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You cannot train employees on AI literacy until you know which specific skills they need.
AI literacy in the workplace is the ability to use AI tools with judgment, which means knowing what a model does well, checking what it produces, protecting the data you put into it, and recognizing when a person is better-suited for a task. Every employee needs the 10 skills in this article, regardless of their role.
Why Does AI Literacy Matter for Every Employee, Not Just Developers?
AI arrived in every department almost at once without giving employees a chance to catch up. Someone in marketing had to start drafting customer-facing copy with it, while someone in support had to figure out how to build an automation that can pull account history from the customer relationship management (CRM) system and draft a reply to the customer.
The problem is that access to AI tools moved faster than AI literacy skills did. We call this the AI Capability Gap. The Conference Board found in July 2026 that 55% of workers use AI regularly while only 33% took part in employer AI training in the past six months1. What's worse, 28% of respondents say their employer gave no AI training at all.
Training makes a measurable difference. Boston Consulting Group found that only one-third of employees say they’ve been properly trained, and that regular AI use is sharply higher among those who receive at least 5 hours of training. Without the right skills, it's difficult to make the most of new tools.
What Happens When Employees Use AI Without These Skills?
Employees without AI training tend to make small, routine errors. Though not catastrophic on their own, they can quickly add up into a significant problem.
KPMG found in April 2025 that 44% of US respondents knowingly use AI at work in ways their employer hasn't authorized.3 Only 54% believe their organization even has policies for responsible AI use.Those employees have a deadline and a tool that helps, so they make do. If their company never set up a specific AI policy or program, employees will figure out how to incorporate it through trial and error.
When that's the case, employees are bound to repeat the same mistakes. In marketing, someone might draft a customer-facing policy update with AI and publish it with a commitment the company can't honor. Or an analyst could build a quarterly forecast based on a number the model invented.
What these employees lack is a defined set of skills to prevent those mistakes.
What Are the Core AI Skills Every Employee Needs?
The 10 AI skills every employee needs have more to do with judgment than technical skill. Benchmark your workforce against this list to find where your weak spots are.
Skill 1. Knowing What AI Can and Cannot Do
AI models can draft and summarize reliably, but they don't handle precise arithmetic or recent events very well. Employees who can tell the difference can use the right tool for the right task.
Skill 2. Writing Clear, Specific Prompts
An employee with prompt engineering skills can give the model enough context and a clear format for its answer. Vague questions produce vague answers that take longer to fix than writing a better prompt would have taken.
Skill 3. Verifying Output Before You Use It
AI models can output something confidently that sounds right, but is actually fully made up. Employees who check every factual claim before passing it along prevent an incorrect fact from landing on a client report.
Skill 4. Protecting Sensitive Data in AI Tools
Employees need to know which tools their company approved and what they can do with each one. Generally, you want to keep customer records, credentials and unreleased plans out of free consumer accounts that might be training on your inputs.
Skill 5. Recognizing Bias and Gaps in AI Responses
A model reflects the patterns in its training data, including that data’s gaps and biases.. Employees reviewing any AI output should think about which perspectives the model left out to catch bias.
Skill 6. Knowing When Not to Use AI
Some choices involve ethics and emotions that an AI model cannot fully grasp. Employees should route performance feedback, legal commitments and anything that affects a person's livelihood to a human being even if AI helped produce the first draft
Skill 7. Disclosing and Documenting AI Use
Responsible AI use includes telling colleagues where AI contributed to the work. A colleague reviewing the work needs to know which parts came from a model and what the employee already verified.
Skill 8. Understanding the Security Risks in AI-Generated Code and Automations
Employees outside engineering are now building working tools with AI. A marketer might connect a chatbot to the company database to answer questions about customers. That chatbot can read every customer in the database, and without the right access controls, anyone with access to it could read confidential customer information. The same goes for scripts and apps built with AI coding assistants, which can expose secrets, tokens, or data if no one reviews them. Security Journey’s AI Access training teaches non-developers these basics, from secret checks to pull requests and audit logs.
Skill 9. Working Safely With AI Agents
An agent acts on an employee's behalf. The employee writes one instruction and the agent can then take a series of actions all on its own, like sending emails or updating records. That's why the permissions the agent has are so important. Employees should give agents only the access a task requires, require approval before anything irreversible, and know that agents can be manipulated by instructions hidden in the emails, web pages or documents they read.
Skill 10. Turning One-Off Prompts Into Repeatable Workflows
An employee who saves a working prompt and shares it can move the whole team forward. Companies that collect those prompts and create a company library can turn a personal trick into something the organization relies on.
How Can Organizations Build AI Literacy Across Teams?
Start by measuring what your employees can do with AI before you choose what to teach them. Many companies skip this step and buy generic training for problems they don't actually have.
Gallup surveyed 102 chief human resources officers at Fortune 500 companies in 2026 and found that 49% of them offer role-specific AI learning.4 The largest companies already train by role because they know that recruiters and financial analysts use AI tools very differently.
Security Journey built AI training for employees on that principle. Before anyone starts a lesson, an assessment shows what each employee can already do with AI and the platform routes them into an appropriate learning track from there.
Most employees will learn these skills either way. Training determines whetherIthey learn them before or after the mistakes.
References
- The Conference Board. “Report: Most Organizations Are Preparing Workers for Today’s AI, Not Tomorrow’s Jobs.” July 2026. https://www.prnewswire.com/news-releases/report-most-organizations-are-preparing-workers-for-todays-ai-not-tomorrows-jobs-302836842.html
- MIT NANDA. “The GenAI Divide: State of AI in Business 2025.” July 2025. https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf
- KPMG LLP. “The American Trust in AI Paradox: Adoption Outpaces Governance.” April 2025. https://kpmg.com/us/en/media/news/trust-in-ai-2025.html
- Gallup. “AI’s Effect on Workplace Culture.” August 2026. https://www.gallup.com/workplace/712976/ai-effect-workplace-culture.aspx
