There is a growing belief that learning is struggling because of AI — that training teams are under pressure in the era of AI in workplace learning, as automation moves too fast, budgets shrink, and anyone can now create content with a prompt.
That story is convenient — and incomplete.
AI did not create the current problems in workplace learning. It exposed them.
Long before generative tools entered the picture, many organizations were already producing large volumes of training that looked good on paper but had little impact on performance. AI simply accelerated the pace at which that reality became impossible to ignore.
Training is getting faster, cheaper, and easier to produce. At the same time:
- Managers are frustrated by inconsistent performance
- Learners are exhausted
- Business leaders are asking harder questions about value
These trends are connected, not coincidental
The Quiet Failure of “Reasonable” Training
What makes this moment uncomfortable is that much of today’s training is not obviously bad.
Organizations are running in-person sessions, spacing learning out over months, using discussion, activities, and facilitation instead of lectures. In many cases, significant time and money is invested.
And yet, learners often experience training as a burden rather than a benefit:
- People attend because they have to
- They fall behind on real work
- They leave sessions knowing more, but doing nothing differently
Over time, even well-designed programs begin to feel like noise.
This is not a motivation problem, nor a generational issue. It happens when learning is designed around exposure rather than transfer, when attendance becomes the proxy for effectiveness, and when program success is measured by completion instead of capability.
From the learner’s perspective, the math is simple:
If training takes time away from work and does not make work easier afterward, it will be experienced as fatigue — no matter how polished it looks.
Why AI Changed the Conversation So Fast
AI in learning forced a reckoning because it made explicit what had been implicit for years: much of what we call training is content.
If training is content, then:
- Speed matters more than design
- Cost matters more than judgment
- Anyone with access to tools can produce it
Learning teams now compete with subject matter experts, administrators, and software.
That is why many leaders say things like:
- “Just use AI and make it faster.”
- “We don’t need learning for this anymore.”
They are responding rationally to a model of training that was already fragile.
The problem is not that AI can generate material. The problem is that generated material does not equal skill.
Teaching is not the same thing as learning to perform — and it never has been.
Understanding Is Not Performance
Skills develop through practice, feedback, and repetition in conditions that resemble real work. This is true whether the skill is technical, interpersonal, or cognitive.
Without this bridge, knowledge stays theoretical and performance stays the same.
This is why learners can genuinely say they “learned something” while managers see no difference on the job. Both are telling the truth.
Why Learners Are So Tired
Learners are not tired of learning. They are tired of training that asks for time without delivering payoff.
They are frustrated by:
- Sessions disconnected from real decisions
- Programs that accumulate without replacing anything
- Initiatives that feel additive rather than enabling
Even good intentions create fatigue when learning is layered on top of work instead of designed to improve it.
This is the context in which AI entered — not as a destroyer of learning, but as a mirror.
What High-Performing Organizations Are Doing Differently
High-performing organizations are not chasing more content or bigger platforms. They are rethinking learning entirely:
- Design for specific skills rather than broad topics
- Focus on moments that matter, not comprehensive coverage
- Replace long events with short, realistic opportunities to practice decisions and conversations people actually face
- Treat learning as a performance lever, not an educational exercise
They ask the tough questions:
- Can people do this now?
- Are mistakes happening less often?
- Is confidence higher where it matters?
These questions are uncomfortable, but they are the only ones that matter.
The Real Risk for Learning Teams
The risk is not that AI will replace learning professionals. The risk is that learning teams continue to define their value as content production.
AI will always win that race.
But AI cannot:
- Diagnose messy skill gaps
- Design meaningful practice
- Understand how performance actually breaks down in context
Those capabilities require judgment, experience, and proximity to the business.
The future of learning does not belong to faster training. It belongs to learning that measurably changes how people perform.
The shift is already underway. The only question is who is willing to design for it.
That’s why I’m passionate about Beyond Role Plays—it’s the intersection of:
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Effective learning that stakeholders understand
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Learning that learners want to take
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Learning that leads to measurable skill change
If this is the path learning is taking, let’s help to be part of the solution.
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