How can SMEs close the AI skills gap? Canadian small and medium-sized enterprises can close it by concentrating scarce learning time on a few valuable workflows, giving everyone a responsible-use baseline, developing selected internal champions, and buying specialist expertise only where it accelerates transfer. The aim is not to copy a large company’s AI department. It is to build enough internal judgment to choose, use, supervise, and improve AI responsibly.
Why is the AI skills gap different for SMEs?
SMEs usually have fewer specialist roles, smaller training budgets, and less capacity to remove employees from daily operations. Yet they still need business judgment, data discipline, secure use, output verification, vendor oversight, change leadership, and technical support. The answer is therefore a smaller capability system, not a smaller version of enterprise bureaucracy.
Statistics Canada’s 2023 business survey found that 6% of small businesses and 8% of medium-sized businesses used AI technologies, compared with 26% of large businesses. In a separate 2024 survey, training current staff was the most common workforce adjustment among Canadian businesses that had implemented AI, reported by 38.5%. These figures are a national baseline, not a target for every sector.
“A company becomes AI-ready when it can turn one good idea into safe, adopted work, then learn fast enough to do it again.”
Mehrzad Verdizadegan, PhD
CEO, Praevion Consulting Inc.
Which five capabilities should SMEs build first?
| Capability | Practical evidence | Likely owner |
|---|---|---|
| Business selection | A use case has a user, baseline, expected benefit, cost, and accountable owner. | Owner or business leader |
| Responsible use | Employees know approved tools, permitted data, human review, and escalation rules. | Operations with privacy, legal, or security support |
| Workflow practice | Users can apply AI to a real task and handle common exceptions. | Process owner and champion |
| Evaluation | The team tests accuracy, completeness, bias, security, and fitness for purpose. | Domain expert with technical support |
| Value and improvement | The SME measures work outcomes, risk, adoption, and lessons before scaling. | Business owner and finance |
This capability mix reflects evidence that AI demand is not purely technical. The World Economic Forum’s Future of Jobs Report 2025 identifies AI and big data, networks and cybersecurity, and technological literacy as the fastest-growing skills, while analytical thinking, resilience, leadership, and collaboration remain important. SMEs need a blend of technical literacy and human judgment.
What learning model works for a small organization?
Start with one or two workflows where value can be measured and errors can be contained. Examples might include drafting internal material, summarizing non-sensitive documents, preparing first-pass research, or supporting customer-service knowledge retrieval. Avoid high-impact decisions until stronger controls and expertise are available.
- Discover: map tasks, users, current performance, information sensitivity, risks, and existing skills.
- Design: define the target workflow, approved tools, human review, learning objectives, assessment, and stop conditions.
- Deliver: combine short instruction with demonstrations, realistic practice, coaching, and feedback using the actual workflow.
- Transfer: give managers job aids, office hours, peer champions, refresher exercises, and responsibility for sustained use.
The Government of Canada’s implementation guide for managers of AI systems supports general responsible-use training for employees and specialized instruction for technical teams. For an SME, the specialized role may be part-time or externally supported, but accountability for the business decision must remain inside the company.
What should an SME build, buy, or share?
Build internally the knowledge closest to customers, operations, quality standards, and acceptable risk. Employees need enough literacy to recognize poor output and challenge a vendor. Buy temporarily scarce expertise for architecture, privacy, cybersecurity, contracting, model evaluation, or complex integration. Require each external engagement to include documentation, coaching, and a transfer plan. Share learning through sector associations, colleges, universities, supplier communities, and peer networks where confidentiality permits.
Do not hire an advanced specialist before the company can state the business problems that need solving. Conversely, do not rely entirely on a vendor when the SME cannot independently judge quality, cost, privacy, security, or lock-in. A practical AI readiness review can identify which capability is truly limiting progress.
How should leaders measure progress?
Use three levels of evidence. First, measure capability: scenario assessments, observed proficiency, verification quality, and correct escalation. Second, measure adoption: approved-tool use, completion of the target workflow, manager reinforcement, and recurring support needs. Third, measure business results: cycle time, rework, error, service quality, cost, employee experience, and incidents. Compare results with the original baseline and stop scaling when value or control evidence is weak.
Avoid counting course completions as the main success measure. The SME has closed a meaningful part of the gap when people can perform a priority workflow better, explain the limits, and recover safely when the technology fails.
Frequently asked questions
Does an SME need an in-house AI specialist?
Not always. It needs sufficient internal ownership and judgment, with specialist support proportionate to technical complexity and risk.
Should every employee receive AI training?
Everyone exposed to AI-enabled work needs a responsible-use baseline. Deeper learning should follow role, frequency, authority, and possible harm.
Is hiring faster than training existing employees?
Hiring may fill a scarce technical gap, but existing employees hold essential workflow and customer knowledge. Most SMEs need a deliberate combination.
What is the best first AI skill investment?
Start with business and user judgment around one valuable workflow, supported by safe tool use, verification, and clear accountability.
Executive takeaway
SMEs can close the AI skills gap without building a large department. Focus on valuable work, develop a shared baseline and a few deeper roles, source expertise with transfer requirements, and scale only when capability, value, and risk evidence support it.
Praevion Consulting Inc. helps Canadian organizations assess capability, design role-based learning, and transfer responsible AI skills into daily operations. Contact Praevion to discuss a practical AI capability program.
References
- Statistics Canada, Survey of Digital Technology and Internet Use, 2023
- Statistics Canada, Analysis on artificial intelligence use by businesses in Canada, second quarter of 2024
- World Economic Forum, Future of Jobs Report 2025
- Innovation, Science and Economic Development Canada, Implementation guide for managers of artificial intelligence systems
- OECD, SME Digitalisation to Manage Shocks and Transitions, 2024
