Required reading: Exploring the Department of Labor’s AI Literacy Framework

A guide to understanding and implementing effective AI skills in the workplace

Takeaways

• The U.S. Department of Labor has released its Artificial Intelligence Literacy Framework, aimed at accelerating AI skills development nationwide.
• Despite increased investment in AI, only a small percentage of companies have a mature approach to AI deployment, highlighting a significant implementation gap.
• Many organizations struggle to translate AI pilot projects into everyday operational value, with studies showing that up to 95% of GenAI pilots fail to deliver expected outcomes.
• The Framework emphasizes the importance of human skills in AI adoption, suggesting that people remain central to successful AI integration in the workplace.

On February 13, 2026, the U.S. Department of Labor (DOL) released its Artificial Intelligence Literacy Framework. According to Secretary of Labor Lori Chavez-DeRemer, the new framework “provides guidance that will help accelerate effective AI skills development across the country.” 

Here’s a look at what the framework contains, why it’s necessary for businesses and how it can help organizations improve employee education.

Current challenges in AI adoption

Enterprises are increasingly confident that AI will drive business value. According to McKinsey, 92% of companies plan to spend more on AI over the next three years. Interestingly, however, just 1% of leaders say their companies have a “mature” approach to deployment.

This is a natural consequence of AI’s rapid development and adoption. Even as companies spend more on generative AI solutions, build large language models (LLMs) and integrate agentic AI tools, they’re not entirely sure what outcome they’re aiming for — or how to measure it when it arrives. The result is a disconnect between what AI could do and what it’s actually doing for businesses. As noted by the MIT State of AI in Business 2025 study, 95% of GenAI pilots fail when their value is lost in translation from proof of concept to everyday operations.

The AI Literacy Framework lays the groundwork for businesses to bridge the gap — and it starts with the human element. “Right now, there are these incredibly loud, noisy conversations about how AI is going to impact work,” says Amanda Bickerstaff, CEO and president of nonprofit group AI for Education. “The labor department is coming out with almost a counter to that. Humans will still be the valuable component, not the AI systems, and it's more what they can do together.”

Key concepts of the AI Literacy Framework

The AI Literacy Framework features five Foundational Content Areas for workers:

1. Understand AI principles

This content area focuses on understanding how AI works, what it does, and where its limitations lie. In practice, this might include a discussion of basic AI tools such as first-generation chatbots and how they compare to modern agentic iterations. It may also include a discussion about the difference between AI and machine learning (ML) and how they relate to frameworks such as natural language processing (NLP) and LLMs.

2. Explore AI use cases

The evolution of generative AI has expanded AI use cases. It’s also created workforce tensions as staff worry that they may be sidelined by smart solutions or replaced by intelligent agents. This content area explores different AI tools and identifies how they can complement human expertise. For example, intelligent agents may be used to field customer service calls and collect key client information before passing requests on to human experts. Companies may also leverage AI-enabled analytics solutions to track and manage trends related to sales and marketing efficacy, service histories and conversion rates. 

3. Direct AI effectively

Despite rapid advancements, AI is nowhere near human-level general intelligence. The DOL’s third content area speaks to directing AI efficiently through the use of clear prompts that produce desired outputs.  

4. Evaluate AI outputs

The accuracy and relevancy of AI outputs depend on the amount and type of data available, the prompts used and the limitations of any underlying ML models. The result? Users can't take AI answers at face value. The fourth DOL content area targets evaluation: Teaching staff to assess results for accuracy and relevance and giving them the tools they need to iterate on AI outputs.

5. Use AI responsibly

Responsible use rounds out the framework’s foundational content areas. Three components define responsible use: AI must be used ethically and securely, operations must protect critical data, and businesses must take accountability for AI outcomes. 

The Framework also includes seven delivery principles to help companies support employee education:

• Enable experimental learning
• Embed learning in context
• Build complementary human skills
• Address prerequisites to AI literacy
• Create pathways for continued learning
• Prepare enabling roles
• Design for agility

What the DOL document means for businesses

While the AI Literacy Framework is primarily positioned as a foundational document for educational institutions and workforce agencies, it can also benefit businesses.

Consider upskilling. According to Deloitte, there’s a growing divide between AI adoption and employees with the skills to effectively use AI tools. This puts organizations in a tough position: To keep pace with the competition, AI tools and technologies are essential. Without human expertise, however, there’s a limit to how far these tools can go. The DOL framework offers a solid starting point for upskilling efforts. 

One example is ethical use. With many states now drafting and implementing legislation around the ethical use of AI in business, companies can’t afford tools that generate biased or inaccurate outputs. The framework’s third AI Literacy principle — building complementary human skills — helps address this issue. In practice, this means giving staff the skills and knowledge they need to evaluate and challenge AI outputs as necessary, rather than simply accepting intelligent results as fact. This human oversight is especially critical given the “black box” nature of many AI tools; while users can track data inputs and view actionable outputs, they don’t have visibility into data transformation.

Learning the ropes

AI is evolving. Chatbots have given way to agentic operations, and simplistic answers have been replaced by in-depth, multi-source responses. 

While humans remain the primary drivers of business value, AI is non-negotiable for organizations to compete in digital-first markets and deliver on the potential of their data. The DOL’s AI Literacy Framework offers a solid starting point to bring staff up to speed and help them work in tandem with AI technologies.

Secretary Chaver-DeRemer puts it simply: “The Department of Labor is committed to making sure all American workers are able to share in the prosperity that AI will create for our economy.”