Why learning designers need to incorporate AI into their L&D processes
Artificial intelligence (AI) is rapidly becoming part of the learning and development (L&D) toolkit. The tool promises to automate course creation, summarize subject matter expertise, and accelerate instructional design work. But while AI adoption is accelerating, organizations’ readiness has not kept pace.
In my job designing leadership training programs, I regularly receive large amounts of materials from subject matter experts (SMEs), including slide decks, recordings, documents, and videos. Ironically, much of this content is rarely revisited, even by those who originally created it. The real challenge for learning designers today is not a lack of information. It’s a way to turn scattered knowledge into meaningful learning experiences. AI appears to offer a solution, but how we employ it matters.
What industry data says about AI in L&D
Recent industry research confirms what many learning professionals are experiencing first-hand. In other words, AI is already widely adopted in learning design. Several trends stand out.
AI is already integrated into learning strategies. Research shows that nearly 80% of L&D teams use AI, often to streamline content creation and reduce repetitive tasks. AI is moving from individual experimentation to team workflows. In a study by Synthesia, only 2% of respondents reported not using general-purpose AI tools, with the majority relying on platforms such as ChatGPT (74%), Microsoft Copilot (54%), and Google Gemini (39%). AI will shape the way employees learn. TalentLMS research found that 88% of HR leaders expect generative AI to transform the way employees acquire and utilize knowledge.
However, another trend is equally important. Although the adoption of AI is progressing rapidly, confidence in how to use it effectively remains much lower. Many organizations are experimenting with AI tools, but few feel fully prepared to integrate them into their learning processes in a sustainable way. This increases the readiness gap between technical capabilities and organizational practices.
The reality of modern learning design
For instructional designers, this gap is visible in their daily work. A large amount of existing content already exists within your organization, including presentations, recordings, documentation, and training materials. Much of it is valuable, but often fragmented and difficult to navigate. AI can help process this information quickly. However, their use without a structured design process can also create new challenges. In practice, learning teams tend to follow one of two paths.
Path 1: Automate the chaos
The first approach is to automate as much as possible. Generative AI tools can quickly structure scattered materials and generate learning content. Designers can create AI agents that:
sort out
A large collection of slides included in the course structure. identify
Core concepts across existing materials. summarize
Extensive recording and documentation. orchestration
Multiple AI tools to generate learning content.
This approach has clear advantages. Automation saves time and allows learning teams to process information faster. But it also comes with hidden costs. When workflows are largely automated, collaboration with subject matter experts can become more transactional. Instead of jointly interpreting knowledge, the process often looks like this:
Small and medium-sized businesses provide content → Designers filter using AI → Create learning content
Over time, the rift between colleagues can widen. The design process becomes more mechanical, there is less introspection, and there is less space for conversation, interpretation, and common understanding.
Learning materials may become more efficient, but they may start to lose connection with the actual needs of learners. Many of us have heard feedback like this after a training session: “The learning content was well designed, the slides were great, and the class was smooth, but something was missing.” Or participants left the session loaded with information but found they were still unsure of what to change in their work.
These are often signs that the content is sophisticated but disconnected from practice. If learning is shaped primarily by automation rather than interaction with small businesses and learners, it can be technically powerful but empirically diluted. Structure, visuals, and information exist, but sensemaking that helps people apply knowledge in real-life situations is even harder to find.
Path 2: Incorporate AI into the L&D learning design process
The alternative is to incorporate AI directly into the architecture of the learning design process, rather than delaying AI adoption. Instead of automating processes, learning teams can intentionally integrate AI tools into each stage of the design workflow. In this approach, the AI does not drive the work itself. The design framework remains a structure that guides decision-making, collaboration, and accountability.
When you incorporate AI in this way, it becomes a support system for your team, rather than replacing the thinking that happens between designers, small businesses, and facilitators. This allows teams to process large amounts of information, surface knowledge patterns, and move through early drafting stages more quickly while keeping human interpretation at the heart of the work. For example, within a common learning design framework, AI can play different roles at each stage.
analysis
AI can help summarize small business conversations, transcripts, and documents, highlighting recurring themes, knowledge gaps, and areas that require deeper explanation. design
AI helps organize learning objectives, build course outlines, and suggest different learning paths based on program goals. development
AI can help draft content, refine language, structure scenarios, and support the creation of supporting materials such as scripts, summaries, and visuals. implementation
AI helps organize learning resources, improves accessibility, and helps learners navigate the material more easily. evaluation
AI analyzes participant feedback and identifies patterns across comments and surveys, allowing teams to interpret what works well and what needs improvement.
When used in this way, AI enhances rather than replaces the learning design process. Frameworks continue to guide the work, and AI helps teams move through the process more efficiently without losing the collaboration and reflection required for meaningful learning design.
AI and human collaboration need to grow together
AI continues to expand in learning and development. Avoiding it is no longer realistic. These tools are already shaping the way information is generated, organized, and shared within organizations. However, automation alone does not improve learning. The real opportunity lies in designing processes where AI and human collaboration evolve together. When AI is intentionally integrated into learning design workflows, it becomes a system that supports thinking, rather than a shortcut to thinking.
In Language Machines, Leif Weatherby describes AI as a system that surfaces patterns in collective language. This idea is especially true in L&D, where much of our work is built around language: descriptions, stories, policies, and shared practices. AI can help uncover patterns across documents, conversations, and feedback that might otherwise remain hidden.
But those patterns only have meaning when people interpret them. Learning designers and small businesses are still shaping how knowledge is translated into real learning experiences. In this sense, AI should not replace the learning design process. It should help enhance it, and incorporating AI into the L&D process can make this happen.
