AI looks North: Bridging Canada’s corporate artificial intelligence gap

From high tech to retail, from mining to transportation, artificial intelligence (AI) is fast becoming one of the most promising forms of digital disruption. Early adopters are seeing real benefits that range from reducing operational costs by streamlining processes to building lucrative new AI-based businesses. And fast-followers are developing strategies to leverage AI opportunities while guarding against AI-driven competition from non-traditional sources.

Industry leaders are beginning to envision a future shaped by AI. The insurance sector is starting to think about how AI-guided autonomous vehicles will impact auto insurance as the number of accidents and private ownership declines. Forward-thinking retail companies are beginning to envision a time when AI’s predictive capabilities—fed by a torrent of consumer data—will become so accurate that products can be shipped before customers order them. Tomorrow’s leaders in the financial industry are preparing for an environment in which few products will be standardized, and offerings will be calibrated to individual needs, guided by AI-informed customer profiles.

These changes are coming, and all companies should be preparing themselves.

In Canada, which pioneered many foundational advances in deep learning research and possesses a rich AI talent base, the question confronting businesses is whether they can reap AI benefits commensurate with the nation’s historic academic leadership. Right now, big players such as Google, Facebook, and Microsoft are coming to Canada to leverage its deep talent pool. But will Canada’s pioneering AI history translate into accelerated technology advances in its own businesses?

To find out, McKinsey & Company surveyed 120 leading Canadian executives and interviewed 31 business leaders in depth. We found an encouraging amount of enthusiasm for AI—and a willingness to take even bolder actions to search for AI value. We uncovered valuable lessons from Canadian leaders that can be applied to other businesses that are struggling or have yet to embrace AI. However, we also found notable gaps in business leaders understanding of AI’s potential, and its impact on the value chain, which has manifested itself in a paucity of truly impactful initiatives. If Canada cannot mobilize aggressively to tap its wealth and talent, others will—and Canadian businesses will risk finding themselves at an enduring competitive disadvantage.

The report distills our findings and anecdotes into a series of targeted recommendations tailored to Canadian business leaders (exhibit).

Our research uncovered three key gaps between Canada’s AI aspirations and activities:

• Strategy does not match expected impact. Although 89 percent of Canadian business leaders believe AI will create major, positive change within three to five years, only 34 percent have transformed their longer-term corporate strategies to position themselves appropriately to seize AI’s potential benefits.
• The basics of AI are not widely understood. Although 82 percent of survey respondents reported they currently use or invest in sophisticated AI applications—deep learning, reinforcement learning, neural networks—interviews revealed current use cases focus nearly entirely on traditional analytics—dashboards and statistics-based analysis.
• Current AI applications are not transformative. Most businesses that are currently exploring AI’s benefits often focus on a small number of non-core use cases. However, transformative, value-chain targeting experiments are often underutilized.

Artificial intelligence: The next digital frontier

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While these responses align with early stage AI maturity, Canadian business leaders need to accelerate their efforts to fully participate in this next digital revolution. They can do this by implementing the following:

• Become fluent in AI—driving alignment at the senior level on the potential of AI. A formal effort must be taken to ensure that all top-level executives are well-versed in the disruptive powers of an AI transformation.
• Reimagine the business as a tech company—determining where AI can support business objectives and reimagining processes by finding the relevant value drivers. The focus should be on changing the business, rather than only improving current assets. If new ideas are not disruptive, they are not bold enough.
• Build a digital and analytics backbone—building the foundational analytics and digital structures, capabilities, and policies. AI cannot exist in a vacuum. It can best succeed by building on existing capabilities.
• Experiment and scale AI applications—developing proofs of value and quickly scaling the best. The current rate of experimentation needs to be accelerated by moving from two to three small scale experiments to ten or more executed simultaneously across the organization.
• Embed analytics and analytics-to-business “translator” talent—ensuring the company has the right talent to execute its AI strategy. Businesses must re-train or recruit analytics-to-business “translators” and make strategic decisions about the degree of in-house versus vendor development talent. To maximize its impact, AI cannot be siloed. Businesses will need to consider and plan for new career paths and roles.
• Manage human changes during the AI transformation—communicating the upcoming changes broadly and clearly to ready the business and its people for the transformations to come. AI transformations are as much about people as they are about technology.

Download AI looks North: Bridging Canada’s corporate artificial intelligence gap (PDF—2MB) in English and Les entreprises Canadiennes et l'intelligence artificielle: Libérer le plein potentiel de l'ia au Canada (PDF—2MB) in French.