LinkedIn Learning's AI Courses: What's Worth Your Team's Time

Every week, someone asks me which external AI courses are worth recommending to their teams. LinkedIn Learning comes up constantly—it’s widely available, reasonably priced, and has a massive course library.

But that massive library is also the problem. There are hundreds of AI-related courses, varying enormously in quality, relevance, and usefulness. Recommending “take an AI course on LinkedIn Learning” isn’t helpful guidance.

I’ve spent the last several months reviewing LinkedIn Learning’s AI offerings systematically. Here’s what I’ve found for L&D teams looking to curate meaningful learning paths.

The Overall Landscape

LinkedIn Learning’s AI content falls into several categories:

Technical/Developer Content

Courses aimed at developers, data scientists, and technical roles. Topics include machine learning fundamentals, Python for AI, neural networks, specific frameworks like TensorFlow or PyTorch.

Quality: Generally strong. Often taught by experienced practitioners.

Relevance: Only for technical roles. Most of your workforce doesn’t need this.

Business/Strategic Content

Courses about AI strategy, implementation, and organisational transformation. Topics include AI for leaders, digital transformation, and AI ethics.

Quality: Mixed. Some excellent, some superficial.

Relevance: Good for managers and leaders, but often too abstract for practical application.

Productivity Tool Content

Courses about using specific AI tools—Microsoft Copilot, ChatGPT, Midjourney, and similar applications.

Quality: Highly variable. Some excellent, some already outdated.

Relevance: Most applicable for general workforce upskilling.

Conceptual/Foundational Content

Courses explaining what AI is, how it works conceptually, and its implications. AI fundamentals, machine learning explained, and similar.

Quality: Generally solid for building mental models.

Relevance: Useful as foundation before tool-specific learning.

Standout Courses Worth Recommending

These courses have earned my recommendation across multiple client implementations:

For General Workforce Foundation

“Introduction to Artificial Intelligence” - This foundational course builds solid mental models without getting too technical. Good for starting everyone’s AI journey.

“ChatGPT: Tips and Tricks” - Practical, applicable immediately, stays focused on useful techniques rather than theory.

“AI for Business” - Bridges conceptual understanding with business application effectively.

For Managers and Team Leaders

“Leading with AI” - Addresses the people side of AI adoption that managers need to navigate.

“Managing in the Age of AI” - Practical guidance on how management practices evolve with AI tools.

For Practical AI Application

“Microsoft Copilot: First Look” series - If your organisation uses Microsoft tools, these courses provide solid introduction.

“Prompt Engineering: Working with AI” - Good foundation in effective AI communication, applicable across tools.

For Ethics and Governance

“Ethics in AI” - Covers important considerations without being preachy or abstract.

“AI Accountability” - Useful for understanding responsible AI use.

Courses to Skip

Some courses aren’t worth the time:

Heavily dated content: AI moves fast. Courses from 2022 or early 2023 often reference tools and capabilities that have changed significantly. Check publication dates.

Tool-specific courses for tools you don’t use: Obvious but worth stating. Don’t assign courses on tools your organisation hasn’t approved.

Overly technical courses for non-technical roles: Complex technical content overwhelms people who just need to use AI tools effectively.

Extremely short courses: Many 15-30 minute courses are too superficial to build real capability. Look for sufficient depth.

Courses that are just recordings of talks: Conference recordings dressed up as courses rarely provide structured learning.

Building Learning Paths

Individual courses are less effective than structured paths. Here’s how to construct them:

Foundation Path (All Staff)

  1. Conceptual foundation (what AI is, capabilities, limitations)
  2. Organisation-specific policies and expectations
  3. Basic tool introduction (whatever tools you’ve approved)
  4. Prompt writing fundamentals

Total time: 4-6 hours across courses

Productivity Path (Knowledge Workers)

After Foundation:

  1. Specific tool deep-dive (relevant to their work)
  2. Advanced prompting techniques
  3. Workflow integration
  4. Quality assurance for AI outputs

Total time: 6-8 additional hours

Leadership Path (Managers and Above)

After Foundation:

  1. AI strategy and decision-making
  2. Leading teams through AI change
  3. Ethics and governance
  4. Workforce planning implications

Total time: 4-6 additional hours

Supplementing LinkedIn Learning

LinkedIn Learning is useful but has limitations. Supplement with:

Organisation-Specific Content

LinkedIn Learning can’t cover:

  • Your organisation’s AI policies
  • Your specific tools and configurations
  • Use cases relevant to your context
  • Integration with your processes

Build internal content for these elements.

Hands-On Practice

Watching courses doesn’t build skills. Add:

  • Practice exercises with real tools
  • Facilitated workshops
  • Peer learning sessions
  • Coaching and support

LinkedIn Learning provides knowledge; you need to create skill-building opportunities.

Current Updates

AI changes rapidly. LinkedIn Learning courses take months to produce and update. Supplement with:

  • Internal updates on new capabilities
  • Curated articles and resources
  • Regular refresh sessions

Keep learning current even when courses aren’t.

Implementation Recommendations

To get maximum value from LinkedIn Learning:

Curate Rather Than Assign Broadly

Don’t tell people “go learn about AI on LinkedIn Learning.” Too much choice leads to paralysis or poor choices.

Curate specific courses into paths and assign those paths.

Set Expectations for Completion

LinkedIn Learning completion rates without expectations are often below 20%. Set clear expectations:

  • Specific courses to complete
  • Timeframes for completion
  • Integration with development plans
  • Follow-up activities

Expectations drive completion.

Combine With Active Learning

For every hour of LinkedIn Learning, include at least an hour of active application:

  • Practice exercises
  • Discussion groups
  • Application to real work
  • Reflection and sharing

Passive consumption alone doesn’t build capability.

Track and Follow Up

Use LinkedIn Learning’s tracking features:

  • Monitor completion
  • Identify struggling learners
  • Follow up with non-completers
  • Connect completion to next steps

What gets measured gets managed.

The Bottom Line

LinkedIn Learning is a valuable resource for AI upskilling if used well. The platform offers substantial, accessible content that can accelerate workforce development.

But it’s a resource, not a complete solution. Effective AI upskilling requires:

  • Curation of the right content
  • Organisation-specific supplementation
  • Active learning opportunities
  • Clear expectations and support

Use LinkedIn Learning as part of a comprehensive approach, not as the entire approach.

The investment in thoughtful curation and implementation pays returns many times over compared to just telling people to “take some AI courses.”

Curate carefully. Implement thoughtfully. Watch capability develop.