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What Businesses Need to Know.

 

For learning and development (L&D) leaders, AI offers both disruption and opportunity. It’s reshaping how organizations design, deliver, and personalize learning programs.

But AI isn’t just a tool; it’s a catalyst. It’s shifting the role of learning from a static curriculum to a dynamic, context-aware ecosystem. That change requires thoughtful leadership and a strategic lens.

Here’s a closer look at the trends shaping AI-enabled learning, the pros and cons, and practical guidance for those looking to deliver the very best to learners.

TREND 1: The Rise of Agentic AI in Learning

Imagine an AI agent that watches a learner do an activity in a work simulation, adjusts the activity’s difficulty based on what the learner does, and offers custom feedback pulled from the organization’s best practices.

That’s not science fiction. Organizations are already using agentic AI: autonomous agents powered by large language models (LLMs, like Chat GPT) that can perform complex tasks with minimal human direction.

These AI agents can do things like:

  • Serve as personalized virtual coaches or tutors.
  • Simulate decision-making scenarios in real time.
  • Guide people through learning paths based on their performance and preferences.

Cool. But why use them? AI learning agents can reduce the burden on human facilitators, increase scalability of learning programs, and enable more just-in-time, responsive learning.

But they also require oversight — both ethical and instructional — to make sure the learner’s experience aligns with learning objectives and organizational values.

TREND 2: Generative AI for Content and Graphics Creation

Instead of spending weeks developing a deck, what if your instructional designers could prompt AI to create a first draft, then refine it based on audience needs?

Generative AI — tools like ChatGPT, DALL·E, and others — has unlocked new ways to design and develop learning programs.

For example, L&D teams are using AI to:

  • Draft learning objectives, course outlines, and microlearning content.
  • Generate infographics, diagrams, and training visuals.
  • Translate and localize learning materials faster and more affordably.

This saves time and money. It’s especially good for fast-moving organizations that have to train employees on tools, policies, or compliance issues that are constantly changing.

Teams should use Gen AI carefully, though. If you lean on it too heavily, you get generic, impersonal learning. You need human instructional designers to assess learning needs and ensure relevance, nuance, and tone. AI can create content and , but it can’t (yet) think like your learners or reflect your culture.

Why are organizations leaning into learning AI?

  1. Speed and Scale: AI helps learning teams move faster and serve more learners without increasing L&D headcount.
  2. Personalization: AI enables content and experiences to adapt in real time to learner needs, improving engagement and retention.
  3. Data-Driven Insights: AI can turn learning analytics into actionable conclusions. For example, it can identify where learners struggle most and why.
  4. Cost Efficiency: Automating lower-value tasks (like building quizzes or frees up humans to focus on high-value learning strategy, planning, and design.

What should we watch out for?

  1. Technical Quality: AI-generated content can be inaccurate or misleading. It needs a human reviewer. This is a big risk for programs like compliance or technical training.
  2. Image Quality: Some tools also have a long way to go before they can generate custom images that match your needs. You might get two-tailed dogs or graphics that come close, but aren’t what you want, even after a lot of direction.
  3. Equity and Bias: AI tools can reflect and amplify bias present in training data, potentially reinforcing stereotypes or excluding certain learner perspectives.
  4. Over-automation: Delegating too much to AI can diminish human connection, which is critical in programs like leadership development and behavior change.
  5. Data Privacy: As AI tools collect and process learner data, organizations must stay vigilant about data security and compliance.

Here’s our advice.

AI has incredible potential to enrich learning, but it also raises critical questions. Here’s how to approach it strategically:

Start with purpose.

Avoid the trap of adopting AI for AI’s sake. Ask: What learner experience are we trying to improve? What business outcome are we driving? Then explore how AI can accelerate that result.

Keep humans in the loop.

AI should augment, not replace, the expertise of learning professionals. Use AI for generating ideas, content, and information, but leave strategy, design, reviews, and facilitation to the humans.

Do it ethically.

Build a governance model for AI in learning. This should include guidelines on transparency, accuracy, bias mitigation, and data use. Involve a variety of voices to pressure-test your standards.

Upskill your teams.

AI is here to stay, and L&D professionals need new skills like prompt engineering, AI tool evaluation, and content curation. Invest in upskilling your team so they can get the most of their AI tools.

Focus on behavior change.

AI can support learning, but it doesn’t drive behavior change on its own. Organizations still need thoughtful change management solutions to translate knowledge and skill into sustainable performance.

AI is not replacing strong learning leadership from humans. As tools evolve, the real differentiator will be your team’s ability to connect new technologies with timeless principles: clarity of purpose, learner empathy, and behavior-based design.

AI can help you write the script faster, but you still need to direct the play.