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Steve Irvine is the founder and CEO of integrate.ai, adviser to the Vector Institute for Artificial Intelligence and board member for the Centre for Aging and Brain Health Innovation.

One year ago, I left my executive role at Facebook in Silicon Valley to return to Canada and build integrate.ai, an AI-focused startup. I was convinced that Canada was the best place in the world to build a global-leading AI business and I was ready to prove it.

A lot has happened in the past year, and the sheer volume of AI stories has been overwhelming. With so much commentary, it can be difficult to get a clear perspective on how Canada is doing in pursuit of becoming a world leader in AI and where we should focus to win.

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Here are three points that business, government and all citizens should be discussing as AI moves into the mainstream of our technological future:

What winning in AI looks like for Canada and why should we care?

PWC estimates that AI will add US$15.7-trillion ($19.4-trillion) to the global economy by 2030. It is the biggest opportunity to expand the Canadian economy, alleviate our current dependency on natural resources, evolve to new technology-driven industries and create a prosperous future .

There's reason to be optimistic, thanks to the foresight and investment by the Canadian Institute for Advanced Research (CIFAR) in AI research decades ago. Canada did a lot in 2017 to build on our AI research advantage with more than $300-million in new funding for research and $260-million raised by Canadian AI startups. We have also seen global tech giants, including DeepMind, Facebook, Samsung, and Uber establish AI research labs in Canada.

How do we measure up to other countries?

While Canada is picking up speed, other countries are turning on the turbo chargers.

China plans to be a world leader in AI by 2030 and recently committed $2.5-billion to a national AI research park in Beijing with a goal of supporting 400 companies producing revenue of 50 billion yuan ($9.8-billion) a year. Considering China's large population, lax privacy regulations and aggressive AI talent hiring from technology giants Alibaba, Tencent and Baidu, there is good reason to believe China will capture the largest piece of the global AI market opportunity.

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The United States remains home to the world's leading AI companies including Google, Facebook, Apple, Amazon, a host of well-funded startups and strong universities including Carnegie Mellon, Stanford and MIT.

Even Estonia, with just 1.3 million people, was recently featured in The New Yorker for its world-leading data infrastructure: every citizen has an ID card that links to all government services. This type of open and consistent data structure is the foundation for applied AI.

Where do we need to focus in 2018 to win?

It is essential that Canada starts to prove it can translate an established research advantage into an economic advantage by shifting focus more aggressively to commercialization from academic prowess.

Most notably absent in Canada are homegrown, global-leading technology companies to capture the economic growth associated with this opportunity. We need at least a handful of $10-billion-plus companies, such as Shopify, to help create the commercial density and global relevance needed to attract and retain elite talent, create new high-value jobs and capture an outsized economic gain.

To accelerate toward commercialization, we need to focus on removing the friction our most promising AI scale-ups are encountering in their paths to global leadership.

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Commit to a meaningful AI project with a promising startup tackling a meaningful business problem with a real budget and stay connected to it. Get strong people on it, pay them on time, don't tie them up for months in legal or procurement and work until you figure it out. There is no better learning agenda for your team than rolling up their sleeves and seeing this new technology in practice delivering results for you. If you are in government, focus on simplification. Startups need to grow revenues quickly to compete globally and scale up their impact. How can your incentives tie directly to that goal and generally minimize red tape? Speak with the leaders of these startups about which current regulations to update.

Finally, let's acknowledge that this is not all going to be easy, and we can't leave the majority of Canadians behind, but the world is not going to wait for us, either. Industries will be reshaped, some jobs will change, more tasks will be automated and there are real issues to tackle.

The race for global AI leadership can be compared with an Olympic 4 x 100-metre relay race. The start is crucial and you want to get out to an early lead, which we have done this with our research advantage. However, you don't get a medal for leading after the first leg. We have to sustain our advantage and find ways to extend it because we are racing against countries that are only going to get stronger and faster the further we get in the race. The baton needs to pass from research to commercialization seamlessly, and by the time we get to the anchor leg, we need startups and scale-ups to be there ready to bring us to the finish line.

Only then can we claim our place on the artificial-intelligence podium.

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