Bridging the AI skills gap: a guide for HR + L&D leaders

83% of HR leaders say AI has improved their efficiency, yet two-thirds still worry about its role in people management.* AI adoption is accelerating, but beyond leadership, uptake is uneven. Resistance, confusion, and job security fears persist — even as AI consistently tops employees’ learning wishlists.*

To close the AI skills gap, HR leaders must understand who’s using it, how, and where capability or confidence is lacking.

This article breaks down the AI skills gap’s real-world impact, five strategies to address it, and a checklist to assess where your teams stand. You’ll also see an example of what structured AI enablement looks like in practice.
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*Leapsome’s Workforce Trends Report, 2024
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What is the AI skills gap?

The AI skills gap is the mismatch between the skills employees need to effectively use AI tools and the current capabilities of the workforce. So, when 63% of decision-makers identify a critical AI skills gap in their organization, that’s to say their teams are missing the core knowledge they need to apply AI effectively. 

This gap might show up as:

• Lacking technical fluency when working with tools that power chatbots or data insights
• Struggling to develop and manage prompts that get reliable outputs from tools like ChatGPT and Claude
• Having difficulty integrating AI into daily workflows across sales, operations, and support‍

What this means for your organization is that automation opportunities get missed. Instead of streamlining reporting, onboarding, or content operations, teams are bogged down in manual work. Meanwhile, managers aren’t equipped to identify where processes could be redesigned. At the executive level, this weakens strategic resource allocation and scalable AI adoption.

Once you close this gap, you’ll empower your workforce to use AI with confidence, boosting productivity and efficiency. 
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Why the AI skills gap matters in 2025

94% of HR leaders agree that failing to train or reskill employees in AI carries multiple risks. But instead of developing internal capability, many companies bring in consultants to bridge the gap. That might work in the short term, but it creates a dependency that’s inefficient and expensive. 

Other teams default to old tools and processes even when better options exist. Imagine a manager manually writing out performance reviews when the company adopts an AI performance management tool that drafts reviews from peer and self-feedback. Feedback quality suffers, and managers lose time on tasks that could be handled in seconds. 

Now scale that across other roles and departments. The result is a workforce that isn’t just under-productive but also out of sync with how the business should operate. As a result, the ROI projected while procuring AI solutions doesn’t materialize in practice.

‍HR is uniquely positioned to lead on this. The relationship between AI and HR is becoming more central to organization-wide capability building. According to our 2025 HR Insights Report, HR leaders are: 

• Prepared to drive impact — 92% feel ready to generate business results.
• Influencing strategy — Nearly half (48%) of HR leaders are already making key business decisions.
• Responsible for workforce productivity — 62% now oversee this as part of their role.

These HR statistics reflect a shift in how strategic the function has become. The fact that 60% of leaders flag employee resistance to AI implementation as an urgent challenge makes one thing clear: this isn’t just an IT problem. It’s as much a mindset and confidence issue as a skill one. Therefore, addressing the AI skills gap falls naturally within HR’s scope.
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5 ways to bridge the AI skills gap

Your organization needs to treat AI capability as part of how work gets done — across levels of hierarchy, departments, tasks, and projects, all the way up to strategic planning.

The five approaches below focus on role-specific capability directly related to business outcomes. They reflect what you already drive: productivity, skills development, and adopting new tools and processes.
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1. Align AI skill development with business strategy

AI training shouldn’t happen in isolation. To close the skills gap, you must align AI capability-building with broader business priorities — like increasing operational efficiency, fostering data-driven decision-making, or improving the employee experience. That starts with making AI literacy a C-suite-level discussion. Executives should understand:

• What skills are needed — Leaders should have access to data on which AI capabilities are lacking or emerging across roles and functions based on structured assessments like an AI skills gap checklist.
  
  
• How they connect to outcomes — They need to see how AI skills contribute to measurable impact across efficiency, speed, employee experience, and ultimately, ROI.
  
  
• Where the biggest opportunities lie — They should know which departments or workflows would benefit most from focused training and support.
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One way to operationalize this is by tying AI skill development to goal-setting frameworks. For example, a company-wide goal to improve AI capabilities might break down into:

• Team-level targets — These could include running AI training sessions or integrating AI into monthly reporting workflows.
  

• AI-related projects — Might involve using AI to analyze engagement survey results and identify patterns or trends.
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• ‍Individual goals tied to tool proficiency — Employees might be expected to develop prompts that generate actionable insights from performance data.
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💡 Tools like Leapsome Goals can play a key role here by helping you visualize how each layer contributes to the broader strategy, which in turn helps keep everyone engaged and accountable.

2. Recruit for & promote AI fluency

Non-technical roles naturally don’t need deep technical expertise, but they do require a baseline comfort with AI tools, data interpretation, and experimentation.

‍That means updating job descriptions to reflect this shift. Add clear expectations around AI comfort level or data literacy — even for roles in HR, marketing, or operations. In interviews, ask practical questions about how candidates have used AI to improve workflows, speed up decision-making, or extract insights from data.

You should also look inward. Some of your best AI advocates are already on the team: employees who’ve found ways to integrate AI into their daily work without being told to. Identify them early, promote their methods, and build their influence into onboarding, peer learning, or cross-functional projects. The more visible and normalized AI fluency becomes, the faster it spreads across the organization.
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✨ Real-world example of AI advocacy: the Leapsome AI Ambassador Program

Leapsome has established a core AI team of ambassadors with several key objectives, including:

· Enhancing efficiency by automating routine tasks to free up time for strategic work
· Upskilling the workforce through training employees to work with AI technologies

The AI Ambassadors’ operating principles include:

‍🌟 Embracing AI-driven change and building excitement by demonstrating AI’s capabilities
🌟 Exploring ways to radically improve processes
🌟 Sharing resources and insights via dedicated channels
🌟 Prioritizing automation with existing tools over manual processes or new tool purchases

The program focuses on identifying AI ambassadors across all teams to help formulate and implement Leapsome's AI strategy. This structured approach aims to seed AI thinking throughout the organization and drive fast, practical applications.

✨ Want to learn more about how your team, too, can automate tasks, uncover insights, and make smarter decisions? Explore Leapsome AI.

3. Build role-based AI upskilling programs

An effective AI training program starts by identifying where AI upskilling will have the strongest impact on daily tasks — whether that’s writing effective prompts, interpreting AI-generated insights, or integrating tools into routine workflows. Generic, one-size-fits-all training won’t address these specific gaps. Different roles use AI differently, and training should reflect that.

For example, HR teams might need AI support to analyze engagement surveys, while finance might focus on AI process automation for monthly reporting. Legal teams could benefit from tools that summarize policy documents or highlight compliance risks. What matters is making the training relevant to each team’s tasks.

To build role-specific programs at scale, you can use a solution like Leapsome Learning, which allows you to build and assign tailored learning paths based on team, role, or skill focus, using a mix of internal and external content. The most effective programs emphasize hands-on application — training teams with the tools, workflows, and scenarios they work with every day. That’s how you build real familiarity and confidence.
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4. Train teams to use AI critically & effectively

It’s easy to assume that once people know how to use AI tools, they know how to use them well. But without the ability to question outputs, spot inaccuracies, or challenge assumptions, teams risk taking AI responses at face value, which can have dangerous consequences.

For example, an HR manager might ask an unspecialized AI tool to summarize engagement survey results and present them as is — without noticing the exclusion of isolated but high-risk concerns like burnout or discrimination.

Focused, practical training on prompt engineering, bias detection, and critical thinking will help. Continuing the scenario above, you could run a workshop where teams review AI-generated summaries of engagement survey data, then use the tool to ask follow-up questions or reframe prompts to surface what might have been missed. Bringing cross-functional teams together can also accelerate learning. Creating small pods — say, HR, L&D, and product — lets teams test AI workflows in context, compare approaches, and build shared standards. When people learn from each other, not just from documentation, they develop more decisive judgment and more adaptable practices.
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5. Reward experimentation & remove fear of failure

When 60% of HR leaders cite employee resistance to AI as an urgent challenge, it signals a deeper issue: people won’t adopt what they don’t feel safe trying. That resistance won’t shift through policy or training alone — people need the freedom to experiment with what does and doesn’t work.

Creating space for low-stakes testing — like AI hack weeks or opt-in sprints — helps remove pressure and encourages curiosity. One simple example: give each team a week to replace one manual process with an AI-powered alternative, then share what worked (and what didn’t) in an open team review.

Showcasing internal wins builds visibility and momentum while recognizing teams and individuals experimenting in meaningful ways reinforces that AI adoption is an evolving process, not a one-off initiative.
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AI skills gap identification checklist

Use this checklist as an assessment framework to evaluate your organization's current state of AI skills. You can consult it as part of a working session with team leads and department heads, or build it into existing performance and development planning, including discussions on how to coach your team members through their AI adoption challenges.

The goal is to highlight where teams are already strong, where adoption is lagging, and where targeted support (like training, coaching, or workflow redesign) is most needed. Revisit it regularly to track progress and adapt your learning and development approach as your teams evolve.
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Close the AI skills gap with Leapsome

Most teams won’t get value from AI unless it’s embedded into how they make decisions, complete tasks, and collaborate — and that will not happen through generic training or isolated tools. HR teams need a way to build real capability, track progress, and make adoption part of everyday work.

‍Leapsome helps you do exactly that. With tools for learning, goal setting, and performance reviews, you can create role-specific training, connect it to real outcomes, and see where support is needed.

Crucially, you can tie AI learning directly to individual growth and team priorities so adoption doesn’t happen in a vacuum. With built-in feedback and progress tracking, it’s easier to see where support is working — and get buy-in from all levels of your organization.
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