With abundant resources, cutting-edge technological expertise, and a growing commitment to decarbonization, Canada is positioning itself as a key catalyst in the global energy transition. To maintain this strategic position and meet both climate and economic imperatives, the country can rely on AI as a pivotal lever to modernize energy production, distribution, and consumption.
Energy + AI: Use Cases for a Successful Transition
The integration of AI into Canada’s energy sector is taking place on multiple levels, with concrete use cases driven by local companies and government initiatives. These innovations target operational optimization, infrastructure security, environmental resilience, and technological sovereignty.
Smart and Resilient Power Grids: AI is already being used to model energy demand in real time, anticipate consumption peaks, and reduce the risk of overloads. Combined with advanced sensors and monitoring systems, it enables predictive maintenance that detects technical failures before they occur, thus minimizing service interruptions. BluWave-ai exemplifies this approach: the company works with utilities in Ontario and New Brunswick to optimize grid operations through AI, notably in load management and the integration of renewable sources.
Data-Driven Renewable Energy Production: Artificial intelligence helps offset the natural intermittency of hydropower, solar, and wind energy. By leveraging high-frequency weather forecasting systems combined with historical data, AI enables dynamic management of energy supply. Hydro-Québec, for example, is experimenting with using second-life batteries to stabilize solar energy flows in local grids. These solutions maximize the value of existing infrastructure while accelerating renewable adoption.
Value Chain and Critical Resource Traceability: Canada is a strategic player in the extraction of critical minerals such as lithium, nickel, and cobalt. AI makes it possible to ensure full traceability of these resources—from extraction sites to recycling—while assessing environmental risks. Geosapiens, a Québec-based company, develops predictive modeling tools to anticipate natural hazards, optimize logistics, and minimize ecological impacts.
Energy Efficiency and Optimized Local Consumption: AI also improves energy consumption at industrial and community levels. Automated management systems control flows in real time, identify waste, and suggest targeted adjustments. In several regions, cooperatives supported by the Rural Electrification Fund have adopted smart platforms to distribute consumption fairly within communities, fostering greater energy self-sufficiency.
An Industry in Transition: Between Progress and Fragmentation
Despite tangible progress, several challenges still hinder the widespread adoption of AI in the energy sector. A recent study by the Council of Canadian Innovators highlights a lack of coordination between provinces, unequal access to technology, and only partial adoption within local supply chains.
Data governance, the scarcity of AI expertise applied to energy, and regulatory constraints also slow the scaling of many initiatives. As Sreedhar Sistu, Vice President of AI Offers at Schneider Electric, stated on stage at ALL IN 2024 (translated from English): “In my opinion, the first challenge we face with AI compared to other technologies is that expectations for AI are now extremely high. As a result, anything that comes to market will, by definition, be perceived as underperforming.”
6 Ways to Accelerate AI Adoption in Canada’s Energy Sector
To accelerate the adoption of homegrown AI in the energy sector, stakeholders across our ecosystem can:
- Create cross-sector consortia bringing together businesses, universities, and governments to share use cases and reduce experimentation costs.
- Develop specialized training programs in AI applied to energy to address the shortage of experts.
- Standardize data formats and protocols to facilitate interoperability between players and provinces.
- Strengthen public-private partnerships to fund digital infrastructure and open data platforms.
- Encourage the repurposing of technologies developed in other sectors (transportation, industry) for energy applications.
- Deploy collaborative platforms to share AI results, learnings, and models in real time.
Energy Stakeholders: Why Choose Canadian AI?
In a sector as strategic as energy, choosing AI designed and developed in Canada is a tangible lever for performance and resilience. Local teams have in-depth knowledge of the country’s climate, regulatory, and infrastructure realities—from managing grids in remote areas to adapting to extreme seasonal variations.
This deep contextual understanding enables solutions that precisely address sector needs: optimizing the balance between production and consumption, adapting systems to the specificities of Canadian renewable sources, and improving service continuity even under challenging environmental conditions.
Adopting local AI also fosters direct collaboration with designers and technical experts, accelerating innovation cycles and the deployment of pilot projects. Data generated from Canadian operations can be integrated into models more quickly, ensuring predictive and operational tools better adapted to national energy realities.
Finally, investing in Canadian AI strengthens the country’s energy and technological sovereignty, supporting an ecosystem that promotes both business competitiveness and the transition to more sustainable practices.
A Dynamic Supported by Scale AI
Innovation clusters such as Scale AI play a structuring role in driving AI adoption in the energy sector. In 2024, Scale AI supported a project led by Cléo, a subsidiary of Hydro-Québec, aimed at optimizing the electrification of commercial vehicle fleets. The initiative relies on a machine learning algorithm capable of predicting usage cycles and detecting charging anomalies. These capabilities improve operational planning, enhance fleet reliability, and concretely support the transition to e-mobility in Canada.
Explore More at ALL IN 2025
These issues will be at the heart of discussions at ALL IN 2025, which will bring together more than 6,000 AI ecosystem leaders on September 24–25 in Montréal. Among the must-attend sessions: “AI & the Future of Energy: Enhancing Efficiency, Storage and Distribution”, which will explore concrete solutions implemented by industry leaders. Energy—as an industry at the crossroads of climate, technological, and social challenges—will be strongly represented through panels, demonstrations, and Canadian startups.
Join the conversation at ALL IN 2025
