5 Essential AI Skills for L&D Leaders

According to LinkedIn’s 2025 Workplace Learning Report⁽¹⁾, 71% of L&D professionals are now incorporating AI into their learning strategies, nearly double the adoption rate from just two years ago. Yet a striking skills gap has emerged: while 86% of learning leaders acknowledge AI’s critical importance to their function, only 24% report feeling confident in their ability to effectively leverage these technologies for leadership development.

In this guide, we’ll explore the 5 essential AI skills for L&D leader. You’ll discover practical frameworks for integrating AI into leadership development initiatives, learn how to balance technological capabilities with human expertise, and gain strategies for upskilling yourself and your team. We’ll explore real-world applications showing how AI is transforming coaching, content creation, skills practice, and program measurement.

Whether you’re just beginning your AI journey or looking to advance your existing capabilities, this guide provides a clear roadmap for developing the AI skills for L&D Leaders in the coming years. The window for gaining competitive advantage through AI-enhanced leadership development remains open right now but it won’t stay that way forever.

Why Have AI Skills Become Non-Negotiable for Today’s L&D Leaders?

As an L&D professional, you’re likely familiar with the overwhelming challenges facing the industry today. From drowning in mountains of learner data to the constant pressure of scaling training programs across diverse teams, the demands can feel relentless.

Many L&D teams struggle with making sense of all the data they collect. You might have assessment results, completion rates, and engagement metrics, but turning these into actionable insights often feels like solving a puzzle with missing pieces. Without the right tools, this valuable data sits unused while learning programs continue to operate on assumptions rather than evidence.

Another common headache is the need to scale training effectively. Your organization keeps growing, teams are increasingly remote or hybrid, and everyone needs different training at different times. Using traditional methods, you’re constantly choosing between quality and reach – a frustrating compromise that leaves many learners underserved.

Perhaps the most significant challenge is delivering truly personalized learning experiences. Your learners have diverse needs, backgrounds, and career paths, yet most training programs still follow a one-size-fits-all approach. This leads to disengagement, poor knowledge retention, and ultimately, wasted resources on training that doesn’t stick.

The AI Shift: Is your Job at Risk?

The L&D landscape isn’t just evolving, it’s transforming at lightning speed. AI tools are rapidly becoming standard in creating learning content, analyzing performance data, and delivering personalized learning paths. What once took weeks of manual work can now happen in minutes through AI assistance.

If you’re hesitant to try these technologies, the stakes are higher than you might realize. Organizations are increasingly expecting their L&D teams to leverage AI to deliver better results more efficiently. Those who can’t meet these expectations risk being sidelined or replaced by professionals who can utilize AI’s potential.

Think about it this way: if you could choose between an L&D partner who manually creates standard training materials versus one who uses AI to quickly develop personalized learning plans backed by data insights, which would you pick? This is the choice many organizations are making right now.

The truth is, AI isn’t coming to L&D – it’s already here. From AI-powered content creation tools that can generate scenarios and assessments in seconds to learning platforms that automatically adapt to each learner’s needs, these technologies are becoming essential, not optional.

By developing your AI skills for L&D now, you position yourself as a forward-thinking L&D professional who can bridge the gap between human expertise and technological advancement. Rather than fearing AI as a replacement, you can leverage it as your most powerful ally in creating meaningful learning experiences that truly impact your organization.

The Scalability-Personalization Paradox

Perhaps the most compelling reason AI skills for L&D have become non-negotiable is what we might call the scalability-personalization paradox:

The Challenge: Organizations need to develop thousands of leaders across different levels, regions, and contexts each with unique strengths, gaps, and learning preferences.

The Traditional Approach: Either standardize content for scale (sacrificing personalization) or customize programs for impact (sacrificing scale).

The AI Solution: Machine learning algorithms can analyze skill gaps, learning preferences, and contextual factors to create dynamically personalized learning journeys at scale.

For example, consider how Netflix transformed entertainment with personalized recommendations for millions of viewers simultaneously. Netflix’s recommendation system processes vast amounts of data, including viewing habits and ratings, to curate personalized content for each user.

In a comparable manner, AI in leadership development can process data on leadership behaviors and learning interactions to deliver targeted developmental content. This approach not only enhances engagement but also accelerates leadership growth across diverse populations, effectively balancing the need for both personalization and scalability in training initiatives. 

5 Core AI Skills for L&D Leader

In today’s rapidly evolving workplace, AI isn’t just a buzzword, it’s becoming an essential part of the L&D toolkit. Whether you’re just starting to explore AI skills for L&D or looking to deepen your expertise, developing these five core skills will help you harness AI’s potential to transform your learning programs.

1. AI Literacy 

AI literacy is your ability to understand AI fundamentals, including how AI systems learn, what they can do well, and where they struggle. It includes knowing key concepts like machine learning, natural language processing, and generative AI.

Let’s start with understanding AI types relevant to learning. You must recognize how different AI technologies serve distinct purposes in development programs:

• Generative AI: Creates new content, scenarios, and personalized learning materials
• Recommendation engines: Suggest tailored learning pathways based on individual needs and behaviors
• Natural language processing: Powers conversational learning experiences and content analysis

A critical aspect of AI literacy is prompt engineering, the art of crafting effective instructions for AI tools. Think of it as learning to speak a new language that helps you get the most from AI systems. For example, instead of simply asking, “Create training on communication,” you should write, “Create a 3-module outline for a business communication course for new managers, including key learning objectives for each module and suggested activities.” The difference in results can be dramatic.

Without AI literacy, you risk making poor technology decisions, underutilizing powerful tools, or implementing AI in ways that create problems rather than solutions. Many L&D professionals waste hours fighting with AI tools simply because they don’t understand how to effectively communicate with them.

How Will This Skill Benefit You?

Confidently discuss AI solutions with vendors and IT teams without feeling lost in technical jargon
Create clear, effective prompts that get better results from AI tools like ChatGPT when developing learning content
Identify realistic opportunities for AI to enhance your learning experiences while avoiding overhyped promises

2. AI-Powered Learning Course Development

This skill involves using AI as a collaborative partner in creating learning experiences knowing which aspects of content creation to delegate to AI and where human expertise remains essential. It includes using AI to generate initial drafts, create varied examples, develop case studies, design assessment questions, and even create basic graphics or video scripts.

Here’s the guide for how to create a course with AI. In practice, this might look like using AI to quickly generate a first draft of compliance training content, which you then refine and enhance with your instructional design expertise. Or you might use it to create ten variations of a scenario to practice the same skill in different contexts, making learning more transferable. You could also use AI to transform a lengthy technical document into an engaging microlearning series with key points, analogies, and practice questions.

The key is understanding that AI excels at generating volume and variations but requires your expertise to ensure the content aligns with learning objectives, organizational culture, and instructional best practices.

How Will This Skill Benefit You?

Cut content development time in half by using AI to create first drafts that you can refine and personalize
Quickly generate varied examples, scenarios, and case studies that make learning more relevant and engaging
Produce more learning content without expanding your team, helping you meet increasing training demands

3. Personalized Learning Design

Personalized learning design means creating training experiences that adapt to individual learner needs, preferences, and performance. With AI, you can design systems that automatically adjust content difficulty, provide tailored feedback, and create individualized learning journeys for all without requiring manual intervention for each learner.

For example, with tools like Risely, your employees receive personalized guidance and support from AI coach- Merlin, who provides personalized feedback to their specific challenges. When an employee struggles with a particular skill, like handling difficult client conversations, Merlin can recognize this pattern and automatically deliver relevant tips, practice exercises, and reminders right when they’re needed. Merlin can provide gentle nudges for behavioral change like “Remember to ask open-ended questions in your next client meeting” and celebrate wins when progress is detected.

Without this type of AI-powered personalization, many learners disengage when content feels irrelevant or overwhelming. Generic learning experiences often result in some employees being bored while others are confused, neither group getting what they actually need to improve.

McKinsey research⁽²⁾ indicates that L&D functions leveraging AI appropriately are delivering personalized leadership development at scale while reducing program costs by 20%. These aren’t marginal improvements, they represent a fundamental shift in how effective leadership capabilities can be built.

How Will This Skill Benefit You?

Design learning paths that automatically adjust based on learner performance, ensuring everyone gets the right level of challenge
Create more inclusive learning experiences that accommodate different learning styles and needs
Boost completion rates and knowledge retention by delivering content that feels personally relevant to each learner

4. Data Analysis and Interpretation

Data Analysis and Interpretation involves transforming learning data into actionable insights that drive improvement. It’s about knowing which metrics truly matter, how to interpret patterns, and how to make data-informed decisions about your learning programs.

Continuing with the above mentioned example, with Risely, you gain access to comprehensive dashboards that visualize your employees’ learning activities, engagement patterns, and skill development. Instead of relying on completion rates and satisfaction surveys alone, you can see how well your managers are performing on key leadership skills, and get benchmarks across the organizations, which managers & team members are having issues with. 

For example, your dashboard might reveal that 70% of your customer service team excels at problem resolution but struggles with empathetic communication. With this insight, you can create targeted learning experiences focused specifically on empathy skills for that team. You might also discover that managers who completed a particular leadership module show measurable improvements in team engagement scores, providing concrete evidence of that training’s effectiveness.

This level of data insight allows you to move beyond intuition to make evidence-based decisions about your learning investments, from designing assessments that accurately measure skill levels to creating precisely targeted learning interventions.

How Will This Skill Benefit You?

Spot patterns and trends that reveal which learning approaches are working best for different groups
Make evidence-based improvements to your programs instead of relying on gut feelings or participant satisfaction scores alone
Show leadership concrete evidence of learning impact, strengthening their support for your L&D initiatives

5. AI Tools Selection

AI Tools Selection involves evaluating and selecting the right AI learning technologies for your specific needs. It requires developing a structured approach to assessment that considers factors beyond flashy features, looking at integration capabilities, user experience, content control, data privacy, and alignment with your specific learning challenges.

In practice, you’ll create clear criteria for tool selection based on your organization’s unique requirements. For example, if you’re looking for a tool to help with content creation, you’ll evaluate options based on the types of content you typically create, the level of customization needed, and how the output integrates with your existing learning platforms.

You’ll need to look beyond vendor demonstrations to understand how tools will actually function in your environment. It means asking tough questions about implementation requirements, requesting trial periods, and possibly running small pilot projects before making major investments.

How Will This Skill Benefit You?

Save money by investing only in AI tools that address your organization’s actual learning needs
Avoid the frustration of implementing tools that don’t integrate well with your existing systems
Build a streamlined tech stack that enhances your learning offerings without overwhelming you or your learners

Developing these five AI skills doesn’t happen overnight, but even small steps forward can lead to significant improvements for you.. Start by focusing on one skill that could address your most pressing L&D challenge, and build from there. Remember, the goal isn’t to replace your expertise with AI, but to enhance what you already do well with powerful new capabilities.

Roadmap to Building Your AI Skillset

Building AI skills for L&D isn’t an overnight achievement—it’s a strategic progression that begins with honest self-assessment and deliberate skill development. As L&D leaders, you’re uniquely positioned to not only implement AI in your organization’s learning strategies but to model the very learning journey you design for others.

Step 1: Assess Your Current AI Readiness

Before diving into courses or experimenting with tools, take stock of where you stand:

Self-assessment framework for L&D professionals

Understanding your AI readiness isn’t just about technical knowledge, it’s about your mindset, awareness, and application capabilities across multiple dimensions:

• AI Awareness: How familiar are you with AI concepts, capabilities, and limitations?
• Technical Comfort: Can you interact with AI tools and understand their basic functionality?
• Strategic Vision: Do you see how AI fits into learning strategy beyond just automation?
• Ethical Understanding: Are you aware of bias, privacy, and ethical considerations?

Identifying your strengths and development areas

Most L&D leaders find themselves stronger in learning design principles and weaker in technical implementation. Be honest about where you excel and where you need growth. For example:

• Strength: Understanding adult learning theory and how to design effective experiences
• Development Area: Knowing how to effectively prompt AI systems for specific content generation

Understanding your organization’s AI maturity level

Your personal AI journey exists within an organizational context. Assess where your company falls on this spectrum:

• AI Novice: Limited awareness, no formal strategy
• AI Aware: Recognizes potential, isolated pilot projects
• AI Ready: Clear strategy, active implementation in progress
• AI Mature: Integrated AI capabilities, measuring ROI

Donald H. Taylor’s “Intention and Reality” model states, “There is always a gap between what we intend to do and what actually happens in practice.” Many L&D leaders want to use AI but struggle to take the first step. Time, complexity, and lack of support often hold them back. As Taylor puts it, “Turning intention into reality requires action, experimentation, and a willingness to embrace change.” The key is to start small, learn the basics, try AI tools, and show results. AI won’t replace you; it will make your work easier and more impactful.

Without assessment, many L&D professionals waste time developing skills they don’t need while overlooking critical capabilities. A personalized assessment prevents this scattershot approach.

You’ll develop a targeted learning path that addresses your specific needs, saving months of unfocused effort. L&D leaders who begin with structured self-assessment report reaching implementation readiness 40% faster than those who jump straight into learning tools.

Step 2: Create Your AI Learning Plan

With your readiness assessment complete, it’s time to build a learning roadmap. Focus on AI applications relevant to L&D, start with beginner-friendly content, and choose resources that offer hands-on practice. Practical learning will help you apply AI effectively.

Free platforms like Coursera, edX, and Google’s AI Hub provide a solid foundation, while paid options like LinkedIn Learning and Udemy offer structured courses and certifications. Joining AI-focused L&D groups or attending webinars can also help you stay updated and connected.

Paid vs. Free: What’s Right for You?

Free resources are great for getting started, but they may lack depth and structured learning paths. Paid options often provide guided instruction, practical exercises, and certification—useful for credibility and career growth. A mix of both ensures well-rounded learning without overspending.

By curating the right learning materials, you’ll accelerate your AI literacy and confidently integrate AI into your L&D strategy.

Recommended resources for developing AI literacy

Balance your learning across different formats:

Courses

• Learning How to Learn with AI on Coursera
• AI for Everyone by Andrew Ng

Newsletters & Podcasts

• “AI in L&D Weekly” by Donald Clark
• Learning Scientists Podcast episodes on AI
• Risely’s Podcast on L&D 

Hands-on practice:

• Schedule weekly “AI experimentation time” to test tools like ChatGPT, Claude, or Midjourney
• Apply AI to a current challenge: “How might I use AI to improve our onboarding materials?”
• Keep a learning journal documenting insights and questions

Experiential learning opportunities with AI tools

Theory without practice yields limited results. Schedule hands-on time with these tools:

• Content Creation: Experiment with ChatGPT, Claude, or Bard to generate learning materials
• Image Generation: Try DALL-E or Midjourney to create learning visuals
• Video Creation: Explore Synthesia or D-ID for creating training videos
• Data Analysis: Use Obviously AI to analyze learning data without coding

Start with a real L&D challenge from your work and attempt to solve it using AI. Document what works, what doesn’t, and how you might improve your approach.

The AI field is vast, and without a structured learning architecture, it’s easy to get lost in technical jargon or theoretical concepts that don’t translate to practical L&D applications. Your learning should be deliberate, not accidental. A structured plan ensures you develop versatile skills that directly impact your work. You’ll gain confidence faster by focusing on relevant applications rather than trying to become an AI generalist. 

Step 3: Start Small but Think Big

Build confidence through targeted experimentation:

Begin with pilot projects that have minimal risk but potential impact: Starting small allows you to test AI in a controlled environment without major disruptions. This helps you learn, adjust, and build confidence before scaling AI adoption.

• Use AI to generate discussion questions for an existing workshop
• Create a personalized learning path recommendation system
• Develop AI-enhanced job aids for a specific role

Document your process and results: Tracking your steps and outcomes provides clear insights into what works and what doesn’t. It also helps demonstrate AI’s value to stakeholders, making it easier to secure buy-in for future initiatives.

• What problem were you trying to solve?
• How did AI help (or not help)?
• What was the learner’s response?
• What would you change next time?

Attempting organization-wide AI implementation without proven success cases is a recipe for failure. Small wins build both your confidence and organizational trust in AI-enhanced learning approaches.

Beyond the immediate benefits of each project, these pilots create an evidence base for larger initiatives. Success metrics from small experiments provide compelling data when seeking resources for broader implementation. They also help you identify which types of learning challenges respond best to AI solutions in your specific context.

Step 4: Collaborate and Learn Continuously

Accelerating your growth through community is very crucial because it allows you to learn from others’ experiences, stay updated on AI trends, and gain support in navigating challenges:

• Form strategic partnerships with IT and data teams who can support your initiatives
• Create an AI Learning Circle within your organization where colleagues share discoveries
• Contribute your insights through blog posts, internal presentations, or industry forums

As you develop AI Skills for L&D, you’ll find that it enhances rather than replaces your L&D expertise. The goal isn’t to become an AI engineer, but to become an L&D leader who leverages AI to create more impactful, personalized, and efficient learning experiences.

AI adoption in L&D isn’t a solo journey. The field evolves too rapidly for any individual to keep pace. Cross-functional partnerships also ensure you don’t create solutions that conflict with organizational data policies or technical architecture.

A collaborative approach accelerates your learning curve exponentially. Organizations with cross-functional AI learning communities report 3x faster adoption of effective practices. These relationships also help you navigate potential obstacles before they derail your projects, from data privacy concerns to integration challenges.

Conclusion

Let’s wrap this up simply, becoming AI-savvy as an L&D leader doesn’t have to be scary. These AI skills for L&D we’ve talked about can really boost your impact, making your training programs more data-driven, personalized, and effective.

Here are some recommendations as you move forward:

• Start small but don’t wait: Even understanding the basics of AI gives you a good foundation
• Focus on real problems: Look at what’s not working well in your programs and see how AI might help
• Try things out: You’ll learn more by actually using AI tools than just reading about them
• Keep the human touch: Remember, AI helps your work but doesn’t replace your leadership and empathy
• Connect with others: Learning alongside peers makes everything easier

But here’s the thing – there’s a real cost to sitting on the sidelines. Without AI skills for L&D, you might:

• Have less influence with executives (because you lack data-backed insights)
• Face budget cuts (as programs without clear ROI often get cut first)
• See your career options shrink (as AI knowledge becomes expected in senior roles)

AI isn’t coming for your job – it’s here to make you better at it! 

References

1. LinkedIn’s 2025 Workplace Learning Report
2. McKinsey Technology Trends Outlook 2022