation: Canadian businesses have historically been behind in digitizing their operations, including the adoption of cloud services and software as a service (SaaS). This has broader implications for AI adoption, as companies that lag in digitalization find it more challenging to integrate new technologies like AI.

4. Regulatory and Nontechnical Barriers: In sectors like healthcare, which could greatly benefit from AI due to its potential to improve efficiencies and outcomes, adoption is slow due to stringent regulations and complex incentives within the system. These nontechnical barriers significantly impede the deployment of AI technologies.

5. Need for Rapid Response and Investment: There's a call for more aggressive action and investment from Canadian stakeholders to catch up with global competitors in AI adoption. The discussion suggests that while the federal government's investment is a positive step, it may not be sufficient compared to the aggressive funding and development strategies seen in other countries.

6. Educational and Workforce Challenges: Integrating AI into the Canadian economy requires changes in education and workforce training. There's a need for new educational approaches that embrace AI and prepare the next generation of workers to effectively use these technologies.

7. Opportunity in Niche Areas: Despite these challenges, there are opportunities in niche sectors such as agriculture and healthcare, where AI can provide significant benefits, from improving farm yields to enhancing disease diagnosis.

Overall, while Canada has made substantial investments and shown leadership in AI research, there are significant hurdles to overcome in terms of commercialization and widespread adoption across industries. Addressing these challenges requires not only financial investment but also strategic changes in regulation, education, and industry practices.