When ChatGPT launched in November 2022, most people didn’t see it coming, and few expected it to be so impactful. 

Flash-forward to 2025, and artificial intelligence has completely revolutionized how we live, work, and learn. Accessing information and generating content for almost any purpose has never been easier, thanks to generative AI and large language models.

Even now, AI technology is evolving at a breakneck pace. 

Riding this wave is the key to success both in corporate learning and development and in the broader business landscape — and people know it. This year, the market for AI in training and development is expected to hit $6 billion as more and more businesses adopt it. This is also the year that adaptive learning will likely become non-negotiable, driven by a combination of employee expectations and rapid market growth. 

There’s a problem, however. Although around two-thirds of companies are looking for ways to use AI, most lack the training and skills to implement it effectively. 

This article covers tips, best practices, and use cases for using generative AI (genAI) in corporate training — including overcoming implementation challenges.

1. Understand the Limitations of AI

To understand what GenAI can do for corporate training, you need to understand what it can’t do. Although disruptive, powerful, and incredibly promising, it isn’t a silver bullet. Using the technology without knowing its limitations can be a recipe for disaster.

So what can’t AI do? 

AI Can’t Replace Humans

AI tools can do everything from generating hyper-personalized learning paths and creating dynamic training content to assessing learner performance. But they also lack intuition and the capacity for abstract thought. They cannot practice empathy or emotional intelligence, nor can they think critically or navigate complex decisions. 

Instead of replacing people, GenAI can augment and empower them. Machine intelligence will always be most effective when paired with human intelligence. Harvard Professor Karim Lakhani put it best: 

“AI is not going to replace humans, but humans with AI are going to replace humans without AI.” 

GenAI’s Outputs Are Only As Good As Your Prompts

One common misconception about GenAI is that you can talk to it the same way you would speak to a colleague. This is true only in some scenarios, such as with virtual assistants or support chatbots. Otherwise, when using GenAI, effective prompt engineering is crucial. 

The truth is that no matter how human GenAI might seem, it doesn’t possess the same knowledge, skills, or understanding of context and nuance as a human.

Imagine that you’ve asked a colleague to brainstorm some topics for your company’s customer education program. They will come up with ideas based on their existing knowledge of your business and industry. If you address the same simple request to GenAI, you’ll receive only a generic or superficial response. 

Here’s what an effective prompt should include: 

• A clear objective, such as the specific customer education topics 
• Unambiguous, direct language 
• Contextual details such as use case and industry
• Your desired outcome 

The more you give, the more you get. If your AI is connected to the internet, provide it with a link to your website so it can get a feel for the subject matter and tone of voice. You can also upload any offline documents, such as older training courses that worked well. Just bear in mind that your AI is unlikely to be able to read text within images.

You Can’t Adopt AI Overnight

GenAI requires an operational and a cultural shift if implementation is going to deliver on your goals. That’s why change management can be the difference between success and failure. 

Define what you want to achieve at the outset — an objective that’s both tangible and measurable, such as greater proficiency in compliance and reporting. From here, develop a comprehensive roadmap that includes milestones and target dates. Make time for employee education as well; many of your people will likely need to acquire new knowledge and skills before they can leverage AI to its full potential. 

2. Know What to Look for in an AI Tool

There are plenty of AI tools for corporate training and development on the market, available both as standalone solutions and bundled into other platforms such as learning management systems. When evaluating vendors and solutions, look for a solution with the following characteristics:

• Developed specifically for your industry and use case — or, failing that, designed for corporate learning and development
• Capacity to integrate with your existing technology stack 
• An intuitive, user-friendly interface with a simple learning curve 
• Support for the right input and output formats – this will vary based on your use case
• Effective handling of sensitive information and adherence to data privacy legislation
• Clear guidelines on how the AI model uses data, with the option to prohibit it from using yours 
• Extensive documentation and vendor support 
• The ability to customize details such as training style, assessment format, content modality, and branding 

3. Address Potential Legal and Ethical Concerns

Copyright and data privacy are two of the most prevalent issues in the AI sector, and will likely remain so for years to come. ChatGPT developer OpenAI, for instance, has been accused on multiple occasions of scraping copyrighted or private data from the web to train its AI model. It’s faced multiple lawsuits related to both copyright infringement and data privacy violations. 

In the broader AI space, as of 2025 at least 38 lawsuits related to AI have been filed in the United States alone.

But what does any of this have to do with training? 

Simply put, if you use a GenAI tool that was trained on copyrighted or sensitive data, there’s a chance it may partially replicate unlicensed material. You may consequently end up facing legal consequences should you distribute that content. If you use an AI tool that leverages your data for machine learning, your content could also end up being infringed upon. 

It’s important that, whenever possible, you work with vendors who practice ethical AI development and are fully transparent about how they collect and process data. 

Biased data is another potential concern. If you use a solution that allows you to train your own AI models, it’s important that you perform regular audits to identify and address potential issues with or gaps in training data, and also properly sanitize your datasets. 

4. Embrace Adaptive Learning

Adaptive learning empowers your instructors to leave one-size-fits-all training in the past. Leveraging artificial intelligence, it allows you to deliver dynamic, personalized content at scale. In addition to improving knowledge and comprehension, adaptive learning significantly reduces instructor workloads while also enabling a more iterative approach to learning and development. 

Common adaptive learning techniques include: 

• Real-time hints and feedback based on learner actions 
• Learning paths that evolve as the learner progresses through their training
• Dynamic assessment difficulty 
• Personalized content delivery

Adaptive learning solutions typically consist of two main components: domain models allow the system to ingest and comprehend the subject matter, while learner models are designed to track and evolve with individual users. An adaptive engine ingests and delivers information from the two models while also refining them over time. 

5. Offload the Administrative Burden

Deeper personalization isn’t the only advantage of using AI for training. AI tools are also capable of automating much of the administrative work involved in maintaining a learning and development program, including data entry, assessments, and scheduling. You can also use AI for better analytics, identifying patterns that would be invisible to humans, and for generating easily digestible reports. 

6. Verification and Assessment

GenAI’s ability to support iterative learning through instant feedback and deliver actionable learner analytics are only two examples of the many ways machine intelligence can help you track and measure learner progress. 

A computer vision algorithm can be used within a virtual IT lab to: 

• Verify that learners are correctly completing training tasks by comparing their outputs with images of the desired results
• Validate the syntax and formatting of a trainee’s code in a course related to scripting or programming using optical character recognition. 
• Analyze a learner’s virtual environment or webcam feed to measure engagement. 

All of these scenarios would traditionally require manual confirmation by an instructor. With AI, everything is automated. Because they’re no longer spending so much time on performance assessments, the instructor can focus more of their attention on explaining concepts, providing guidance, and answering questions. 

In the case of self-paced training, the learner can access immediate feedback on what they’re doing right and wrong, keeping them on the right track and allowing them to enjoy a smooth and seamless learning experience.

 7. Eliminate Skills Gaps

AI is ideally suited to handling, analyzing and distilling massive amounts of data. For large organizations, employee activity reports, access patterns, and performance records represent an enormous and ever-growing mountain of largely unstructured data just waiting to be mined by AI.

By ingesting both real-time and historic employee data, an AI model can help you identify potential skills and knowledge gaps, allowing you to develop targeted learning and development programs. You can also use AI-driven analytics to predict potential skills gaps that might arise in the future, examining both internal business data and external market data to mitigate talent shortages. 

Streamline Corporate Training Creation with GenAI 

Generative AI is clearly one of the most disruptive technologies in history, and it’s fundamentally changing how businesses operate. Leveraging the technology for corporate learning and development has the potential to drive better outcomes for both learners and instructors. Through techniques such as adaptive learning, organizations can unlock new proficiencies, and eliminate skills gaps that have previously gone unaddressed.

And that’s only the start. As AI continues to evolve, its impact on the business landscape will grow even more pronounced, as will the need to adopt it. 

Learn more about the kind of training your employees will require in order to prepare for the future in Teaching Smarter: How to Provide Winning Technical Training for Generative AI. We also recommend reading The Power of AI in Learning and Development for gaining a clearer idea of its impact. 

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