AI Skills Are Splitting. Most Training Programs Only Cover One.

AI Skills Are Splitting. Most Training Programs Only Cover One.

AI is no longer confined to specialized teams. It’s becoming part of how work gets done across IT, cybersecurity, and beyond.

But while demand for AI skills is rising, the nature of that demand is changing in a way many training programs have not fully caught up to. AI skills are no longer one category. They are splitting into two distinct directions, and most training programs are still built as if they are not.

The Data Behind the Shift

The growth of AI in the workforce is not subtle. Mentions of AI skills in job postings have surged in recent years, with triple-digit growth across 2023 and 2024. At the same time, demand is no longer limited to highly technical roles.

Some reports show an 800% increase in job postings mentioning generative AI since 2022, including roles that are not traditionally considered technical.

At the other end of the spectrum, many of the most visible AI roles remain small in volume. Positions like prompt engineering account for less than 0.5% of job postings.

It has become clear that AI is not only creating a set of new jobs. It is also massively reshaping a broad range of existing ones.

The Two Directions AI Skills Are Taking

As AI adoption expands, the skills required to work with it are diverging into two primary paths.

Applied AI (The Operator Path)

These professionals apply AI within existing environments to improve efficiency, automate processes, and enhance decision-making. They are not building systems from scratch.

This includes roles like:

• Cybersecurity analysts
• System administrators
• IT support specialists
• Network engineers

AI is becoming embedded in how these roles operate.

AI Development (The Builder Path)

This path focuses on designing and maintaining AI systems. These roles involve programming, often with Python, along with machine learning frameworks and automation pipelines. Python remains the most requested language in AI-related job postings and continues to dominate development environments. Demand here is strong, but more specialized.

Where Training Falls Out of Alignment

Most training programs still treat AI as a single track.

In practice, they tend to fall into one of two extremes:

• Deep theory and model development
• Surface-level exposure to AI tools

Both approaches can miss how AI is actually being used on the job. While nearly 75% of knowledge workers are already using AI on the job, most report receiving little to no formal training. The gap is no longer about access. It’s now about the ability to apply AI effectively and securely in real-world environments. Professionals need to be able to appropriately apply AI in context.

That requires the ability to:

• Integrate AI into IT and security workflows
• Improve efficiency through automation
• Use AI within governance and compliance boundaries
• Understand how AI systems function across their lifecycle

This is applied implementation. It’s where many programs fall short, and where more hands-on approaches are starting to stand out. ACI Learning’s AI Skill Labs, for example, are designed to give learners practical experience applying AI in real-world IT and cybersecurity scenarios. Instead of just introducing concepts, they focus on building the ability to use AI within workflows, from prompt engineering to security and lifecycle considerations.

From Application to Capability

For most learners, applied AI is the starting point, not the endpoint.

In practice, skill development tends to follow a progression:

• Using AI tools to improve day-to-day work
• Automating tasks and workflows
• Building or customizing solutions using programming and data skills

This progression reflects growing workforce needs. Not just users of AI, but professionals who can extend and adapt it. Training that only supports one stage limits that growth.

What Effective AI Training Looks Like

To stay aligned with demand, training needs to do more than introduce AI concepts or tools. It needs to help learners build usable capability, and to feel empowered by AI, rather than intimidated by it.

That includes:

• Hands-on application
  Learners actively use AI in realistic, role-based scenarios
• Workflow integration
  AI is taught as part of IT and cybersecurity processes, not in isolation
• Security and governance awareness
  AI usage aligns with compliance, risk management, and responsible practices
• Clear progression paths
  Opportunities to move into deeper skills like Python, automation, and system design

This combination is what turns AI from a topic into a functional skill set. Programs are also starting to embed AI directly into the learning experience itself. For example, tools like Coach Maci, ACI Learning’s integrated AI coach within the myACI platform, give learners real-time guidance as they work through training. Because it’s trained on internal expert content, it helps reinforce concepts, answer questions, and keep learners moving without breaking their flow.

Building a Complete AI Learning Pathway

The goal for training programs should be to connect applied AI and technical depth, not to choose between them.

ACI Learning supports this by combining:

• AI Skill Labs focused on applied implementation
  Giving learners hands-on experience with AI workflows, prompt engineering, and secure usage across the lifecycle
• Broader IT and technical training
  Including Python, programming, and foundational skills that support deeper development

This allows learners to start with practical application and expand into more advanced capabilities over time.

Preparing Learners for What Comes Next

AI is expanding the number of ways people work with technology, not narrowing them. Most roles now require the ability to apply AI effectively. Some require the ability to build it.

The most effective training programs recognize both and create pathways between them.

That is what prepares learners not just for today’s roles, but for how those roles are continuing to evolve for tomorrow.