A simple example is an HR coordinator who has always written job descriptions and onboarding FAQs by hand, and now learns to draft them with the company’s approved AI tool, check the output for accuracy, and adapt it to the company’s voice. The role stays the same, but the way the work gets done has changed, and the company has invested in helping that person adapt.
What Is Upskilling
Upskilling is a planned investment an organization makes in its current workforce. It is not a one-off training event, and it is not something employees are left to figure out on their own. When a company upskills its people, it recognizes that a role someone already does well is changing, and it gives that person the tools, time, and guidance to grow with it.
The key word is deliberate. Good upskilling is designed around what the work actually requires rather than around whatever course happens to be available. It also builds on what people already know. An operations manager who understands process documentation does not need to become a technology expert. They need enough skill with AI to draft, review, and update procedures faster and more consistently, so the organization is adding to a foundation that is already there.
What Are Examples of Upskilling at Work
The clearest way to understand upskilling is to look at what people actually learn in each function. Right now, AI is the main example in most organizations, so each example below names the task that changes and the skill the person builds to handle it well.
HR Teams
HR professionals are using approved AI tools such as ChatGPT or Copilot to draft job descriptions, policy FAQs, and onboarding materials. The skill they build is directing the tool well and judging the result: writing clear instructions, checking drafts against current policy, and knowing which information should never go into a prompt, such as candidate or employee personal data. Our page on AI training for HR leaders covers how HR teams approach this in more depth.
Sales Teams
Sales reps are using AI to research accounts and prepare for client calls. The skill is knowing what to ask for, verifying what comes back before it reaches a client, and understanding what client information the company allows them to share with the tool.
Finance Teams
Finance professionals are using AI to draft plain-language commentary on budget variances for management reports. The skill sits on top of existing financial knowledge: checking every AI-drafted explanation against the source numbers and catching the places where fluent writing hides a wrong conclusion.
Operations Teams
Operations teams are using AI to draft and update standard operating procedures, which often go out of date because nobody has time to maintain them. The skill is turning a working process into clear instructions an AI can follow, then reviewing the output with the people who actually do the work.
Managers
Managers have a distinct upskilling need that is easy to overlook. Beyond using AI themselves, they are learning to lead a team that uses it: setting expectations about when AI-assisted work is appropriate, reviewing AI output critically, and giving useful feedback on AI-generated drafts. Our guide to how AI literacy programs for managers differ from generic AI workshops explains why managers need their own preparation.
Upskilling Beyond AI
Not all upskilling involves AI. A customer service team learning a new CRM platform is being upskilled, and so is a new manager learning how to give structured feedback. The common thread is that the organization has spotted a capability gap in an existing role and built a way to close it. For more examples of where AI fits into everyday work, see our guide to top AI use cases in business.
What Is the Difference Between Upskilling and Reskilling
Upskilling and reskilling are often mentioned together, and while they are related, they solve different problems. Upskilling adds new capabilities to a role someone already holds, so the person stays in their position while the work around them evolves. Reskilling prepares an employee for a different role entirely, either because their position has changed so much that it is effectively a new job, or because the organization wants to move them into a different function rather than hire from outside.
Most organizations need both at different times. For HR and L&D leaders building an AI adoption program, though, upskilling is usually the more immediate priority, because the goal is to help existing teams work better with new tools rather than move people into new positions.
Why Are Companies Focused on AI Upskilling Right Now
The scale of the shift is significant. In its Future of Jobs Report 2025, the World Economic Forum reports that employers expect 39% of workers’ core skills to change by 2030, and that if the world’s workforce were 100 people, 59 would need training by 2030.
Most employees have not yet been prepared for AI specifically. A global study by the University of Melbourne and KPMG found that only 47% of employees say they have received AI training, and only 40% say their workplace has a policy or guidance on generative AI use. Many organizations have given people access to AI tools, but far fewer have built the structured programs that help them use those tools well. For the broader business case, see our post on why AI training is essential for employees and businesses.
Why Is AI Upskilling Different From Traditional Upskilling
Traditional upskilling usually has a finish line. A team learns a new CRM, the training ends, and the skill stays useful for years. AI does not work that way, because the tools keep changing and the range of tasks they can help with keeps growing. Training people on one version of one tool’s features gives them a skill with a short shelf life.
What lasts is the habit of using AI well, whatever the tool. That is why we describe AI upskilling as a ladder that teams climb over time rather than a course they complete:
- Ask: people use AI for one-off questions and requests in the moment.
- Save: they keep the prompts and context that work, so the whole team can reuse them.
- Chain: they link prompts into multi-step workflows, such as research, draft, refine, and format.
- Delegate: they define a task clearly enough that AI can handle it with light supervision and human review.
Each rung is a change in how people work, not a new set of features to memorize, so a team that has climbed the ladder can adapt when the tools change. Our guide to what AI fluency is and how teams build it explains each rung and the habits that move a team up.
What Does Good Upskilling Look Like in Practice
Effective upskilling has a few qualities that separate it from a training session people forget the following week.
It Is Tied to Real Work
The strongest programs are built around what people actually do in their jobs rather than generic scenarios. A sales team learning to use AI for call preparation should practise on the kinds of accounts they really work on, because people are more likely to use what they have practised in context.
Managers Are Part of It
Research on how training transfers to the job supports this. A meta-analysis by Blume, Ford, Baldwin and Huang found that whether people apply training at work is linked to a supportive work environment. When managers are upskilled alongside their teams, people have someone nearby who expects and encourages the new way of working.
It Continues After the Session
A workshop can introduce a skill, but it rarely changes behaviour on its own. Good upskilling includes follow-up, regular practice on real tasks, and teams learning together so colleagues can share what works.
How Do Companies Start an Upskilling Program
You don’t need a large program to begin. A focused start makes it easier to learn what works before you scale.
Pick the Roles Where Work Is Changing Most
Look for the teams whose daily tasks are shifting fastest, often because new tools have already arrived. Those are the roles where upskilling makes the biggest difference soonest.
Choose One or Two Real Tasks per Role
Rather than training on everything a tool can do, choose the specific tasks each role does often, such as drafting job descriptions in HR or preparing call notes in sales. Building the program around those tasks keeps it practical.
Start With a Pilot Team
A single team going first lets you see what helps, what gets in the way, and what people need after the training ends. Their experience shapes the program before it reaches everyone else.
Bring Managers In From Day One
Prepare managers before or alongside their teams, so they can answer questions, set expectations, and reinforce the new skills once the session is over.
If AI is your starting point, AI First® Fundamentals is the first step in Teamland’s AI First® Suite, building shared fluency with tools such as Copilot, ChatGPT, and Claude, along with prompt craft and safe-use patterns. To see how a broader program fits together, visit our Corporate AI Training page.
Once a program is running, the next question most HR and L&D leaders face is whether it connects to what the business needs. Our post on what causes AI upskilling for employees to feel disconnected from KPIs covers exactly that, and it is worth reading before you finalize your program design.
Not sure where your organization stands? Take the AI First® Readiness Report to get a score and a gap map that show where to focus first.
Frequently Asked Questions
What Is an Example of Upskilling
A common example is an HR coordinator who learns to draft job descriptions and onboarding materials with the company’s approved AI tool, then checks and adapts the results. The person’s role has not changed, but the organization has added a new capability to how that role is done. Other examples include a sales team learning to prepare for calls with AI and a finance team learning to draft report commentary with AI assistance.
Is Upskilling the Same as Training
The two overlap, but they are not identical. Training is the broad term for any activity that builds knowledge or skill, while upskilling refers specifically to building new capabilities for someone’s current role because that role is changing. All upskilling involves some form of training, but not all training is upskilling.
What Skills Should Employees Be Upskilled In
It depends on how each role is changing, but most organizations start with the skills their people use every day. Right now that usually means using AI tools well and safely, alongside skills that become more valuable as AI takes on routine tasks, such as judgment, communication, and working with data. The best place to start is the tasks in each role that are changing fastest.
Who Is Responsible for Upskilling Employees
From the employer’s side, upskilling is an organizational responsibility rather than something left to individuals. HR and L&D leaders usually design and own the programs, managers support learning within their teams, and senior leaders set the expectation that building new skills is a priority. When upskilling is left entirely to individuals, it tends to be uneven and harder to connect to what the business needs.
