AI literacy: What it is, why it matters, and how to build AI skills

Organizations need people with AI skills to use, build, and manage AI tools and systems effectively. But the specific skills they need—and how to build them—isn’t always clear.

In this article, we break down what AI literacy means today, why it matters, and how to help your teams develop relevant AI knowledge.

AI literacy means understanding foundational AI concepts and capabilities to use the technology responsibly and effectively.

Legislation like the EU AI Act formally defines AI literacy as the “skills, knowledge and understanding that allow providers, deployers and affected persons, taking into account their respective rights and obligations in the context of this Regulation, to make an informed deployment of AI systems, as well as to gain awareness about the opportunities and risks of AI and possible harm it can cause.”

To put it another way, when employees understand AI, they’re empowered to make smarter decisions and work more effectively.

AI literacy is the first step towards getting value from AI investments, preparing for new regulations, and controlling tech spend. Here’s why AI literacy is so important:

Organizations are investing billions of dollars in AI technology, but pilots don’t always make it to production. In fact, 95% of generative AI pilots at companies are failing, and 88% of agentic AI pilots never reach widespread deployment.

One of the biggest barriers holding them back? A lack of people with the right AI skills. According to the Tech Skills Report, 48% of IT professionals have abandoned projects due to a lack of tech skills.

If emerging AI jobs are anything to go by, organizations recognize the need for people with AI skills. But hiring for these niche roles can be expensive and time-consuming. In most cases, it’s faster and more cost-effective to invest in your current employees, build their AI skills, and help them grow into AI roles.

Without proper governance and oversight, AI costs can quickly spiral out of control. (According to some reports, one company received a $500 million Claude bill after forgetting to set usage limits for employees.)

AI literacy helps people understand AI costs and keep them to a minimum. This includes setting up guardrails and token limits, improving agentic visibility, and practicing effective prompt engineering to maximize efficiency.

AI has given rise to new regulations like the EU AI Act. Under that legislation, organizations must take measures to support AI literacy among their staff. In situations like this, AI literacy is no longer optional training. It’s a requirement for many organizations.

In practice, the exact capabilities someone needs to be AI literate will depend on their role, along with their organization’s goals or tech stack. 

Before you start upskilling teams, ask them to take a skill assessment. This will help you understand your team’s current skills and opportunities for growth. You can then design custom upskilling programs that take their needs into account. 

In general, though, these skills can help your teams get baseline AI literacy.

First and foremost, everyone in your organization—whether they occupy a tech role or not—needs to understand how AI works at a high level. This includes foundational AI concepts, capabilities, and responsible use.

Depending on their existing skills and gaps, cover concepts like how generative AI works, when to use it, and what responsible use looks like, specifically within the bounds of your organization’s policies. 

From there, cover the basics of large language models (LLMs), Retrieval-Augmented Generation (RAG), agentic AI, prompt engineering, and AI ethics. 

The goal is high-level understanding tailored to roles and responsibilities, not an overwhelming amount of deep content for everyone.  

Helping teams get familiar with AI tools and services, and how to choose the right one, is also helpful. ChatGPT, Copilot, Gemini, Google Bard, Anthropic Claude, and Midjourney are all solid starting points. 

Uncover the best AI courses to learn AI skills.

The people building and managing AI tools and systems need more than foundational knowledge to be considered AI literate. They need practical skills to build, test, deploy, and scale AI solutions with confidence.

If you’re building an AI-ready engineering team, for example, they’ll need a deep understanding of—and practical experience with—agentic coding tools, token and cost management, AI infrastructure, security, and more.

According to the Pluralsight Tech Learning Pulse, the top AI skills tech teams are focusing on include: 

• Claude and Claude Code

• Agentic AI, multi-agent systems, and Model Context Protocol (MCP)

• Prompt engineering and generative AI for developers

• LangChain and LangGraph

• Retrieval Augmented Generation (RAG)

These AI literacy courses and learning paths for developers can help:

• Anthropic Claude 3

• Model Context Protocol (MCP)

• Prompt Engineering

• LangChain

• Retrieval Augmented Generation (RAG) for Developers

Whether you’re upskilling your entire organization or focusing on your engineering team, AI literacy is a moving target. Technology is constantly changing, and AI in particular is changing faster than most.

As it does, the skills and knowledge teams need to complete AI projects and drive business value will change, too. Continually building your team’s skills is key to keeping up and delivering consistent value with your investments.

Not sure where to start? AI Academy is our end‑to‑end program designed to upskill employees from AI literacy to agentic capability. By building a shared language and understanding of AI, AI Academy takes your organization from individual AI productivity to scalable transformation and measurable business outcomes. Learn more about AI Academy.