How generative AI is helping L&D teams create training content at lightning speed—and what you need to know to get started
Picture this: You're the head of learning and development at a fast-growing tech company. Your team is tasked with rapidly developing and deploying training to thousands of employees globally on topics ranging from compliance to cutting-edge technical skills. The old ways of creating courses—spending months painstakingly developing content from scratch—simply can't keep up with the pace of business today. You need a new approach that allows you to develop high-quality learning content at scale.
Enter generative AI.
Artificial intelligence is fundamentally changing the game when it comes to corporate training and e-learning. By leveraging the power of large language models and other AI technologies, L&D teams can now automatically generate custom learning content on virtually any topic in a matter of minutes or hours rather than weeks or months. This game-changing capability is poised to disrupt the $360 billion corporate training industry.
So what exactly is generative AI and how can it be used for training content creation? In a nutshell, generative AI refers to artificial intelligence systems that can generate new content—text, images, audio, video, code, etc.—rather than just analyzing existing data. The most prominent example is OpenAI's GPT-3, a powerful language model that can generate human-like text when given a prompt.
For L&D, some exciting use cases of generative AI include:
The potential is vast. But as with any new technology, generative AI for learning comes with both opportunities and challenges to navigate. Let's dive in and explore how to effectively harness this transformative technology to take your L&D programs to new heights.
So you're bought into the concept of using AI to generate training materials. Now what? How do you actually get started?
The first step is to identify a solid use case. Not every training topic is well-suited for AI-generation (more on that later). Good initial candidates include:
Once you've landed on a strong pilot use case, the next step is to choose your AI content generation tool. A growing number of platforms now offer AI-powered authoring for L&D, including:
These tools provide user-friendly interfaces for "training" the AI on your source content and desired outputs and then generating learning materials. Some, like WildFireAI, even have pre-trained models on common corporate training topics for plug-and-play content creation.
When evaluating tools, consider factors like:
With the right use case and tool in hand, you're ready to dive into AI-generated training content creation! The general process looks like this:
The power of this approach lies in its speed. What used to take weeks or months can now be done in a matter of hours or days. You can rapidly create and deploy content, gather feedback, and continuously improve—the hallmark of agile L&D.
While AI content generation is a powerful tool, it's not a magic wand. To get the best results, keep these tips and best practices in mind:
The AI is only as good as the source material it's trained on. Be selective about the inputs you use—outdated, inaccurate or irrelevant content will yield poor outputs.
AI can generate a strong first draft, but it still needs a human touch. Review all content with a critical eye and don't be afraid to make changes. You'll likely need to revise things like tone, company-specific examples, formatting, etc.
AI works best for fact-based, procedural knowledge where clear right and wrong answers exist. It's less well-suited for nuanced topics that involve judgment, opinion, or interpretation. Don't try to force-fit AI where it doesn't make sense.
Maintain review checkpoints so learners receive content that is accurate, engaging, and trustworthy.
Generative AI offers enormous promise, but organizations must address its limitations:
• Accuracy and Bias: AI models can produce errors or reinforce biases if left unchecked
• Data Security: Sensitive employee or corporate data must be protected during AI training and content creation
• Change Management: Teams may need training and reassurance to trust AI-assisted workflows
• Compliance: Regulated industries must validate that AI-generated content meets legal and accreditation standards
Balancing these risks with governance frameworks and clear policies ensures responsible adoption.
The business case for AI in L&D is compelling:
• Speed to Market: Courses that once took months can now be produced in days
• Cost Efficiency: Reduce reliance on extensive external development resources
• Scalability: Expand training across geographies and business units with minimal incremental effort
• Personalization: Adaptive content improves learner engagement and retention
• Agility: Quickly update training to keep pace with business, regulatory, or market shifts
Forward-thinking organizations are already using these advantages to make learning a competitive differentiator.
To fully realize the benefits, organizations should:
• Establish internal guidelines for AI use in learning
• Train L&D teams on prompt engineering and AI editing techniques
• Monitor outcomes to ensure learning objectives are achieved
• Pilot small before scaling broadly
• Collaborate with IT and compliance teams to manage risks
Generative AI is not the future of corporate learning—it is already reshaping how organizations create, deliver, and sustain training programs. Companies that embrace it responsibly will build more agile, scalable, and engaging learning environments. As for others they will be left behind wondering where they missed.
