What AI is Teaching us about How Adults Learn

Cosimina Nesci, Senior Learning & Leadership Manager Design and Delivery, Allianz Australia

The rapid adoption of AI in the workplace is not just changing how work gets done. It is also revealing a long-standing mismatch between how organisations design learning and how adults actually learn at work.

Traditional corporate learning has centred on structured courses, programs, and curricula. These still play an important role, particularly for risk, compliance, and regulatory learning, where consistency, traceability, and assurance are critical. However, they are often overused for day-to-day capability building, where flexibility, relevance, and context matter more.

AI exposes this gap by allowing learning to shift into the flow of work, rather than being confined to pre- or post-training moments.

Instead of waiting for the next workshop or eLearning module, employees can now ask a question when needed, receive an answer in context, apply it immediately, and refine as they go. This on-demand, personalised, and task-specific learning works because it mirrors how adults naturally solve problems.

Adults learn best when learning is intertwined with the work itself. AI accelerates capability by supporting thinking and action within the workflow, such as drafting an email, preparing for a difficult conversation, analysing information, or working through a problem under time pressure.

The power lies in immediacy. Responses are instant, relevance is obvious, and motivation comes from resolving a real issue rather than completing a course. Learning embedded in work can outperform stand-alone training because it closes the gap between knowing and doing, a gap traditional learning models have long struggled to bridge.

Most corporate learning still operates as push learning:

“Here’s what we think you need, and here’s when you’ll learn it.”

AI enables true pull learning:

“I need this now, for this task, in this context.”

This shift matters because pull learning respects adult autonomy. It assumes people are capable, not deficient, and trusts them to recognise when support is needed. AI removes friction by delivering that support immediately, without disrupting the flow of work.

For organisations, this means AI adoption should not be driven by long training programs or complex use cases. The most effective entry point is often the simplest and most familiar.

A prompt such as “Rewrite this email more professionally” is powerful because it is low-risk, immediately useful, and easy to apply. It builds confidence in seconds and demonstrates that AI is not about replacing judgement, but enhancing it. These small, practical interactions create momentum and help normalise AI as a day-to-day work tool rather than something abstract or intimidating.

This is where organisational support becomes critical. Adoption accelerates when AI is embedded into existing tools and workflows rather than treated as a separate system to learn. When employees can access AI where work already happens—in documents, collaboration platforms, or knowledge systems—learning becomes seamless and continuous.

Equally important is how organisations frame capability building. Replacing “AI training” with guided practice, real examples, and peer sharing has proven far more effective. Showing how someone used AI to prepare for a meeting, structure their thinking, or explore options is often more powerful than teaching prompts in isolation.

Psychological safety also plays a major role. People are more willing to experiment with AI when expectations are clear, boundaries are understood, and exploration is encouraged. When leaders model everyday AI use openly— using it to think, draft, reflect, or problem-solve—it signals permission more strongly than any policy or rollout plan.

AI should be positioned not just as a productivity tool, but as a partner. Framing its value around better judgement, clearer communication, and stronger decisions aligns more naturally with professional identity than a narrow focus on speed or efficiency.

AI is not replacing learning, but it is reshaping it. There will always be a place for structured learning where risk, compliance, and regulation demand it. However, for most day-today capability building, performance support, and problem-solving, AI can now play a significant role.

The challenge for organisations is no longer how much learning they provide, but how intentionally learning is woven into the way work is done.

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