AI upskilling: how HR and L&D teams can prepare their workforce for the future

AI is already changing how work gets done. What HR and L&D leaders need now is a clear plan to help employees keep up.

Two-thirds of HR leaders say they’re facing a serious AI skills gap.* As AI becomes a workplace essential, that gap risks slowing teams down and undermining business outcomes.

AI upskilling isn’t about training a team of data scientists. It’s about building the confidence and skills employees need to use AI effectively — from writing better prompts to applying AI insights to speed up their work.

This article highlights which AI skills to prioritize and how to build a scalable program that drives real results, fast.
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*Leapsome’s HR Insights Report, 2025
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What is AI upskilling?

AI upskilling is the process of training employees to understand, collaborate with, and make decisions using artificial intelligence tools. It’s not limited to technical roles — it includes enabling people across departments to apply AI to their day-to-day work in practical, role-specific ways.

Unlike AI training for engineers or data scientists, AI upskilling emphasizes applied knowledge: understanding how AI tools work, where they add value, and how to use them responsibly. It differs from reskilling, which involves preparing employees for entirely new roles. AI upskilling is about enhancing the capabilities of your existing workforce in the roles they already hold.
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Real-world examples of AI upskilling

• A customer service rep using AI to summarize tickets and reduce response times
• A marketer prompting AI to generate first-draft content, then editing for voice and accuracy
• A product leader experimenting with prompt engineering to speed up research on user pain points

Done right, AI upskilling boosts productivity, improves employee confidence, and makes your workforce more agile without adding headcount or overhauling your org chart. This is especially important considering that 71% of managers and 63% of individual contributors (ICs) reported increased workloads in 2024.
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Why AI upskilling is critical in 2025

AI tools are here, and their use is growing. Over 50% of organizations have implemented generative AI tools across at least one business function, with adoption expected to accelerate across industries in 2025. But while the tools are in place, most teams aren’t equipped to use them effectively, and that’s becoming a serious liability. What’s at stake:

• 64% of HR leaders consider the AI skills gap an urgent problem. 
• Unaddressed, this translates into missed automation opportunities, slower decision-making, low confidence among employees, and weak return on AI investments.

Pressure is building internally, too. With AI tools evolving at breakneck speed and integrating into everything from content creation to data analysis, HR and L&D teams are tasked with pushing for adoption and proving ROI now, not someday.
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Skills to prioritize in your AI upskilling programs

Before launching a new AI initiative, HR and L&D leaders need to be precise about what success looks like. That starts with identifying the skills that matter most — not just the technical basics, but role-specific use cases and high-value soft skills that drive adoption. The right mix will vary by department, but these core skill sets are a solid foundation for any upskilling strategy.
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Foundational technical literacy 

Everyone in your organization needs a baseline understanding of how AI tools work and where they may fall short.

Start with prompt engineering. Helping employees get comfortable writing clear, goal-oriented prompts can dramatically improve output quality and save hours of trial and error. Combine this with hands-on training using tools like ChatGPT, Claude, Jasper, and Notion AI so teams can build muscle memory and real confidence.

And don’t overlook limitations. AI is powerful but far from perfect. Employees should understand issues like bias, hallucinations (false outputs), and how to validate results — especially when making business-critical decisions.

Wrap this foundation technical literacy with essential data privacy and AI ethics training. Employees need to know what they can and can’t input into these tools, how to spot risks, and where internal policies apply.
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Role-specific applications

68% of HR leaders are concerned about implementing AI into business processes. However, the best programs connect AI use cases to real, everyday tasks, not abstract capabilities. That means pairing training with examples employees recognize from their roles and giving them time to experiment with new tools in a safe, structured setting. 

Let’s consider some examples by function:

• Sales — Use AI to summarize call notes, generate follow-ups, and improve pipeline forecasting.
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• Marketing — Test headlines, create first-draft copy, and analyze campaign performance.
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• Product — Run AI-assisted user research, analyze feedback, and accelerate roadmap planning.
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• ‍HR & People teams — Apply AI to streamline 360° performance reviews, generate organizational and team OKRs, create competency frameworks, analyze survey results, and personalize learning paths and onboarding workflows.
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High-value soft skills

AI upskilling isn’t just about gaining expertise with relevant tools. It’s also about nurturing the human capabilities that make AI use effective, responsible, and sustainable at scale.

Start with critical thinking. Employees need to assess AI outputs with a sharp eye — separating signal from noise, spotting gaps, and knowing when to edit or override results. This is especially important in roles where decisions carry risk or reputational impact.

Next is collaboration. As more teams adopt AI tools independently, alignment becomes critical. Employees should understand how AI is used across the business, avoid inconsistencies and conflicts, and work together to share successful use cases.

Adoption leadership is another overlooked skill. Upskilling early adopters to support others — through peer learning, feedback, or even informal “office hours” — can boost tool adoption and create momentum from the inside out.

Finally, emphasize ethical reasoning. From privacy issues to model bias, employees should be equipped to raise concerns, follow company policies, and model responsible AI use in their teams.
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👏 Pro tip: Consider creating an AI Ambassador Program within your organization to encourage team members to explore AI use cases, share learnings, collaborate, and build momentum.

Marc-Alexander Vetter, our Head of Finance and AI Ambassador Program Coordinator, explains how that looks at Leapsome:

“Since launching the AI Ambassador Program, we’ve seen a shift from cautious curiosity to confident exploration. Colleagues across teams are increasingly experimenting with AI tools, staying current on new developments, and sharing what they learn. They’re also identifying use cases and driving implementations that make their work more impactful.

‍We’ve built a foundation for upskilling that goes beyond one-off sessions. It’s rooted in ongoing collaboration, shared wins, and practical applications. This momentum is helping us make AI a natural part of everyday work — not a passing trend, but a lasting shift in how we operate.”

How to build a scalable AI upskilling program

The fastest way to stall an upskilling initiative is to roll out a generic plan. To get traction and results, you need to meet team members where they are, prioritize based on actual workflows, and create clear, trackable paths for improvement.

Here’s how to build an AI upskilling program that fits your organizational needs and scales without reinventing your L&D strategy from scratch.

Step 1: Run a focused skills & workflow audit

Start by getting a clear picture of where AI is already being used across your organization. For example:

• Customer support using ChatGPT to draft email responses
• Operations team members automating routine reports with AI tools
• Marketers testing ad copy in Claude or Jasper

Then, identify 3–5 high-leverage workflows per team that AI could improve right now — repetitive, time-consuming, or easily templated tasks.

To understand team readiness, send out a quick pulse survey or run a workshop to assess current AI literacy, comfort levels, and blockers. If you choose the survey route, a tool like Leapsome Surveys can help you segment results by department, seniority, or location.

By the end of this step, you should have a short, actionable list of roles and workflows to prioritize over the next 90 days.
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Step 2: Segment your audience by role & AI exposure

Not everyone needs the same level of AI training. Segmenting your audience avoids wasted time and mismatched content and keeps your programs relevant from day one.

Start by grouping employees into three broad categories:

• AI beginners — Little to no exposure. Need basic understanding, context, and safe-to-try use cases.
  
  
• Tool users — Already experimenting with AI tools. Need guidance on best practices, limits, and how to apply AI more consistently.
  
  
• Technical stakeholders — Working directly with AI integrations, APIs, or data. Need deeper training on model behavior, risk management, and advanced use cases.

Once you’ve mapped employees into these groups, you can tailor depth, tone, and examples to each one. That might mean a lightweight onboarding module for AI beginners, more tactical sessions for tool users, and deep dives or peer-led demos for technical stakeholders.

This segmentation also helps identify champions early — employees already comfortable with AI and can help drive internal adoption as the program scales. 
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Step 3: Pick formats that get people hands-on fast

One of the biggest concerns HR leaders have about AI implementation is employee resistance. That resistance usually comes from uncertainty, not just about the tools but also about how they can fit into and actually improve day-to-day work.

Don’t just teach AI: build training that centers on the real tasks people already do. Make sure employees are learning in context, not in isolation.

These are some high-impact formats to consider:

• Live team workshops — Use real scenarios and team-specific prompts (e.g., “write a follow-up email based on this AI-generated call summary”).
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• Async microlearning — Deliver short, targeted lessons through your learning management system (LMS). Leapsome Learning makes it easy to build and track these kinds of sessions for different teams.
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• AI sprint weeks — Challenge employees to use AI to speed up 2–3 regular tasks or workflows.
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• Internal AI office hours — Team leads or early adopters share tips, demo workflows, and answer questions in a casual setting.
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💡 Don’t forget to spotlight use cases that illustrate AI performance management in action — like using AI tools to help managers summarize performance notes or draft feedback based on employee goals and check-ins.

Step 4: Choose vendors & tools that match your reality

Your tech stack doesn’t need to be cutting-edge — it just needs to align with how your teams work. The best AI upskilling programs start small, involve solutions people actually use (or are interested in using), and scale intentionally.

For non-technical teams, prioritize intuitive tools with clear use cases, for example:

• General productivity — ChatGPT, Claude, Notion AI
• Marketing & content — Jasper, Copy.ai, Perplexity

For technical teams or research and development (R&D) functions:

• AI development — Hugging Face, LangChain, Claude API, custom GPTs
• Internal experimentation — Open-source LLMs or sandbox environments

And if you’re looking for software solutions that can help develop or deliver AI training materials:

• General — Coursera, DataCamp, Udemy for Business
• B2B-focused or internal — Leapsome Learning or in-house enablement resources tailored to your workflows

Keep your vendor list tight; one or two main platforms are usually enough. Supplement with internal demos, peer-led training, and lightweight check-ins that keep momentum high without creating complexity.
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Step 5: Track & adapt based on real outcomes

AI upskilling should deliver clear value, and the best way to demonstrate that is by identifying and monitoring success metrics and optimizing your program based on what the data tells you.

Some interesting metrics to track include:

• % of workflows augmented by AI (team-level or org-wide)
• Time saved per task
• Employee-reported confidence with tools
• Manager feedback on performance or process improvements

Use these inputs to build a lightweight dashboard or tracker. You can combine tool usage reports (e.g., ChatGPT or Notion AI activity) with a tool like Leapsome Surveys to get more context on your data.

Then, reassess your AI upskilling program every 90 days or so. Retire content or formats that aren’t being used, double down on what’s working, and rotate in new workflows based on evolving business needs. 
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Sample 90-day AI upskilling sprint

You don’t need a year-long roadmap to start making AI upskilling progress. A focused 90-day sprint can build momentum, surface early wins, and help refine your approach before scaling across the business.

Here’s a sample rollout plan to get you started:

Week 1-2: AI awareness

• Introductory sessions covering what AI is (and isn’t)
  
• Tool walkthroughs and organizational examples
  
• Basics of ethical AI use, data privacy, and internal policies

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Weeks 3-4: Role-specific use

• Team workshops using real prompts and workflows
  
• Peer demos, for example: “how I used AI to speed up [x] task”
  
• Add a feedback loop to capture early insights

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Weeks 5-6: Workflow redesign

• Different teams identify specific processes they think they can optimize with AI
  
• Quick prototype development or SOP creation
  
• Internal sharing, feedback, and iteration 

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Weeks 7-8: Internal sharing

• Slack threads or async videos showing results
  
• Shout-outs in team meetings for successful use cases
  
• “What I learned” sessions led by early adopters

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Weeks 9-10: Reinforcement

• Refresher microlearning via a platform like Leapsome Learning
• Managers prompt team members for feedback and new insights during 1:1s and team check-ins
• Answer new questions and remove blockers

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Weeks 11-12: Impact tracking

• Send out pulse surveys to track confidence, tool usage, and blockers
  
• Measure time saved and specific workflow improvements
  
• Report wins back to leadership with clear, tangible data

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Turn AI upskilling into a long-term advantage

Upskilling your workforce is a long-term investment in resilience, agility, and growth. As AI tools evolve, companies that build adaptable, AI-literate teams will move faster, work smarter, and retain top performers who want to stay ahead.

By focusing on role-specific workflows, mixing hands-on formats, and tracking what works, you can build an AI upskilling program that’s both scalable and sustainable. With tools like Leapsome Learning, Surveys, and Reviews, HR and L&D teams can go beyond theory and turn skill development into a competitive edge.

AI isn’t replacing your workforce. But companies upskilling now will set the pace for everyone else.
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