Why Learning Designers Must Embed AI Into The L&D Process
Artificial Intelligence (AI) is rapidly becoming part of the Learning and Development (L&D) toolkit. Tools promise to automate course creation, summarize subject matter expertise, and accelerate Instructional Design work. But while AI adoption is accelerating, organizational readiness is not keeping pace.
In my work designing leadership training programs, I regularly receive large volumes of materials from Subject Matter Experts (SMEs)—slide decks, recordings, documents, and videos. Ironically, much of this content has rarely been revisited, even by the people who originally created it. The real challenge for learning designers today is not the lack of information. It is how to transform scattered knowledge into meaningful learning experiences. AI appears to offer a solution, but how we adopt it matters.
What Industry Data Tells Us About AI In L&D
Recent industry research confirms what many learning professionals are experiencing first-hand: AI is already widely adopted in learning design. Several trends stand out:
- AI is already embedded in learning strategies. Research suggests that nearly 80% of L&D teams are using AI, often to streamline content creation and reduce repetitive tasks.
- AI is moving from individual experimentation to team workflows. In a survey by Synthesia, only 2% of respondents reported using no general-purpose AI tools, while the majority rely on platforms such as ChatGPT (74%), Microsoft Copilot (54%), and Google Gemini (39%).
- AI will shape how employees learn. A TalentLMS survey found that 88% of HR leaders expect generative AI to transform how employees acquire and interact with knowledge.
However, another trend is equally important. While AI adoption is growing rapidly, confidence in how to use it effectively remains much lower. Many organizations are experimenting with AI tools, but fewer feel fully prepared to integrate them into learning processes in a sustainable way. This creates a growing readiness gap between technological capability and organizational practice.
The Reality Of Modern Learning Design
For Instructional Designers, this gap becomes visible in everyday work. Large amounts of existing content already exist inside organizations: presentations, recordings, documents, and training materials. Much of it is valuable, but it is often fragmented and difficult to navigate. AI can help process this information quickly. But when used without a structured design process, it can also create new challenges. In practice, learning teams tend to follow one of two paths.
Path One: 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:
- Organize
Large collections of slides into course structures. - Identify
Core concepts across existing materials. - Summarize
Long recordings and documents. - Orchestrate
Multiple AI tools to generate learning content.
This approach offers clear advantages. Automation saves time and allows learning teams to process information faster. But it also carries a hidden cost. When the workflow becomes primarily automated, collaboration with Subject Matter Experts can become more transactional. Instead of jointly interpreting knowledge, the process often becomes:
SMEs provide content → designers filter it through AI → learning content is produced
Over time, this can widen the gap between colleagues. The design process becomes more mechanical and less reflective, with less space for conversation, interpretation, and shared understanding.
Learning materials may become more efficient, but they can start losing connection with real learner needs. Many of us have heard feedback like this after a training session: “The learning was well-designed, the slides looked great, the delivery was smooth, but something was missing.” Or we notice that participants leave a session overloaded with information, yet still unsure about what to do differently in their work.
These are often signs that content has become polished but disconnected from practice. When learning is shaped primarily through automation rather than dialogue with SMEs and learners, it can become technically strong but experientially thin. The structure is there, the visuals are there, the information is there, but the sense-making that helps people apply knowledge in real situations is harder to find.
Path Two: Embed AI Into The L&D Learning Design Process
The alternative is not to slow down AI adoption, but to embed AI directly into the architecture of the learning design process. Instead of automating around the process, learning teams can intentionally integrate AI tools into each stage of the design workflow. In this approach, AI does not drive the work on its own. The design framework remains the structure that guides decision-making, collaboration, and accountability.
When AI is embedded this way, it becomes a support system for the team rather than a replacement for the thinking that happens between designers, SMEs, and facilitators. It helps teams process large volumes of information, surface patterns in knowledge, and move faster through early drafting stages, while still keeping human interpretation at the center of the work. For example, within a typical learning design framework, AI can play a different role at each stage:
- Analysis
AI can help summarize SME conversations, transcripts, and documents, highlighting recurring themes, knowledge gaps, and areas that require deeper clarification. - Design
AI can assist in organizing learning objectives, structuring course outlines, and proposing different learning pathways based on the goals of the program. - Development
AI can help draft content, refine language, structure scenarios, and support the creation of supporting materials such as scripts, summaries, or visuals. - Implementation
AI can assist in organizing learning resources, improving accessibility, and helping learners navigate materials more easily. - Evaluation
AI can analyze participant feedback, identify patterns across comments and surveys, and help teams interpret what worked well and what needs improvement.
When used this way, AI does not replace the learning design process—it strengthens it. The framework continues to guide the work, while AI helps teams move through the process more efficiently without losing the collaboration and reflection that meaningful learning design requires.
AI And Human Collaboration Must Grow Together
AI will continue to expand in Learning and Development. Avoiding it is no longer realistic. The tools are already shaping how information is produced, organized, and shared in organizations. However, automation alone will not improve learning. The real opportunity lies in designing processes where AI and human collaboration evolve together. When AI is intentionally integrated into the learning design workflow, it becomes a support system for thinking rather than a shortcut around it.
In Language Machines, Leif Weatherby describes AI as a system that surfaces patterns in collective language. This idea is especially relevant for L&D, where much of our work is built on language—explanations, stories, policies, and shared practices. AI can help reveal patterns across documents, conversations, and feedback that might otherwise remain hidden.
But those patterns only become meaningful when people interpret them. Learning designers and SMEs still shape how knowledge is translated into real learning experiences. In this sense, AI should not replace the learning design process. It should help strengthen it, and this can happen when you embed AI into the L&D process.
