CBJ JUNE 2026

17 CANADA’S AI ECONOMY: GOVERNANCE, TRUST, AND THE FUTURE OF WORK JUNE 2026 « The Canadian Business Journal 16 transformation, which primarily focused on storing and distributing information, AI systems increasingly generate content, make predictions, and influence outcomes. Their growing role in areas such as hiring, lending, healthcare, and customer service means that decisions once made solely by humans are now shaped by algorithms, raising new questions about accountability and oversight. Determining responsibility can be challenging when AI systems contribute to consequential decisions. Accountability may be shared among developers, organizations deploying the technology, and the individuals relying on its outputs. The challenge is further complicated by the limited transparency of many advanced AI models, where the reasoning behind recommendations is not always easily explained. For organizations, this creates operational, legal, and reputational risks, particularly in sectors where fairness, accuracy, and explainability are essential. Existing regulatory frameworks were not designed for this level of automation. Earlier digital regulations focused largely on data privacy, online content, and platform responsibilities. AI systems operate differently because they actively generate content and recommendations, creating uncertainty around issues such as liability, transparency, and governance. In Canada, as in many jurisdictions, businesses currently navigate a mix of evolving guidelines, privacy laws, and sector-specific regulations rather than a single comprehensive framework. For business leaders, the challenge extends beyond compliance. Trust is increasingly becoming a strategic asset. Organizations that establish clear governance structures, maintain human oversight, and demonstrate responsible AI practices are likely to earn greater confidence from customers, employees, investors, and regulators. As AI adoption accelerates, effective governance may prove to be as important to long-term success as the technology itself. The labour market adjustment problem The central challenge for Canada is not whether AI will create or eliminate jobs in aggregate, but how quickly the labour market can adjust to changes in job structure. Several frictions make this adjustment complex. The first is skills mismatch. The skills required for emerging AI assisted roles are different from those in traditional administrative and routine cognitive work. Retraining takes time, and education systems often lag behind labour market changes. The second is geographic immobility. Job growth in AI and technology tends to be concentrated in specific urban centres, while housing costs and regional differences limit worker mobility.The third is the entry level bottleneck. If fewer junior roles exist in professional sectors, it becomes harder for workers to enter and progress through traditional career pathways. This could have long term implications for income distribution and social mobility. These factors together suggest that the most significant risk is not sudden unemployment, but persistent underemployment or slower wage growth for certain segments of the workforce. The rise of independent and flexible work One of the countervailing trends in the AI economy is the rise of independent work. AI tools reduce the cost of starting and operating small businesses or solo professional practices. Tasks that once required teams, such as marketing, administration, and content production, can increasingly be handled by individuals with AI assistance. This enables a more fragmented labour market where some workers operate as independent consultants or micro businesses.