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AI for L&D: What You Need to Know in 2026


Over the last two years, AI has powerfully shifted Learning & Development. Not only did it arrive with a dramatic revolution, but it also seeped through everyday workflows: first as a helpful writing assistant, then as a faster way to draft storyboards, summarize interviews, generate scenarios, or clean up messy data. Many people and L&D teams in particular started using it almost immediately — not because they had an “AI strategy,” but because it made their work easier.

 

What makes 2026 different is not that AI exists, but that it is becoming part of how learning is designed, delivered, organized, and evaluated inside real organizations. The question for L&D is no longer “Should we use AI?” but rather: “How do we use it well, safely, and in service of behavior change — not just content creation?”

 

This article keeps it simple: what AI actually is for L&D, where it is already being used in the real world, what it can and cannot do, and what this means for our roles going forward.


Table of Contents:

 

 

What AI actually is

 

For L&D purposes, you can think of AI as:

 

  • a pattern-recognition engine that predicts what should come next

  • a content generator that can write, summarize, translate, reformat, and simulate

  • a fast assistant that helps you think, draft, analyze, and iterate

     

Generative AI tools (like ChatGPT, Copilot, or similar systems) don’t “understand learning” — they generate plausible responses based on data patterns. That makes them useful, but also risky if you don’t apply human judgment.

 

 

Where AI is already changing L&D

 

Here are a few concrete, publicly documented examples — not hypotheticals — that show how organizations are already using AI in learning ecosystems:

 

Microsoft – Copilot in Viva Learning

AI helps recommend, summarize, and surface learning content inside the flow of work.

 

Degreed – AI Skills Graph

AI personalizes learning, recommends content in the flow of work, maps skills, and helps organizations understand capability across the workforce.

 

IBM – SkillsBuild (AI tutoring and coaching features)

Uses AI to guide learners through skills and career pathways.

 

Coursera – Coursera Coach

AI acts as a learning assistant: explains concepts, answers questions, and guides study.

 

SAP – Joule (AI copilot integrated into enterprise workflows)

Shows how AI is being embedded directly into enterprise systems, including learning contexts.

 

What these cases have in common is this: AI is not replacing L&D — it is being woven into platforms, tools, and workflows that L&D already relies on.

 

What AI cannot (and should not) do in L&D

 

AI is powerful, but it has clear limits. It cannot:

 

  • diagnose the real root cause of a performance problem

  • read organizational politics or power dynamics

  • build trust with stakeholders

  • facilitate difficult conversations

  • coach with emotional intelligence

  • redesign culture

  • decide what learning a business truly needs

 

In practice, this means AI is strongest in execution, not in strategy or judgment.

 

How AI fits across the learning cycle

 

You can think of AI as a companion across the end-to-end L&D process:

 

Analysis

  • summarizing documents, interview notes, and survey data

  • drafting needs assessment questions

 

Design

  • generating objectives, activities, or scenarios

  • brainstorming formats and learning flows

 

Development

  • creating first drafts of slides, scripts, microlearning, or quizzes

 

Delivery

  • role-play simulations or virtual practice partners

 

Evaluation

  • analyzing feedback, themes, and patterns in data

 

The important point: AI supports the process, but humans own the decisions.

 

Risks, ethics, and common mistakes

 

Some traps to avoid:

 

  • pasting confidential company data into public AI tools

  • assuming AI outputs are always correct

  • using AI to mass-produce content without performance context

  • replacing facilitation with automation

  • optimizing for speed instead of impact

 

A simple rule of thumb: if the data is sensitive, don’t put it into open AI tools.

 

A simple AI starter workflow for L&D beginners

 

If you want to experiment in a safe, useful way:

  1. Ask AI to summarize a long document or policy

  2. Use it to draft learning objectives

  3. Generate a few scenarios or role plays

  4. Edit everything with your professional judgment

 

This won’t make you an AI expert — but it will make you AI-literate.

 

What this likely means for L&D roles

 

I wish I had a crystal ball that could tell the future. Alas, I am a simple L&D professional who has been in the industry for 15+ years now (as of writing this article, anyway). Which means that I don’t know how AI will impact L&D in the long-term. What I can do, however, is speculate based on years of corporate learning in Europe, North America, and Asia, and on how organizations actually behave. So take the below with a grain of salt… or two.

 

With that in mind, I expect:

 

  • less time spent on manual content production

  • more time spent on performance consulting (YAY!), facilitation, and impact

  • greater emphasis on data and learning analytics

  • stronger focus on behavior change, not just courses

  • deeper collaboration with business leaders and managers

 

In short: AI changes how we work, not why we exist.

 

Who should care about AI in L&D

The short answer – everyone! But I’m guessing you’re here for the longer one. Here it is, then:

 

  • beginners — to build safe habits early

  • specialists — to work faster and smarter

  • managers — to think about governance and strategy

  • CLOs — to shape ecosystems, ethics, and ROI

 

Final thought

 

AI is not the story of L&D in 2026 — people, performance, and behavior are. Or should be! AI should simply be a powerful new tool in service of that mission. If we keep our eyes on real workplace change, AI becomes an ally rather than a distraction.

 

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