AI Governance for Quebec SMEs: A Practical Guide

What Changed in 2026: Three Converging Pressures

Law 25 Is No Longer Optional

Since the full entry into force of the Act modernizing legislative provisions as regards the protection of personal information, Quebec SMEs have concrete obligations. As soon as an AI tool collects, processes, or analyzes personal data (including your employees' data), the law applies. This includes meeting transcription tools, writing assistants configured with customer data, and HR performance analysis models.

Lacking a usage policy is not a gray area. It is a documented vulnerability during an audit or incident and exposes the organization to new cybersecurity risks and vulnerabilities.

Your Major Clients Demand Governance Guarantees

Large enterprises and institutional buyers are adding AI governance clauses to their vendor contracts. It isn't universal yet, but it is coming. Having a documented framework before being asked puts you in a position of strength during RFPs.

Expected Productivity Doesn't Materialize Without Structure

Often, what we observe is that SMEs that adopted AI tools without structure report a recurring phenomenon: every employee works individually with personal or public licenses (which poses a major data security risk) and different tools, resulting in a loss of internal knowledge and stalling skill sharing. Data doesn't flow between platforms and expected gains fail to materialize. A manufacturing SME supported by Baseline had seven active AI tools across the organization prior to our engagement. Nobody knew which ones exchanged data. Unraveling that situation took two weeks, but it was necessary before any structured deployment.

The Four Governance Roles for an SME

To effectively govern AI, an SME relies on clear executive sponsorship and three key operational roles.

Executive Governance Lead (General Management, President, Executive Committee, etc.)

Essential to the success of the process, the executive sponsor provides political and strategic support for governance. Whether general management or a dedicated committee, their role is to grant the necessary legitimacy to decisions, allocate required resources, and decide on strategic directions. They bridge company vision, operational execution by the Privacy Officer, and grassroots awareness led by the adoption champion.

Privacy Officer (Personal Information Protection Officer)

This role is mandatory under Law 25. In an SME, it is often held by the CEO or Administrative Director. Their AI responsibility: ensuring deployed tools comply with confidentiality obligations, personal data is not transmitted to third-party vendors without contractual safeguards, and employees understand what is collected.

This role does not require a full-time commitment, but rather a quarterly review of active tools and a clear onboarding process for new software acquisitions.

Operational Digital Lead

This is the person who understands the existing toolset and can evaluate new requests. In an SME of 20 to 100 employees, this role is often held by the IT Manager or Operations Director. Their function: evaluating the technical compatibility of new tools, managing access, and overseeing integrations.

The most common friction point in SMEs is that this person lacks time. This is precisely why external support absorbs the initial workload and then transfers responsibilities with documented processes.

Internal Adoption Champion

This is not a formal position. Many organizations already have AI enthusiasts and optimists: identifying one of these convinced users and giving them a clear mandate greatly facilitates adoption by example. Ignoring this role is the main reason AI deployments stall after the pilot phase.

Law 25 Compliance and AI: What You Need to Document

AI governance and Law 25 compliance intersect at several specific points.

Tool Inventory.

List every active AI tool in your organization, the data it processes, involved vendors, and the accountable tool lead. This inventory is the foundation for everything else. Without it, you cannot assess your exposure.

Acceptable Use Policy.

A one-to-two-page document specifying what employees can do with company-provided AI tools, what is prohibited (e.g., sharing customer data with unapproved models), and the consequences of non-compliance.

Privacy Impact Assessment (PIA).

Mandatory for AI projects handling sensitive data. A PIA documents identified risks and mitigation measures taken. Drafting one takes two to five days depending on project complexity.

Vendor Agreements.

Your contracts with AI tool vendors must specify how data is used, stored, and protected. Several popular solutions have model training clauses that deserve careful review before signing.

Four-Phase Roadmap

This progression is drawn from real engagements. Timelines are indicative for an SME with 15 to 75 employees starting without a governance structure in place.

Phase 1: Diagnosis (Weeks 1 to 2)

Inventory currently used AI tools, who uses them, and what data they process. Identify immediate compliance gaps. This phase often reveals surprises: tools used by employees without management's knowledge, active subscriptions on unapproved platforms, customer data entered into public models.

At the end of Phase 1, you have a list ranked by risk level. Not a complete strategy—a list.

Phase 2: Foundations (Weeks 3 to 6)

Draft the acceptable use policy. Formally assign the three governance roles. Renegotiate or document agreements with priority vendors. Train managers on Law 25 basics applied to AI.

This phase is administrative, not technological. It may not feel flashy, but it conditions everything that follows.

Phase 3: Targeted Pilot (Weeks 7 to 14)

Select a single high-potential, low-risk use case. Deploy the tool with your adoption champion. Measure results against a specific metric: processing time, error rate, volume handled. A professional services SME supported by Baseline reduced time spent on data reconciliation between its CRM and invoicing system by 40% during this pilot phase. This figure justified the investment for subsequent phases.

Phase 4: Structured Deployment (Months 4 to 12)

Extend validated tools to other teams. Integrate AI tool reviews into existing processes (annual reviews, new employee onboarding). Verify annual compliance against regulatory developments.

AI governance has no end date.

Available Funding in Quebec

Several programs support Quebec SMEs in adopting AI. Eligibility criteria and funding amounts vary by fiscal year and sector.

• Investissement Québec offers loans and loan guarantees for digital transformation projects including AI.
• Digital Canada and the NRC's Industrial Research Assistance Program (IRAP) cover part of the costs for external consulting and development.
• PRIME (Research and Innovation Program in Commercial Entities) supports AI projects conducted in collaboration with universities or research centers.
• TECHNO and Manufacturing SME Grants: Find the general directory and guidelines for these sector-specific initiatives in the guide for Digital Transformation Grants in Quebec, or explore other levers such as Fonds C for Software.

An external advisor can help identify eligible programs and prepare applications. The time spent assembling a grant application is often recovered as soon as the first funding installment is received.

Conclusion

Structuring AI governance in a Quebec SME does not require complex infrastructure. It requires executive sponsorship, three defined operational roles, and a documented usage policy. These frameworks protect your organization and accelerate deployments.

Your employees have already started without you. The question is no longer whether to structure, but how.