Artificial intelligence has become a central focus in business discussions. Organizations are exploring automation, generative tools, and digital transformation initiatives, often under pressure to keep pace with competitors and technological change.
However, alongside this momentum is a longer-standing issue in Canada: relatively weak productivity growth.
Productivity as a structural constraint
Productivity, typically measured as output per hour worked, has grown more slowly in Canada than in several peer economies, particularly the United States. Data from Statistics Canada and the OECD have consistently highlighted this gap over the past two decades.
In practical terms, slower productivity growth is associated with:
- Limited ability to scale without proportional cost increases
- Wage growth that may not keep pace with living costs
- Reduced competitiveness in global markets
Across many organizations, operational challenges contribute to this issue. Common examples include fragmented systems, high administrative burden, duplicated reporting, and workflows that have not been updated to reflect current business requirements.
These factors point less to individual effort and more to structural and process-related constraints.
The role of AI in productivity improvement
AI has the potential to improve productivity by:
- Reducing time spent on repetitive or manual tasks
- Accelerating information processing and access
- Supporting faster or more consistent decision-making
However, research and industry experience indicate that technology adoption alone does not guarantee productivity gains. Outcomes depend heavily on how tools are implemented and integrated into existing workflows.
In some cases, organizations adopt AI in response to external pressure rather than clearly defined operational needs. This can result in limited measurable impact, particularly when underlying processes remain unchanged.
Organizations that report stronger results tend to begin with specific operational questions, such as:
- Where is time being lost in current workflows?
- Which processes introduce delays or rework?
- What tasks are repetitive and rule-based?
- Where could decision-making be improved with better data or speed?
AI is then evaluated as one potential solution, rather than the starting point.
Evidence-based implementation approach
An evidence-based approach to AI adoption emphasizes measurable outcomes and controlled implementation. Common elements include:
- Identifying a workflow with clear inefficiencies and measurable impact
- Establishing baseline performance metrics
- Testing a focused use case within a defined scope
- Measuring results over a fixed period
- Expanding only where outcomes demonstrate clear value
This approach aligns with findings from organizations such as the OECD and MIT, which emphasize that productivity gains typically result from combining technology with process redesign, workforce training, and effective change management.
Observed benefits in successful implementations often include:
- Reduced time spent on documentation and reporting
- Faster response or processing times
- Lower levels of manual or repetitive work
- Reallocation of effort toward higher-value activities
Addressing the broader productivity gap
Improving productivity at the organizational level generally involves:
- Redesigning workflows to reduce friction and redundancy
- Aligning technology investments with specific operational needs
- Making decisions based on measurable outcomes rather than external pressure
Access to advanced tools alone is not a sufficient differentiator. The impact depends on how effectively those tools are applied within existing business processes.
A practical starting point
A focused, incremental approach can help organizations evaluate potential gains:
- Select a workflow with visible inefficiencies
- Define baseline metrics such as cycle time, error rates, or cost per transaction
- Map the current process and remove unnecessary steps
- Introduce a targeted AI-supported improvement
- Measure results over a defined period (for example, 4 to 8 weeks)
- Scale only if improvements are supported by data
Relevant metrics may include:
- Time required per task
- Turnaround or resolution time
- Error and rework rates
- Cost per unit of output
- Employee and customer satisfaction
- Output per employee
Further exploration
For organizations assessing their current position or next steps:
- Read the Evidence-Based AI book: Framework provides a structured approach to implementation
- Take our AI Readiness Assessment : AI readiness assessments can help benchmark current capabilities
- Talk to the BRUKD team : Advisory support can assist in identifying high-impact, compliant use cases