The Impact of Technology on Canada’s Mining Workforce
Oct 13, 2026 | Publication
An executive summary of key findings
Key Findings: At a Glance
At a Glance
Canada’s mining industry is entering a new era of technological transformation. Emerging technologies, including automation, artificial intelligence, remote operations, advanced analytics, robotics and digital monitoring systems are beginning to proliferate across mining operations. These technologies have the potential to significantly reshape workflows and skill requirements across Canada’s mining labour market.
The Impact of Technology on Canada’s Mining Workforce explores how emerging technologies could reshape mining work, the forces accelerating or delaying their adoption and the implications for the workforce.
Report Key Findings
- Analysis identifies 70 emerging technologies that have the potential to impact Canada’s mining workforce.
- The technologies were evaluated for their ability to disrupt tasks across 21 critical mining occupations.
- Tasks can be disrupted through four channels: Automation, Augmentation, Reconfiguration and Task Creation.
Workers retain at least half of all tasks in every occupation
Even with full deployment, automation reaches no more than half of the task bundle in any occupation.
Automation exposure is high for some of mining’s largest occupations
Some of mining’s largest occupations face the highest automation exposure, including Underground Miners and Support Workers, Heavy Equipment Operators and Central Control and Machine Operators.
Supervisory and frontline occupations face the highest reconfiguration exposure
Exposure to reconfiguration (i.e., changing where or how work is organized) is highest among Supervisors, Drillers and Blasters and Underground Miners and Support Workers.
Adoption can greatly reduce realized technology exposure
Across all occupations, workers retain at least 63% of all tasks under the high adoption scenario and 82% of tasks under low adoption.
Adoption changes the balance between automation and augmentation
Adoption reduces both automation and augmentation exposure, but not in the same way for every occupation.
Reconfiguration is highly sensitive to adoption
As adoption slows, occupations shift toward much lower levels of realized exposure. Those that have the highest possible exposure generally see the largest reductions.
Section 01: The Mine of the Future
What technologies are expected to be most transformative for the mining industry in 2026 and beyond?
MiHR identified 70 emerging technologies across five categories being implemented in Canada’s mining industry, ranging from autonomous haulage and drilling systems to remote operations centres, predictive maintenance and generative AI decision support. These technologies were selected for their potential to transform mining operations in the near term, based on a review of literature and validation with industry stakeholders.
Explore the 70 Emerging Technologies
70 emerging technologies grouped into five categories, their mining utility clusters, and individual technologies with definitions.
Search, or click a category to browse its clusters and technologies.
What do emerging technologies mean for the future of mining work?
Collectively, emerging technologies have the potential to reshape how work is performed, how decisions are made and how workers interact with equipment, data and one another. While individual technologies affect mining operations in different ways, their combined impact reveals several broad themes.
The Mine of the Future: Common Themes
Drawn from MiHR’s literature review and key informant interviews.
Click a card to read more.
Section 02: Scope of Technological Disruption
If fully deployed, what is the possible exposure of mining occupations to technology?
The analysis’ methodology breaks down each critical mining occupation into a bundle of tasks. Each task is evaluated against the 70 emerging technologies to determine the potential for disruption. The analysis develops a measure of possible exposure, which defines the maximum extent to which emerging technologies could automate, augment or reconfigure mining tasks if fully deployed across the industry. Possible exposure represents what is technically possible before adoption barriers, costs and other practical constraints are considered.
Four Channels by Which Technology Reshapes Work
Technology can affect a task through more than one channel at once — separating them shows specifically how.
Automation
Technology executes the core productive function of the task; the worker’s role becomes minimal, supervisory or unnecessary.
In practice
An Autonomous haulage system (AHS) runs the haul end to end, moving the operator from driving to oversight.
Augmentation
The worker still performs the task, but technology makes the remaining human work faster, safer or more accurate.
In practice
Fleet management and dispatch systems do not drive the truck but make the driver more productive through better routing and load matching.
Reconfiguration
The task survives and the worker still performs it, but where, how or by whom it is done changes.
In practice
The operator controls the truck from an Integrated operations centre (IOC) rather than the cab, running the same haul from a relocated setting.
Task Creation
Technology generates new tasks that did not exist before deployment, in response to the new capabilities and demands it introduces.
In practice
Autonomous and connected trucks create new work in remote monitoring, fleet oversight and exception handling.
★ = channels where headcount may be affected
Source: Mining Industry Human Resources Council, The Impact of Technology on Canada’s Mining Workforce (2026).
Workers retain at least half of all tasks in every occupation
The chart illustrates the share of tasks in each mining occupation that could be automated, augmented by technology or remain unchanged.
Even with full deployment, automation reaches no more than half of the task bundle in any occupation. Exposure to automation tends to be highest in equipment and material handling roles and lowest in occupations focused on planning, coordination and oversight. For several managerial, engineering and trades occupations, technology is much more likely to enhance workers than replace them.
Possible Exposure:
Machine and Human Share of Task Bundle by Occupation
Technical ceiling at full technology deployment across all 70 technologies combined. Occupations are ordered by automation share.
Search, or select occupations and categories to highlight them.
Source: Mining Industry Human Resources Council, The Impact of Technology on Canada’s Mining Workforce (2026); Statistics Canada, Census (2021); US Department of Labor, Employment and Training Administration (USDOL/ETA), Occupational Information Network (O*NET) database (2026).
Automation exposure is high for some of mining’s largest occupations
The chart compares the potential for automation and augmentation across mining occupations. Production occupations generally face higher automation potential, while professional and supervisory roles are more likely to be augmented by technology.
Some of mining’s largest occupations face the highest automation exposure. Underground Miners and Support Workers (~13,000 workers), Heavy Equipment Operators (~11,100) and Central Control and Machine Operators (~6,600) all sit on the automation-dominant side of the chart.
Possible Exposure:
Automation and Augmentation Shares of the Task Bundle by Occupation
Each bubble is one occupation, sized by workforce. The right-hand quadrants mark higher automation exposure; the upper quadrants mark higher augmentation exposure.
Hover a bubble for details, or search and select occupations and categories to highlight them.
Source: Mining Industry Human Resources Council, The Impact of Technology on Canada’s Mining Workforce (2026); Statistics Canada, Census (2021); US Department of Labor, Employment and Training Administration (USDOL/ETA), Occupational Information Network (O*NET) database (2026).
Supervisory and frontline occupations face the highest reconfiguration exposure
Automation shifts task execution to machines, augmentation helps workers perform tasks, while reconfiguration changes where or how work is organized. Tele-remote operations, for example, can move equipment operators from the mine site to a remote operations centre without removing the worker from the task.
The chart shows the share of tasks in each occupation that could be reconfigured by emerging technologies. Reconfiguration exposure is highest among Supervisors, Mining and Quarrying (61%), followed by Drillers and Blasters (54%) and Underground Miners and Support Workers (49%). At the other end, Industrial Electricians (22%) and Mine Labourers (23%) have the lowest exposure.
Possible Exposure:
Reconfiguration Share of the Task Bundle by Occupation
Each bubble is one occupation, sized by workforce and coloured by occupational category.
Hover a bubble for details, or search and select occupations and categories to highlight them.
Source: Mining Industry Human Resources Council, The Impact of Technology on Canada’s Mining Workforce (2026); Statistics Canada, Census (2021); US Department of Labor, Employment and Training Administration (USDOL/ETA), Occupational Information Network (O*NET) database (2026).
Section 03: Pace of Technological Adoption
How widespread is technology adoption across the sector, and what factors act as key barriers or accelerators?
Just because a transformative technology exists does not mean it gets adopted. Mining operations ultimately decide whether to adopt a technology by weighing its expected benefits against the costs, practical challenges and obligations associated with deployment. Without accounting for these factors, the potential scope of disruption would greatly overstate the changes the workforce is likely to experience.
Scoring Framework for Adoption Factors
Each factor is scored on a standardized 0–3 scale. A high score indicates a strong pull toward adoption for an accelerator and a significant barrier to adoption for a decelerator. First-principles factors are scored against a rubric that defines each measure and establishes criteria for assigning scores from 0 to 3, ensuring scores are comparable across all technologies and auditable against consistent criteria. Empirical factors are scored using labour market data selected to represent each underlying concept, then normalized to a 0–3 scale to allow comparison across all factors.
Adoption accelerators — factors that encourage technology uptake by making deployment more attractive
Technical Maturity
Measures a technology’s readiness for deployment. Technologies receive the highest scores when they are commercially available, field-proven and deployable today.
Safety Improvement Potential
Measures a technology’s capacity to reduce the risk of worker injury or fatality. Technologies that can physically remove workers from hazardous situations receive the highest scores.
Productivity
Measures a technology’s ability to increase output from a given set of inputs, such as labour, capital, energy and materials. Technologies receive the highest scores when they substantially increase productivity at a key stage of mining operations.
Environmental Benefit
Measures a technology’s capacity to reduce an operation’s environmental footprint, considering greenhouse gas emissions, energy and water use, tailings and waste, land disturbance and dust. Technologies with the highest scores deliver stronger environmental outcomes that address regulatory requirements, support ESG objectives and strengthen the social license to operate.
Labour Shortage Mitigation
Measures a technology’s potential to reduce reliance on occupations that are difficult to recruit and retain. Technologies receive the highest scores when they can substitute for or reduce labour demand in occupations facing the most significant labour shortages.
Adoption decelerators — barriers that discourage technology uptake by making deployment more difficult or costly
Capital Intensity
Measures the upfront investment required to deploy a technology at meaningful scale within a mining operation. Technologies receive the highest scores when adoption requires substantial capital investment to integrate the technology into routine operations.
Infrastructure Compatibility
Measures the difficulty of integrating a technology into an existing mining operation. Technologies receive the highest scores when adoption requires costly changes to existing infrastructure, equipment and operational processes.
Support & Supply Chain Fragility
Measures the challenges of sustaining a technology throughout its operating life. Technologies receive the highest scores when ongoing operation depends on specialized OEM support, scarce replacement parts, long lead times or remote service networks.
Regulatory Complexity
Measures the approval and compliance burden associated with deploying a technology. Technologies receive the highest scores when adoption involves extensive permitting, multiple regulatory authorities or uncertainty around approval requirements.
Workforce Transition Complexity
Measures the degree of workforce adaptation required to introduce a technology into mining operations. Technologies receive the highest scores when adoption requires substantial training and significant changes to existing skills, roles or workplace practices.
Socio-political Exposure
Measures the broader workforce and community implications associated with potential labour displacement. Technologies receive the highest scores when the workers potentially affected are concentrated among Indigenous workers, unionized employees or mining-dependent communities, where displacement may have broader implications for communities, governments and labour organizations.
Source: Mining Industry Human Resources Council, The Impact of Technology on Canada’s Mining Workforce (2026).
The path to adoption is easier for some technologies than others
The table brings together five adoption accelerators and six decelerators to provide a complete picture of the forces influencing technology adoption.
Each technology faces its own combination of incentives and barriers, creating a distinct set of conditions for adoption. Some technologies may benefit from strong safety and productivity incentives but also face significant implementation challenges.
For example, Autonomous & Robotic Systems show both strong adoption pull and substantial barriers. By contrast, Data & Intelligent Analytics generally combine strong incentives with fewer barriers, creating a more favourable environment for adoption.
Adoption Factor Scorecard by Technology Category and Mine Utility Cluster
Bringing all five accelerators and six decelerators together shows where the push toward adoption is strongest, and where the barriers are highest. Each dot is one point on the 0–3 scale; a half-filled dot marks a score of x.5 or more. Category and cluster rows are the mean across every technology in the group. Drill down to see each of the 70 individual technologies.
Search, or click a category then a cluster to drill down to individual technologies.
| ACCELERATORS — adoption pull | DECELERATORS — adoption barrier | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Tech. Maturity | Safety Improve. | Product- ivity | Environ. Benefit | Labour Shortage | Capital Intensity | Infra. Compat. | Support Fragility | Regulatory Complex. | Workforce Transition | Socio- political | |
= 2.5 of 3 · green dots = accelerator (pull), red dots = decelerator (barrier)
Source: Mining Industry Human Resources Council, The Impact of Technology on Canada’s Mining Workforce (2026), Adoption Factor Scoring workbook (Summary by Category / by Cluster / by Technology).
Adoption can greatly reduce realized technology exposure
The chart illustrates realized exposure by occupation, showing how the possible exposure ceiling changes once technology adoption is applied under three scenarios.
The scenarios show how sensitive technology exposure is to the pace of adoption. Under the low-adoption scenario, at least 82% of tasks remain with workers across all occupations, while no more than 18% are automated. Even under the high-adoption scenario, at least 63% of tasks remain with workers.
Realized Exposure:
Machine and Human Share of Task Bundle by Occupation
Technical ceiling at full technology deployment (Possible Exposure), across all 70 technologies combined. Occupations are ordered by automation share.
Search, or select occupations and categories to highlight them, or switch the adoption scenario below.
Source: Mining Industry Human Resources Council, The Impact of Technology on Canada’s Mining Workforce (2026); Statistics Canada, Census (2021); US Department of Labor, Employment and Training Administration (USDOL/ETA), Occupational Information Network (O*NET) database (2026).
Adoption changes the balance between automation and augmentation
The chart compares automation and augmentation exposure for selected occupations under different technology adoption scenarios.
Adoption reduces both automation and augmentation exposure, but not in the same way for every occupation. For Managers in natural resources production, the reduction occurs almost entirely through augmentation. By contrast, production occupations such as Material Handlers and Labourers in mineral and metal processing see larger reductions in automation exposure.
Realized Exposure:
Automation and Augmentation Shares of the Task Bundle by Occupation
Each dot is one occupation, positioned by automation and augmentation exposure at full deployment. Axes and quadrant lines are fixed to the Possible Exposure view.
Hover a bubble for details, search or select occupations and categories to highlight them, or switch the adoption scenario below.
Source: Mining Industry Human Resources Council, The Impact of Technology on Canada’s Mining Workforce (2026); Statistics Canada, Census (2021); US Department of Labor, Employment and Training Administration (USDOL/ETA), Occupational Information Network (O*NET) database (2026).
Reconfiguration is highly sensitive to adoption
The chart compares reconfiguration exposure for selected occupations under different technology adoption scenarios. As adoption slows, occupations shift toward much lower levels of realized exposure, with those that have the highest possible exposure generally seeing the largest reductions.
For example, Supervisors, Mining and Quarrying have 61% possible exposure, but this falls to 41% under the high-adoption scenario and to less than 20% under low adoption. The potential to change where, how or by whom work is performed is much greater than what may ultimately be realized in practice.
Realized Exposure:
Reconfiguration Share of the Task Bundle by Occupation
Each dot is one occupation, positioned by reconfiguration exposure at full deployment. The axis is fixed to the Possible Exposure view.
Hover a bubble for details, search or select occupations and categories to highlight them, or switch the adoption scenario below.
Source: Mining Industry Human Resources Council, The Impact of Technology on Canada’s Mining Workforce (2026); Statistics Canada, Census (2021); US Department of Labor, Employment and Training Administration (USDOL/ETA), Occupational Information Network (O*NET) database (2026).
Takeaways
What the findings mean for the mining workforce
Two conclusions cut across every chapter of this analysis: technology’s ceiling on mining work is real but bounded, and how much of that ceiling gets reached depends on forces largely outside the technology itself.
Report Takeaways
Two overarching conclusions from this analysis.
Click a conclusion to expand it.
1) The future of mining work still depends on people
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No occupation in the analysis loses more than half of its task bundle to automation. Even Material Handlers, the occupation with the highest automation exposure, retain roughly half of their tasks.
Many of the largest technology impacts occur through augmentation and reconfiguration, changing how work is performed rather than eliminating it altogether. The future of mining work is one of transformation, not substitution.
2) Potential becomes impact only through adoption
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The technical potential of a technology matters only to the extent that it is adopted. Once adoption incentives and barriers are considered, technology’s impact on the workforce falls well below its technical ceiling.
Technology exposure is therefore bounded twice: first by what technologies can technically do, and again by how much of that potential is adopted.
Taken together, the findings point to substantial changes in how mining work may be performed, but not to wholesale replacement of workers across occupations.
