LinkedIn's Fastest-Growing Skills in 2026: What Canadian Businesses Should Prioritize

LinkedIn just published its 2026 Skills on the Rise report, and the findings confirm what forward-thinking Canadian businesses have been acting on for the past year. The eight fastest-growing skill categories reveal a clear pattern: AI technical skills and strategy are in high demand, but human skills like communication, leadership, and market growth are what differentiate the companies pulling ahead.

The report tracks skill acquisition (skills added to profiles) and hiring success (skills possessed by recently hired members), comparing December 2024 through November 2025 against the prior year. For Canadian business leaders investing in workforce development, the implications are concrete and actionable.

What Are the Fastest-Growing Skills in 2026?

LinkedIn's report identifies eight skill categories that are growing fastest across global hiring markets. The list spans the full range from technical AI infrastructure to human-centric capabilities like communication, leadership, and revenue growth.

What stands out is the breadth of the shift. The report notes that one in five professionals globally say that lacking the right skills makes their job search more challenging. Career paths are increasingly shifting toward "new-collar" roles — positions defined by capabilities rather than credentials — and employers are evaluating candidates on what they can do, not where they went to school.

Why Do AI Skills Dominate the List?

Two of the eight fastest-growing categories are explicitly AI-focused: AI Skills & Infrastructure and AI Strategy & Governance. Together, they cover the full AI stack — from hands-on technical work like model training, fine tuning, RAG, and vector databases to strategic capabilities like responsible AI and tech-enabled business transformation.

As the report puts it: "As AI adoption accelerates, so does the need for professionals with the technical expertise required to build, integrate and operationalize AI models." The sub-skills listed — LangChain, FastAPI, OpenAI API, Google Gemini, XGBoost — are not abstract concepts. They are the specific tools and frameworks companies are hiring for right now.

The reason AI skills dominate is straightforward: organisations that cannot deploy AI effectively are falling behind on productivity, cost structure, and customer experience. The skills gap is not theoretical. It shows up in delayed projects, failed implementations, and competitive losses to companies that moved faster on AI adoption.

For Canadian businesses, the urgency is compounded by a relatively tight labour market for AI talent. Universities and bootcamps are producing more AI-literate graduates than before, but demand is outpacing supply — particularly for mid-career professionals who combine domain expertise with AI fluency.

Which Human Skills Still Matter — and Why?

Three of LinkedIn's eight fastest-growing categories are fundamentally human: Communication & Influence, Team Development & Leadership, and Revenue & Market Growth. This is not a consolation prize. These skills are growing because AI is amplifying their importance, not diminishing it.

As the report notes: "Clear, effective communication with key decision-makers sets professionals apart" and "An organization is only as strong as its employees." Navigating a complex stakeholder landscape, leading distributed teams through an AI-driven transformation, expanding into new markets — these require judgment, empathy, and contextual awareness that AI does not possess.

The winning combination is not AI skills or human skills. It is AI fluency paired with strong communication, leadership, and market growth capabilities. Organisations that hire or develop people with both skill sets outperform those that optimise for one dimension alone.

Pure technical skills without soft skills create bottlenecks. Teams of brilliant AI engineers who cannot communicate with business stakeholders produce tools nobody uses. Conversely, leaders who cannot understand AI capabilities make poor decisions about where and how to deploy it. The most valuable employees in 2026 are translators: people who speak both languages fluently.

How Should Canadian Businesses Respond?

The data points a clear direction: invest in both AI literacy and human skills, and do it systematically rather than ad hoc. Here is a practical framework for Canadian businesses responding to these trends.

Build a skills inventory. Before investing in training, understand what your team already has versus what the market demands. Map each role in your organisation against the eight skill categories and identify the largest gaps. This prevents wasted spending on training that does not address your actual needs.

Prioritise practical AI fluency over theoretical knowledge. Most employees do not need to understand transformer architectures. They need to know how to use AI tools in their daily workflows — writing effective prompts, interpreting AI outputs, knowing when to trust AI and when to override it. Practical, role-specific training delivers faster ROI than generic AI courses. For guidance on designing effective training programs, see our guide on training your workforce to collaborate with AI.

Invest in change management alongside technical training. AI skills training without change management is like installing new software without telling anyone how to use it. Employees need context for why they are learning these skills, how their roles will evolve, and what support is available. The organisations that see the highest adoption rates treat change management as a core part of AI rollout, not an afterthought.

Do not neglect human skills — they are the multiplier. AI fluency without communication and leadership skills produces technicians, not leaders. Budget for leadership development, stakeholder communication training, and cross-functional collaboration workshops alongside AI upskilling. These are not soft skills in the dismissive sense. They are the capabilities that determine whether AI investments translate into business outcomes.

Address data governance and responsible AI skills early. With PIPEDA governing how Canadian organisations handle personal data, and the proposed Artificial Intelligence and Data Act (AIDA) on the horizon, risk and compliance skills are not optional. Build data governance literacy into your training programs from day one rather than retrofitting it after an incident.

What Does a Skills-First Hiring Strategy Look Like?

One of the most significant themes in LinkedIn's report is the shift toward what it calls "new-collar" roles — positions defined by capabilities rather than credentials. Employers are increasingly evaluating candidates on what they can do, not where they went to school. This shift from credential-based to skills-based hiring is not a trend. It is a structural change in how companies compete for talent.

The report also finds that one in five professionals globally say lacking the right skills makes their job search more challenging. This creates an opportunity for companies that invest in internal upskilling. If you can develop the skills your competitors are trying to hire for, you gain a retention advantage and avoid the cost and risk of external recruitment.

Practically, a skills-first hiring strategy involves several shifts:

• Rewrite job descriptions around skills, not credentials. Replace "Bachelor's degree required" with specific, demonstrable skills. This widens your talent pool and attracts candidates who built skills through non-traditional paths — exactly the "new-collar" approach LinkedIn describes.
• Build internal AI academies. Create structured learning paths that take employees from AI basics to role-specific proficiency. Pair formal training with hands-on projects that apply AI to real business problems.
• Allocate dedicated learning time. Organisations that formalise learning time see higher completion rates and faster skill acquisition. Given the breadth of skills in the report — from RAG and vector databases to stakeholder management — employees need structured time to develop these capabilities.
• Measure and certify skills internally. Create internal competency assessments that validate AI proficiency. This gives employees a visible career development path and gives managers data on team readiness.

For a deeper look at which roles benefit most from AI and how to sequence your investment, see our role-by-role breakdown.

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