AI in learning and development (L&D) isn’t new. For years, we have seen organizations embrace automation tools, chatbots, and recommendation engines under the umbrella of “AI in learning.” And yes, those technologies brought efficiency and scale.
But here’s the truth: not all AI is created equal.
There’s a new wave emerging: one that is not just technological, but deeply pedagogical. It is ushering in a shift from content delivery to active, adaptive coaching and from automation to autonomy.
This is the world of Agentic AI and the momentum is real: it is projected to power 33% of enterprise software applications by 2028, up from just 1% in 2024, as per a Gartner study.
In this blog, I will try to unpack the difference between Agentic AI and traditional AI, and why this distinction matters more than ever if we want learning to truly translate into performance.
Agentic AI: A New Era of AI in Learning
Over the past decade, AI has quietly reshaped many backend processes in L&D by automating course curation, adding intelligent search and nudges, refining learning analytics and so much more.
But most of this has been traditional AI in learning, that is, structured, rules-based systems that operate within narrow boundaries. These systems can recommend, retrieve, and respond. What they can’t do is coach, adapt, or evolve with the learner.
That’s where Agentic AI coaching changes the game.
Agentic AI is built around AI agents which are autonomous, interactive systems that simulate real-world scenarios, make decisions, and evolve through learner interaction. They don’t just give the right answer; they ask the right questions.
In a world where performance matters more than completion rates, where behavior change is the holy grail, this new form of learning with AI agents is building impact we have long aspired to but rarely achieved.
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