The AI Skills Gap Is Growing: What Employers Should Do Now

AI use at work is growing quickly.

But employees aren't necessarily moving at the same speed.

Some employees are already using AI every day to research, summarize information, create content, analyze data and complete routine tasks. Others may have little experience with it. Managers may be experimenting with AI without clear guidance. And some employees may be wondering what AI means for their jobs in the first place.

New workforce research released this week highlights just how wide that gap is becoming.

For employers, the next AI challenge may not be getting access to the technology.

It may be making sure your workforce is actually prepared to use it.

Is There an AI Skills Gap in the Workplace?

Yes. New workforce research suggests AI adoption is growing faster than access to the training and development employees need to keep up.

PwC's newly released 2026 Global Workforce Hopes and Fears Survey found that nearly two-thirds of workers surveyed had used AI at work during the previous 12 months, up 10 percentage points from the prior year.

At the same time, only 51% said they have access to the learning and development resources they need, down from 59% the previous year.

Among the group PwC describes as the workforce's “engine room,” representing 56% of workers surveyed, only two in five reported having access to the learning and development resources they need.

That creates a growing divide between employees who are becoming comfortable working with AI and those who may be getting left behind.

Read PwC's 2026 Global Workforce Hopes and Fears Survey

Why Does the AI Skills Gap Matter for Employers?

The issue goes beyond whether an employee knows how to write a good prompt.

AI is beginning to change how work gets done.

PwC found that employees who use AI every day reported greater confidence in their job security, greater confidence in their ability to learn new skills and higher levels of trust in management than employees who use AI less frequently.

Daily AI users were also more likely to say AI helps them with some of the more difficult parts of their jobs.

But the benefits aren't reaching everyone equally.

If one group of employees learns how to use AI effectively while another doesn't have the training, access or opportunity to develop those skills, employers could eventually see very different levels of productivity and confidence across the same workforce.

That's why AI readiness needs to include more than buying technology.

It needs to include people.

AI Is Also Changing Jobs, Not Just Tasks

Another employer survey released this week points to a related issue.

Littler's 2026 Labor Survey found that 67% of respondents expect increased AI use to reshape job responsibilities, while 42% expect work to be redistributed across teams and 30% anticipate reductions in entry-level roles.

Yet only 9% said their organizations feel very prepared to manage the labor and employee-relations issues AI adoption may create.

And while employers are communicating with employees about AI in different ways, only 17% reported doing so proactively and consistently.

Read Littler's 2026 Labor Survey findings

That is an important distinction.

Introducing AI isn't only a technology project.

It can become a workforce change project.

If AI changes what employees do, how work is distributed or what managers expect from their teams, employers need to think about training, communication, job responsibilities and human oversight along with the technology itself.

What AI Skills Do Employees Actually Need?

Not every employee needs to become an AI expert.

The skills employees need will depend on their role, the technology they're using and what the organization allows AI to do.

But there are several areas employers should consider when developing AI training.

1. Knowing When to Use AI

Employees should understand where AI can actually make their work easier.

That might include summarizing information, organizing ideas, drafting routine communications, researching a topic or helping complete administrative tasks.

Training should focus on real workplace use cases, not simply explaining what AI is.

2. Knowing When Not to Use AI

Knowing when not to use AI may be just as important.

Employees should understand what information can and cannot be entered into AI tools, particularly when working with confidential company information, employee information, customer data or other sensitive material.

This should also connect directly to your organization's AI workplace policy.

3. Reviewing AI-Generated Information

AI can produce information that sounds convincing and is still wrong.

Employees need to understand that AI-generated information should be reviewed before it is relied upon, shared or used to make decisions.

Human judgment still matters.

4. Understanding Human Oversight

Some uses of AI carry more risk than others.

Using AI to help organize meeting notes is very different from relying on AI to evaluate a job candidate, make an employment recommendation or interpret an HR policy.

The National Institute of Standards and Technology's AI Risk Management Framework emphasizes defining human roles and responsibilities, establishing oversight procedures and providing appropriate training for people using and overseeing AI systems.

NIST AI Risk Management Framework

Employers should decide where human review is required rather than leaving that decision to individual employees.

5. Knowing How AI Affects Their Role

Employees don't only need instructions for using AI.

They also need context.

If AI is changing a workflow, responsibility or expectation, explain what is changing and why.

That becomes increasingly important as organizations move from employees experimenting with standalone AI tools to AI becoming embedded directly into the technology they use every day.

Should Every Employee Receive the Same AI Training?

Probably not. AI training should reflect how different employees and roles actually interact with the technology.

A general AI awareness program can give everyone a common foundation, but employers may need different levels of training for different groups.

For example:

Employees may need guidance on approved tools, appropriate use, protecting information and reviewing AI-generated work.

Managers may need additional training on human oversight, employee questions, changing workflows and when AI should not influence employment decisions.

HR teams may need deeper guidance around recruiting, employee data, policies, documentation and emerging employment requirements.

Leadership may need to understand governance, organizational risk, workforce impact and how AI investments connect to business priorities.

The goal isn't to turn everyone into an AI specialist.

It's to make sure people know enough to use the technology appropriately for their role.

How Can Employers Identify an AI Skills Gap?

Start by finding out what's actually happening inside your organization.

Ask:

• Which AI tools are employees already using?
• Which departments are using AI most often?
• What are employees using AI to do?
• Which tools are approved?
• Do employees know what information they should never enter into an AI system?
• Have employees received any formal AI training?
• Are managers prepared to answer employee questions about AI?
• Are employees reviewing AI-generated work before using it?
• Have any job responsibilities or workflows changed because of AI?
• Do employees know when human review is required?
• Is there a process for raising questions or concerns about AI use?

You may find that AI adoption is much further along in some areas than others.

That's useful information.

The goal isn't to get every employee to exactly the same level. It's to understand where the gaps are and determine what support different groups need.

Don't Forget About the Employees Who Aren't Using AI Yet

There is a natural tendency to focus on the employees who are already excited about AI.

They are experimenting. They are finding efficiencies. They may even be helping other employees figure it out.

Employers should support that innovation.

But they also need to pay attention to everyone else.

PwC's research suggests the employees furthest along with AI may have very different workplace experiences than the broader workforce. Nearly a third of the AI-skilled “front-runner” group surveyed said they were very or extremely likely to change employers within the next year.

Employers therefore face two challenges at once:

Help more employees develop the skills they need while continuing to develop and retain employees who are already ahead.

That makes AI training part of a larger workforce-development conversation.

AI Training Shouldn't Be One and Done

AI is changing too quickly for a single annual training session to be enough.

New tools appear. Existing tools add capabilities. Employees discover new use cases. Regulations and workplace expectations continue to evolve.

A better approach is to treat AI education as an ongoing process.

That could include:

• Initial AI awareness training
• Role-specific training
• Real examples of approved and prohibited uses
• Manager training
• Updates when new AI tools are introduced
• Refresher training as policies change
• A clear way for employees to ask questions
• Regular reviews of how AI is actually being used

Employers should also revisit their AI policies as their use of the technology evolves.

CTR's AI Workplace Policy Toolkit can help organizations think through acceptable use, employee responsibilities, data protection, human oversight and other workplace considerations.

AI Readiness Is About More Than Technology

Earlier this year, we talked about why employers need to start preparing for AI in the workplace.

That conversation has already moved forward.

AI is becoming easier to access, more capable and increasingly embedded into everyday workplace technology.

The next question is whether organizations are bringing their employees along with it.

In conversations with employers throughout 2026, we've seen organizations at very different stages of AI adoption. Some are already incorporating AI into everyday workflows. Others are experimenting. Some are still figuring out where to start.

Even within the same organization, different departments can be at completely different stages.

That's why there isn't one finish line for AI readiness.

A practical approach starts with five areas:

Policy. People. Skills. Processes. Technology.

Do you have clear policies?

Do employees understand what's changing?

Do they have the skills and training they need?

Have you determined where AI fits into your processes and where people still need to lead?

And do you understand the technology being used across your organization?

Those questions matter just as much as which AI tool you choose.

What Should Employers Do Now?

You don't need to have every answer before employees begin using AI.

In many organizations, they're already using it.

Start by understanding where AI is showing up today. Talk to employees and managers. Identify gaps in knowledge and training. Establish clear expectations. Determine where human oversight is necessary. Then build training around the real ways employees will use AI at work.

Most importantly, don't treat AI readiness as an IT project alone.

AI is a technology change, but it's also a people change.

Organizations that invest in both will be in a much stronger position to use AI effectively as the workplace continues to evolve.

Explore AI in the Workplace

CTR Payroll | HR has created practical resources to help employers navigate AI in the workplace, from policy development and employee guidance to emerging AI-powered workforce technology.

Explore CTR's AI in the Workplace Resource Center

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