Venture capital flows are a great source of strategic insights with respect of where certain markets are heading. Tech is one of those markets. And while our PwC MoneyTree report isn’t a crystal ball, every now and again, the data provides us with a very strong signal.

Canadian corporations shouldn’t wait for AI companies to knock on their doors.

This past quarter (Q2 2017), there was one trend that jumped right off the page: the rapid growth in artificial intelligence (AI) deals and funding. This is the first time deals in this burgeoning segment of the tech industry have been substantial enough to make the report. While the overall tech VC may be off to a bit of a slow start in 2017 in Canada, the AI sector is bucking that trend.

As I’ve shared before, there are a number of signs that Canada is laying the foundation for long-term AI success. We have the talent, world-class educational institutions, and top international researchers and academics. And as the report makes clear, we have a VC community that’s ready to put money behind the country’s best ideas.

But to see our Canadian AI sector lift off, we surely need a few other critical parts.

Release the data

Data is, of course, the feedstock of AI: the basic raw material that tech companies need to train all forms of AI technologies. And as with any industry, the more raw material that’s available, the more robust the manufacturing results will be.

This is why we need the stakeholders — governments, regulators, and business leaders — to lead us toward open data. What’s more, we need to make sure that access is afforded to deep enough data sets that AI teams can source the quality data they need to train the machines they’re building. Because here’s the thing: an algorithm without the benefit of training on data is just a line of code. With access to data, that same algorithm can become a powerful tool that can completely transform a business, an industry, and — through scale — our national capacity in this space.

To get there, we need a new way of thinking—a break from the traditional understanding of data as a trade secret, to data as an asset that, when properly stewarded, becomes valued at two times or 10 times. Can we arrive at data sharing as the ultimate win-win of a new, AI-driven economy? I’m hopeful.

Test the hypothesis

To that end, we’re starting to see Canadian enterprise come together in the spirit of a national project and share their data resources. The Vector Institute for AI has already set the stage by connecting corporates, academics, incubators and accelerators.

Another example of this is NEXT Canada’s NextAI program. They’ve partnered with Google and IBM Canada to support AI companies through funding, corporate mentorship, and the sourcing of datasets. Surely more shops will follow suit.

To succeed, enterprises need to find strategic ways to collaborate with new AI companies and integrate their technology into their business processes. While many corporates have solid data science and analytics capabilities, many others are still at square one(ish). In both cases, we need to seize the opportunity: let the AI teams in, let them work the data, and let’s see the results. This means incubation and cross-development of ideas, hands-on advising, acceleration of prototypes, and testing of algorithms on real-life business applications.

Canadian corporations shouldn’t wait for AI companies to knock on their doors; they need to actively look to do business with them, and commercial terms should be struck to fairly apportion ownership, upside, and risk.

The Canadian AI industry has begun to take flight. But to put more lift under the wings, we need to take action. Our leading corporates need to be more than bystanders watching the development of AI from the sidelines—they need to make strategic corporate investments and be eager, enthusiastic, early adopters of Canadian-born AI tech.

BetaKit is a PwC MoneyTree Canada media partner.