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Over 75% of leaders and managers say they use generative AI several times a week in their workflows, according to a Boston Consulting Group survey. Among frontline employees, regular use has stalled at 51%, a plateau BCG calls the “silicon ceiling.” Still the takeaway is clear: most employees are already using AI at work. What's missing is AI training for employees, which only one-third of workers report receiving.
Untrained workers who leverage AI may miss out on opportunities to derive real value from their prompts. Their actions can also put the organization at risk by sharing data that's better off left private or using unapproved tools. This is the AI Capability Gap: organizations are giving people access to AI without helping them build the skills to use it well and within company policy. This guide explores how the right training can close that gap and help every team use AI better and more securely.
What Is AI Training for Employees?
AI skills training is a structured educational program that shows employees how to use AI tools effectively, responsibly, and securely on the job. The best programs are tied to each employee’s role, so people learn to apply AI to the work they actually do. A common thread among business leaders is that providing teams with AI tools is enough to jump-start productivity and realize gains. However, having access to AI tools doesn't mean that employees will unleash their full capabilities. Often, training is necessary to demonstrate what the tools can do and how employees can leverage them in their own daily tasks.
Why Does AI Training for Employees Matter Now?
A 2026 Jobs for the Future survey of U.S. workers found that just 36% say they had enough training to incorporate AI into their daily tasks, down from 45% a year earlier..2 Another global study from Melbourne Business School (MBS) found similar results, with only half of employees in advanced economies stating that their organization offers training in responsible AI use.3
Lack of dedicated AI training has downstream consequences for employers and their team members, including:
- Fewer efficiency gains: Employees who received AI training report a 76% jump in efficiency, compared to only 56% of those who use AI without formal training.3
- Lower Adoption: BCG found that regular AI use is sharply higher among employees who receive at least five hours of training.
- Heightened risk of security incidents: Untrained employees may not realize the risk of sharing sensitive data with AI tools or using unapproved ones. BCG found that more that half of employees who lack the AI tools they need will find alternatives and use them anyway.
- Too much trust in outputs: AI tools can hallucinate and produce inaccurate data. Training helps employees recognize mistakes and verify AI-generated output before relying on it.
The Gap Between AI Access and AI Capability
Providing employees with an AI tool usually isn't enough to realize its full benefits. Instead, workers need structured guidance to understand how to get the most from their prompts. And without it, leaders can see licenses and usage, but not who is actually using AI well.
Take this example. Say you have two developers working on updating an application's user registration page. One employee types in a basic prompt, such as "Write the code for a login screen." The other employee's prompt is more detailed, specifiying the programming language and framework , the fields and validation rules, and security requirements such as hashing passwords, rate-limiting log-in attempts, and never logging credentials. The second employee will likely get better and more secure results because the AI tool has more direction to work with.
This is where training pays off. A structured course would show both employees how to use the tools they're given, without wasting time on vague prompts, and how to review what the AI produces before it ships
The Risks of Untrained AI Use
Shadow AI is a growing phenomenon where employees use unsanctioned AI tools in their work. It poses a significant risk to businesses, since there's no control over what the employee shares or what tools they use. Theoretically, the worker could release sensitive customer or vendor data, private business strategies, and other critical information, potentially resulting in data loss or a breach.
Workers who don't receive AI training are more likely to make this mistake because they may not understand the consequences of doing so. Training on what not to share with AI, combined with a clear organizational policy for the use of AI tools, can mitigate this risk.
Growing Expectations for AI Literacy
When genrative AI chatbots went mainstream in late 2022, they hit with a lot of fanfare and minimal government oversight. That's changing quickly.Since February 2025, the EU AI Act introduced an AI literacy training requirement in Article 4. It applies to provider and deployers of AI systems,a group that includes organizations whose staff use AI tools at work. Following the Digital Omnibus on AI , which took effect on July 27, 2026, organizations must take measures to build AI literacy among their staff, with compliance focused on documented training and awareness programs. I
In February 2026, the U.S. Department of Labor followed up with its Artificial Intelligence Framework. While it's not a full-scale regulation, it does set guidelines that encourage employers to train employees in responsible AI use and evaluation. This framework sets the stage for how employers can integrate AI training and use in their organizations.
What Should AI Training for Employees Cover?
Generative AI training for employees should cover several core concepts. Additional training that maps to the worker's use case is also helpful. Here's a look at what courses may include.
How AI Works and Where It Falls Short
AI tools have a way of presenting answers with supreme confidence, which may lead a user to rely to heavilyon their output. But AI tools are not always right. Their outputs are based on patterns and probability, and some tools may hallucinate or present biased responses. Organizations can train workers on AI fundamentals and where mistakes are likely to occur. For example, training can show workers how to validate a response before incorporating it into their job.
Prompting as Structured Thinking
AI chatbots are trained to answer anything, even short prompts that contain only a word or two. However, the less you give AI to work with, the more likely you are to get a useless answer in response. A better approach is to give the chatbot as much context as you can without sharing data your AI policy prohibits.
For example, say you want the chatbot to create a spreadsheet you can use to track sales. A prompt such as "Make a spreadsheet to track sales" will yield a generic output. But a more effective approach might be: "Make a spreadsheet with the columns Date, Region, Product, Customer ID,, Order Number, and Total Sale. Add a line graph that connects to the data to track weekly and monthly sales." The second prompt is much more detailed and describes exactly what you need without using any customer data.
Evaluating and Verifying AI Output
Employees must treat AI outputs as a suggestion, not cold, hard facts. Training can show workers how to verify outputs against reliable sources and their own expertise. It can also help them identify logical errors that AI systems sometimes make. This is particularly important in high-stakes or customer-facing work, such as preparing financial statements or sales contracts.
Data Privacy, Security, and Responsible Use
Some data should never be shared with public or unapproved AI tools, and some should be shared only with tools your organization has approved for that purpose. Examples include:
- Sensitive personally identifiable information (PII)
- Medical and health diagnoses
- Business plans and private company data
- Credit card or financial information
- Private company documents and contracts
- Customer and vendor information
Organizations can adopt a company-wide AI policy that guides employees on which tools are suitable for use, and what they can share. Clear guidelines that hold employees accountable for misuse can go a long way to protecting organizations from security mishaps. But policy only works when people understand what it means in their day-to-day work, which is where training comes in.
Applying AI to Real Workflows
There's a tendency for employees to turn to AI for one-off tasks, such as answering a quick question or drafting a short email response. While these queries are helpful, they don't deliver the outstanding productivity and business benefits that employers may be hoping for. The real gains come from rethinking how work gets done, such as turning slow, manual workflows into partially automated ones, especially for routine tasks.
For example, B2B salespeople are typically responsible for finding new leads. Instead of scouring the internet for potential customers, they could ask AI to handle the hunting and initial research. Then, they could verify that each lead is real and accurate, confirm research that follows privacy rules, and review the list for good fits before making contact.
How Should AI Training Differ by Role?
A thorough employee AI training program tackles the basics before introducing more complex topics. It's tailored to each individual's role, with guidance on using AI in their workflows. Here's a look at what a robust training program might include.
A Shared Foundation for Every Employee
Organizations may start with a short introductory AI course for everyone before breaking into tiered concepts. Such a course introduces AI terminology, giving attendees a baseline shared vocabulary for future training. It will also touch on guardrails and expectations that are specific to the organization. This is where you'll explain your AI use policy, goals for implementing AI into workflows, and any specific constraints you want employees to be aware of.
Power Users and Business Functions
Teams will use AI in different ways, and your training should reflect that. Plan on courses designed for specific functions, including marketing, finance, HR, operations, and support. Tie it to their specific tasks so that employees see actual use cases where AI makes a difference.
For example, HR teams may use AI to draft open job postings. Function-specific training will demonstrate effective AI prompting that aligns job posts with each role's responsibilities, goals, and expectations. It will also show HR teams how to identify potential bias that's introduced by AI tools in their output.
Some employees will use AI more than others. Such workers will benefit from in-depth training that covers advanced prompting, workflow chaining, the use of AI agents, and other techniques.
Managers and Leaders
Executives and managers are often charged with setting the expectations for AI use. They're the ones who decide which model is the best fit for the organization and which workflows to transition to AI. Training that taps into those factors can help business leaders make informed decisions, improve AI engagement across teams, and identify metrics that track adoption and results. Leaders also need visibility into who is improving with AI, where gaps remain, and where risk is emerging, not just license counts.
Citizen Developers Building With AI
Non-developers (or vibe coders) are turning to AI to build apps, scripts, and automations. Their efforts can result in process optimization and improved workflows. However, they may be unfamiliar with secure building concepts such as protecting secrets and tokens, using private repositories, access control, testing, and review. Training tailored to citizen developers gives them the building blocks they need to develop programs and automations safely. Security Journey’s AI Access training, for example, covers workforce safely with AI coding assistants such as Claude Code, GitHub Copilot, and Codex, including branches, pull requests, secret checks and audit logs.
Developers Working With AI-Generated Code
Code produced by AI may function, but that doesn't mean it's secure. AI training for developers shows them what to look for when reviewing AI-generated code. It addresses OWASP Top Ten Threats along with risks specific to AI and LLM applications, and provides examples of what secure and insecure code looks like. Training should cover the entire software development life cycle, including how to fix vulnerable code so that it's less likely to be exploited.
Why Should Security Be Part of Every AI Training Program?
Drafting an AI use policy is often the first step to governing AI in the workplace. However, a policy only tells employees what's acceptable (and what's not). It doesn't strengthen their judgment skills or completely prevent bad decisions made in the heat of the moment. That's where corporate AI training that covers security becomes essential to your program.
Why Policies Alone Don't Change Behavior
Let's say you share an acceptable use AI policy with your team. In it, you define the tools they can use on the job. Everyone has a copy, so you assume they know how to distinguish proper AI use from improper use.
But that's not the case. A single document doesn't tell employees how to identify risky prompts or outputs. It doesn't explain which data is safe to share, and what's not. Employees derive this knowledge from security training that covers actual scenarios they may encounter in their own interactions with AI.
What Secure AI Habits Look Like Day to Day
AI skills training for security shows employees how to use AI in a way that won't harm your business, staff, customers, or vendors. It covers safe AI habits, such as:
- Use of approved tools: A list of the AI tools that employees are welcome to use in their tasks.
- Defining sensitive data: Information that employees should never share with AI, such as personal identifiers and private business strategies.
- Verifying outputs: An employee's responsibility to confirm that an AI tool's output is accurate and aligns with relevant business data or in-house expertise.
- Scoping access: Verifying and restricting the information or systems an AI model can read or modify.
- Reviewing before shipping: The step-by-step review process used by teams to validate AI-generated outputs before releasing them to the public.
In-depth training goes beyond policies, giving your team clear guidance that supports reliable AI decision-making.
This is where Security Journey’s roots matter. We come from secure development training, where the goal isn’t just to teach concepts but to change how people build. We bring the same discipline to AI, helping developers and employees decide what to trust, verify, protect and review before AI-generated work creates risk.
How to Build an AI Training Program That Sticks
Turning your AI training program from an ambition to reality is possible with a few steps. Take the following actions to roll out your program this quarter.
1. Start With a Baseline of How People Use AI Today
Your first task is to learn which AI tools your employees already leverage in their daily work. The simplest way to find out is to ask. Send a short survey to all your employees inquiring about their AI use, including what tasks they find it helpful for and tool subscriptions they have. Explain why you're asking, so employees don't worry that you'll take punitive action against them for using AI.
Some employees may be reluctant to share these details, especially if your culture hasn't promoted AI or managers dissuade them from using tools. However, there are several ways to uncover unapproved AI usage.
If your team relies on your work network for online connections, you can track website visits to AI providers. Data loss prevention (DLP) and cloud access security broker (CASB) tools can go further and flag sensitive files shared with AI services. Involve your legal and privacy teams first, since employee monitoring rules vary by region.
Some tools can scan computers for shadow AI use. Companies that have restrictive AI policies may leverage these solutions to identify any unsanctioned AI activities. They can also help business leaders discover unknown AI tools their team takes advantage of.
Once you have a reliable inventory of AI tools your team leans on for daily work, you can decide which ones are appropriate and what employees can use them for.
2. Make Training Hands-On and Tied to Real Work
Traditional learning is lecture-based, followed by a quiz or exam. However, this approach isn't as likely to result in strong retention of training materials. According to long-standing research, students gain a stronger grasp of concepts through hands-on, interactive learning.4
In AI training, this can look like sharing realistic scenarios or projects to work on. For example, the trainer could share a specific workflow goal, such as automating a report, and ask students to put their AI skills to work. This type of project would give employees an opportunity to craft a useful prompt, review AI output for mistakes, and verify repeatability.
Hands-on practice also serves as inspiration for future workflow improvements. Employees may come up with innovative ways to use AI that business leaders haven't yet considered.
3. Build in Ongoing Reinforcement
A one-time AI training course isn't likely to result in sustained benefits. People tend to forget what they learned as time goes by. And as new models are introduced, you may find fresh ways (and tools) you want your team to use in their daily tasks.
Proactive refreshers keep your team engaged after the initial training ends. A monthly, one-hour session signals that AI is something your organization is actively investing in, and that you expect your team to leverage, too. Use this time to highlight new AI workflows and share tips curated by employees who handle the tools.
Some organizations find it helpful to appoint internal AI champions. These individuals are the AI leaders of their department or group. They actively encourage their team to use AI, assist with its governance, and spot processes where it may be useful. Appointing AI champions also gives you a point person who can interact with other business leaders regarding AI policies, use, and training.
4. Measure Capability, Not Completion
It's all too easy to treat AI training as a checkbox in a compliance checklist. But this is a mistake. Organizations that gain the most value from AI view training as the first step to a complete makeover of their business processes. And they measure results with metrics that demonstrate actual change, instead of simply tracking course attendance.
Metrics that show how enterprise AI training delivers tangible benefits include:
- Skill assessments: Testing employees on their AI expertise before and after training and tracking results by role, team and cohort tells you whether they have learned something new and where to focus next
- Quality of AI-assisted work: Training teaches employees how to create detailed prompts and critically review outputs. You should see a jump in quality from AI-assisted tasks when your team applies these skills.
- Policy adherence: Defining which AI tools are acceptable (and how employees can use them) may minimize shadow AI in your organization.
- Fewer risky behaviors: Monitoring your organization's network traffic can tell you whether employees are sharing sensitive information or using AI in unapproved ways. This should decline after training.
- Success ready to scale: Look for teams whose AI practices are working, so you can spread them across the organization.
Turning AI Access Into Real Capability
Right now, we're at a turning point for AI. It's still a relatively new technology, and businesses are learning how to leverage it for long-term advantages. The ones that succeed will do more than buy a few tool subscriptions and hand them over to their employees. They'll actively invest in teaching employees how to prompt, judge output accuracy, and automate workflows safely and they’ll measure whether the capability is actually growing.As you think about how to incorporate AI into your organization, there are a few practical first steps to take. The first is to baseline current AI use in your business. That will tell you what tools people are using and where they have an effect. Next, build a foundational training path for one team through a trusted provider. Security Journey is one option. We offer an AI Advantage program that includes foundational and role-based AI training designed to help organizations realize AI capabilities.
Frequently Asked Questions About AI Training for Employees
How long does AI training for employees take?
A single foundational AI training course is typically short, lasting an hour or two. In-depth, role-based classes may stretch additional learning over several weeks or months. Plan for more than a single session, though: BCG found that regular AI use is sharply higher among employees who receive at least five hours of training.
Should AI training be mandatory for all employees?
It's considered best practice to offer AI training for all employees, particularly if they use AI tools on the job. Training can help your organization meet relevant compliance requirements, avoid security risks, and teach employees proper AI handling.
What's the difference between AI literacy training and security awareness training?
AI literacy training covers how AI tools work. It shows employees how to prompt, review outputs for accuracy, and integrate AI into approved workflows. Security awareness training informs employees about AI cybersecurity risks that may affect your business.
How often should AI training be updated?
Since new models are released frequently, it's smart to provide employees with AI upskilling courses at least once per quarter. Ongoing training cements AI as a part of their job and encourages them to use it safely.
Do developers need different AI training than other employees?
All workers (including developers) need role-based AI training that's specific to their tasks. Developers can benefit from training that explains secure coding and OWASP Top 10 Threats to look out for when using AI-generated code, along with how to review, test, and own that code before it ships.
How do you get employees who are skeptical or nervous about AI to engage with training?
Explain your vision for AI in the workplace, and how you feel it can deliver value to their jobs. Clear examples may help employees understand why it's beneficial and encourage stronger engagement during training.
Sources:
1. Boston Consulting Group. AI at Work: Momentum Builds, but Gaps Remain. Read the original article
2. JFF. Worker Anxiety Over AI Is Growing. Read the original article
3. Mississippi State University. Trust and AI: Key Findings on AI at Work and in Education. Read the original article
4. University at Buffalo. Active Learning Effectiveness. Read the original article
