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Updated May 2026

AI Grants and Funding Available in Quebec for 2026

Accelerate AI adoption with Quebec and Canadian funding. Find grants, tax credits, and co‑funding for research, pilots, and commercialization.

Across Quebec, organizations can access a dense ecosystem of artificial intelligence funding that supports research, technology adoption, skills, and commercialization. Programs range from Quebec ministry subsidies to federal grants and tax credits, with specific options for SMEs, corporates, and non‑profits. This directory outlines the main opportunities, eligibility patterns, and application practices to help you plan AI projects with confidence.

93 programs available

Frequently asked questions about AI grants in Quebec

Find clear answers to common questions about AI funding, eligibility, deadlines, and programs for SMEs, corporates, and non‑profits in Quebec.

What AI grants are available in Quebec for SMEs?

SMEs can access provincial programs for digital transformation and adoption, federal support like NRC IRAP, and cluster co‑funding such as Scale AI. They may also combine Mitacs internships and NSERC Alliance with Prompt Québec collaborations. RS&DE and the CDAE tax credits can complement grants when stacking rules allow. Review eligibility, match funding, and deadlines before applying.

How do I apply for AI funding in Quebec step by step?

Start with a clear problem statement and baseline KPIs. Select the right program stream (R&D, adoption, supply chain, workforce), build a realistic budget, and secure partner letters. Prepare IP and data governance terms, submit on time, and respond promptly to clarifications. Plan reporting and audit readiness from day one.

Which AI expenses are typically eligible?

Eligible costs often include salaries, subcontracting, software, cloud, GPU credits, compute hardware, data labeling, and training. Some programs allow commercialization, travel, and security/privacy costs. Always check caps, indirect cost rules, and documentation requirements.

Can I combine RS&DE with grants for AI projects?

Yes, many organizations stack RS&DE with grants, respecting stacking limits and avoiding double counting. Align time tracking and cost attribution to separate claimed work. Keep clear audit trails and coordinate with finance to maximize non‑dilutive support.

What are common AI grant evaluation criteria?

Review panels consider technical merit, team capacity, market impact, data governance, responsible AI, and measurable KPIs. Risk mitigation, partner commitment, and clear budgets improve scores. Include a pilot‑to‑scale plan and post‑award reporting approach.

Are GPU purchases and cloud credits eligible in Quebec AI programs?

Some programs accept GPU hardware and cloud/GPU credits when essential to the project scope and justified. Provide vendor quotes, workload estimates, and cost controls. Confirm eligibility, caps, and depreciation rules for equipment.

What is the difference between adoption and R&D AI funding?

Adoption support focuses on implementing proven AI tools, integration, training, and change management. R&D funding targets experimental development, new algorithms, and higher technical risk. Many roadmaps combine both streams across phases.

How long do AI grant decisions take in Quebec?

Timelines vary by program and call volume. Expect several weeks to a few months, with longer cycles for consortium projects. Build buffers in your plan and avoid committing to fixed start dates before approval.

Can nonprofits and public institutions access AI funding?

Yes, many programs support hospitals, municipalities, colleges (CCTT), universities, and nonprofits, especially for pilots and applied research. Eligibility and match ratios differ, so review program guides carefully.

What documents are required for an AI grant application in Quebec?

Prepare an executive summary, technical plan, budget, and timeline. Add partner letters, IP and data‑sharing terms, risk management, and KPIs. Include evidence of match funding and a reporting plan aligned with program requirements.

What else should I know about Grants and Funding for Artificial Intelligence in Quebec?

Overview: AI grants and funding in Quebec’s innovation ecosystem

Quebec hosts one of Canada’s most active artificial intelligence ecosystems. Organizations can combine provincial subsidies, federal grants, tax incentives, and consortium co‑funding to finance AI research, prototyping, adoption, and scale‑up. High‑intent opportunities exist for SMEs and large enterprises in manufacturing, healthcare, aerospace, fintech, logistics, agri‑food, and the public sector. Core mechanisms include non‑repayable contributions, cost‑sharing, wage subsidies, training support, tax credits, and innovation loans. This guide maps the landscape: Scale AI co‑funding, NRC IRAP support, Mitacs internships, NSERC Alliance research partnerships, Investissement Québec programs like ESSOR, sectoral funds such as Prompt, and cross‑cutting incentives like RS&DE and the CDAE digital/AI tax credit.

Why this matters for businesses and non‑profits

- AI grants in Quebec de‑risk R&D and technology adoption, turning complex roadmaps into staged pilots and production deployments.
- Funding can cover eligible salaries, subcontracting, cloud/GPU compute, data infrastructure, training, and commercialization planning.
- Consortium and university–industry programs align research with market needs, supporting proof of concept and pilot‑to‑scale pathways.

Main funding families for AI projects in Quebec

1) Federal grants and co‑funding relevant to Quebec

- Scale AI (supercluster) funding: co‑investment for supply chain AI, computer vision, NLP/LLM, forecasting, and smart manufacturing. Projects often involve multi‑partner consortia, cost‑sharing, measurable KPIs, and pilot‑to‑production trajectories.
- NRC IRAP AI funding: advisory and non‑repayable support for SMEs developing or adopting AI technologies. Typical use cases include prototyping, integration, and market validation with industrial research assistance.
- Mitacs AI funding: Accelerate, Business Strategy Internships, and Elevate streams connect companies with graduate students and postdocs, enabling applied research, data science, and MLOps projects.
- NSERC Alliance AI grants: university–industry partnerships for research and development, often combined with Mitacs internships and complementary provincial support.

2) Quebec government programs and agencies

- Investissement Québec (IQ): including the ESSOR program for modernization and digital transformation, often supporting AI equipment (e.g., servers, GPUs) and process automation.
- Sector and cluster programs: Prompt Québec (ICT/AI collaborations), regional development funds, and innovation vouchers through CCTTs (College Centres for Technology Transfer) to accelerate AI prototyping with cégeps.
- Workforce and training instruments: subsidies for AI skills development, workforce reskilling, and apprenticeship wage support for data and ML roles.

3) Tax incentives and credits

- RS&DE AI tax credit: non‑dilutive support for experimental development, algorithmic R&D, data pipelines, and related engineering work.
- CDAE digital/AI tax credit in Quebec: designed for eligible digital businesses; can complement grants when projects involve software and AI product development.
- Stacking strategies: organizations frequently combine RS&DE with grants while respecting stacking limits and avoiding double‑counting of eligible expenses.

Program snapshots: named opportunities searched most often

Scale AI funding in Quebec

Scale AI is a national supercluster with a strong footprint in Quebec and across Canada. It co‑funds collaborative AI supply chain projects covering demand forecasting, optimization, computer vision, NLP, and predictive maintenance. Organizations search for “Scale AI funding percentage,” “eligibility criteria for co‑funding,” and “pilot‑to‑scale AI funding Montreal.” In practice, successful applications define clear baselines, ROI assumptions, and milestones supported by data governance and MLOps readiness.

Typical eligible activities

- AI pilot projects and demonstration in manufacturing, warehousing, transportation, and retail supply chains.
- Data infrastructure and data governance workstreams: integration, labeling, quality, and privacy‑by‑design.
- Compute infrastructure and cloud/GPU credits when essential to the use case and authorized by program rules.
- Workforce development, including training on AI operations, change management, and safety and standards.

NRC IRAP AI funding (SMEs)

NRC IRAP supports Canadian SMEs in research and technology development. For AI ventures in Montreal, Quebec City, Sherbrooke, Laval, or Gatineau, IRAP advisors assess market potential, technical risk, and team capacity. Common long‑tail intents include “NRC IRAP AI funding for startups in Montreal,” “success rate IRAP AI Quebec,” and “documents required for IRAP applications.” Emphasis is placed on technical milestones, commercialization pathways, and credible budgets.

Mitacs AI internships and applied research

Mitacs connects companies with graduate talent for applied AI work: computer vision, NLP/LLMs, recommendation systems, edge AI, MLOps, and model validation. “Mitacs Accelerate AI funding for Quebec companies” and “AI internships funding for SMEs Quebec” are frequent searches. Projects often stack with NSERC Alliance or cluster grants when rules allow, covering supervision, IP strategy, and publication considerations.

NSERC Alliance for AI partnerships

NSERC Alliance funds university–industry collaborations in AI, spanning fundamental research to TRL advancement. Applicants prepare statements of work, IP clauses, and data sharing agreements. When paired with Mitacs, organizations can increase hands‑on capacity while maintaining academic rigor and responsible AI practices.

Investissement Québec and programme ESSOR for AI adoption

ESSOR helps finance modernization and digital transformation, including AI equipment purchases, integration costs, and technology adoption projects. Queries often address “GPU purchase eligible under ESSOR for AI,” “loan + grant hybrid AI,” and “AI for manufacturing Quebec.” Budgets typically include hardware, software licenses, integration services, and training.

Prompt Québec for ICT/AI collaborations

Prompt co‑funds collaborative R&D with companies and Quebec research institutions, emphasizing ICT and AI innovation. Use cases include 5G/edge AI, computer vision, cybersecurity + AI, and AI for games or media. Applicants define technical work packages, TRL progression, risk management, and commercialization strategy.

IVADO, Mila, and the Montreal AI ecosystem

IVADO seed grants and Mila industry programs support responsible AI, applied research, and knowledge transfer. Frequent searches include “IVADO grants,” “Mila funding opportunities,” and “AI ethics research grants.” These initiatives often complement federal/provincial programs and strengthen talent pipelines through internships and post‑doctoral funding.

Sectors: where Quebec organizations use AI funding

Health and life sciences

Hospitals, research institutes, and healthtech SMEs pursue “grants for AI proof of concept in health care Quebec,” “hospital AI pilot funding Quebec,” and “procurement of innovation AI pilots Quebec hospitals.” Common projects include diagnostic support, triage, scheduling optimization, and privacy‑preserving data environments (data clean rooms). Privacy, security, and compliance costs are frequently eligible.

Manufacturing and smart industry

“Smart manufacturing AI grants,” “manufacturing AI automation grants Quebec City region,” and “AI for the manufacturing sector in Quebec” reflect strong demand. Funded work covers machine vision for quality, predictive maintenance, supply chain optimization, robotics, and MLOps for production environments.

Aerospace, fintech, energy, and transportation

- Aerospace: “aerospace AI research funding Montreal” supports predictive maintenance, avionics data analytics, and digital twins in Greater Montreal’s aerospace cluster.
- Fintech: “fintech AI funding Quebec” addresses fraud detection, AML, and risk modeling with explainability and governance components.
- Energy and transportation: funded projects optimize grid operations, electric fleet routing, and multimodal logistics.

Natural resources and regions

Long‑tail searches such as “AI for forestry projects grants Saguenay–Lac‑Saint‑Jean,” “AI for mining grants Abitibi‑Témiscamingue,” and “regional AI grants Quebec” point to opportunities aligned with regional development priorities, including data infrastructure, remote sensing, and safety analytics.

Eligibility, costs, and stacking limits

Who can apply?

- SMEs, mid‑market companies, large enterprises, non‑profits, hospitals, municipalities, colleges (CCTT), universities, and research centers.
- Project teams must show managerial capacity, technical expertise, and credible partners for consortium projects.
- Many programs welcome newcomers to AI if training, reskilling, and change management are embedded in the plan.

Eligible expenses for AI projects

- Salaries and wages for data scientists, ML engineers, software developers, quality assurance, and project managers.
- Subcontracting for specialized services such as data labeling, cybersecurity, or model validation.
- Software, cloud services, GPU credits, and compute infrastructure; eligibility depends on program rules and caps.
- Equipment for AI integration (servers, sensors, edge devices), travel for collaboration, and commercialization activities.
- Training and workforce development: “AI training grants Quebec” and “AI workforce development grants” support upskilling.

Stacking, matching funds, and contribution types

- Programs often require matching funds or cost‑sharing; some accept in‑kind contributions for specific cost categories.
- Stacking limits prevent over‑compensation; applicants must align grants, tax credits, and loans with transparent budgets.
- Contributions may be non‑repayable (grants), repayable (loans), or hybrid structures with milestone‑based disbursements.

Application process: from idea to award

Step 1: Define the AI problem and baseline

Applicants who clearly define business pain points, data availability, and expected impact score higher. A concise problem statement, baseline KPIs, and target metrics help review panels evaluate feasibility and economic benefits.

Step 2: Choose the right program stream

Map your project to program intent: R&D (Prompt, NSERC, Mitacs), adoption (ESSOR, IRAP), supply chain transformation (Scale AI), or workforce (training subsidies). For early‑stage startups, “startup AI grants Quebec” and “Montreal startup AI grant list and amounts” point to micro‑grants and innovation vouchers.

Step 3: Build a credible budget and timeline

Review eligible expenses and funding caps, include contingency, and align milestone dates with application windows and deadlines (e.g., “Quebec AI grants 2026 deadlines”). Ensure vendor quotes and letters of intent match the scope.

Step 4: Structure partnerships and IP

For university–industry collaborations, define IP ownership, licensing, and publication rights. Include a consortium agreement or memorandum of understanding for multi‑partner projects. Align data governance and privacy requirements early.

Step 5: Submit, respond, and prepare for diligence

Expect clarifying questions on methodology, risk mitigation, and commercialization. Maintain a versioned work plan, budget, and risk register. Prepare for site visits or technical reviews in higher‑value calls for projects.

Step 6: Manage the award and reporting

Grants often require milestone reports, KPIs, and expenditure evidence. Establish time tracking and cost attribution for RS&DE alignment. Plan for audits and retain documentation for the grant management lifecycle.

Responsible AI, compliance, and standards

Funding increasingly requires responsible AI practices: fairness, transparency, safety, and privacy. Proposals with risk assessments, model monitoring, and incident response plans are stronger. Consider ISO/IEC standards, AI ethics frameworks, and cybersecurity certifications. Programs may support “AI ethics grants,” “AI safety research grants,” and “AI standards and compliance funding” to institutionalize governance.

Regional focus and ecosystem connectors

Montreal, Quebec City, Sherbrooke, and beyond

- Montreal AI grants: proximity to IVADO, Mila, and aerospace/fintech clusters.
- Quebec City AI funding: government services, health institutions, and smart city pilots.
- Sherbrooke and Estrie: applied research via universities and CCTTs, with strong manufacturing links.
- Laval, Longueuil, Gatineau, Saguenay, Trois‑Rivières, Chaudière‑Appalaches, Lanaudière, Laurentides, Montérégie, Bas‑Saint‑Laurent, Côte‑Nord, Gaspésie–Îles‑de‑la‑Madeleine, Centre‑du‑Québec: regional AI grants and college‑industry programs.

Budgeting tips: eligible AI expenses and documentation

- Eligible AI expenses often include salaries, benefits, software licenses, cloud compute, datasets, data clean rooms, and GPU time.
- Keep vendor quotes for hardware (GPUs/servers) and cloud pricing sheets; specify whether costs are OpEx or CapEx.
- Document time spent on experimental development to support RS&DE claims and avoid double counting with grants.
- Track training hours for “AI talent training grants for employees” and “AI apprenticeship wage subsidy Quebec.”

Combining grants, tax credits, and export support

Many AI ventures combine domestic funding with export market development. Programs for trade missions, conferences, or pilot deployments with anchor customers can complement technical grants. Long‑tails such as “AI export market funding Quebec for SaaS” and “travel grants for AI conferences Quebec SMEs” indicate viable stacks when limits permit.

Inclusive access: SMEs, non‑profits, Indigenous and women‑led ventures

Quebec funding frameworks encourage diversity and inclusion. Dedicated pathways or scoring bonuses may apply for projects led by women, youth, newcomers, Indigenous businesses, and non‑profits adopting AI. Searches include “women‑led AI startup grants Quebec,” “Indigenous AI business funding Quebec programs,” and “nonprofit AI adoption grants Quebec.” Workforce development and internship funding help broaden participation and close skills gaps.

From pilot to scale: making grants count

Funders look for credible plans to move from proof of concept to production. Strong applications show a pipeline: discovery, PoC, pilot, limited production, scale‑up, and continuous improvement with MLOps. Align procurement of innovation tools—particularly in healthcare and municipalities—to convert pilots into enduring deployments.

Key documents and readiness checklist

- Executive summary and problem statement, with market context and competitive benchmarks.
- Technical plan, data architecture, and security model; include responsible AI safeguards.
- Budget, match‑funds evidence, and stacking plan (RS&DE, CDAE, grants).
- Partnership letters (universities, hospitals, suppliers), IP and data‑sharing terms.
- Project governance, KPIs, and risk management framework.
- Reporting plan and grant management process.

Conclusion: Navigating AI funding in Quebec with clarity

Quebec offers a broad, layered portfolio of AI grants and funding programs that can be combined strategically. Whether you are an SME in manufacturing, a hospital testing AI triage, a fintech reinforcing compliance, or a regional enterprise modernizing operations, there are pathways across research, adoption, workforce, and commercialization. Plan early, align the right program streams, and balance grants with tax credits to maximize non‑dilutive financing while maintaining compliance and responsible AI practices.

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