AI is everywhere in learning right now. New tools are launching weekly, demos are impressive, production speeds are faster than ever, and new vendors are creating mass confusion as we are
inundated with unsolicited emails about the latest and greatest tools. For many Learning & Development (L&D) teams, the core question has not changed: how does any of this actually improve learning, performance, and business results?
That question is what led us to develop our AI in Learning Framework™. The framework is a practical way to think about how AI supports learning end-to-end, not as a set of disconnected tools, but as a system that improves capability, decision-making, and performance.
What we are seeing today is a pattern that should feel familiar, as many L&D professionals lived through CBT, WBT, Cloud-based learning, and the COVID-induced virtual learning phenomena. As before, organizations are adopting AI in pockets. Content generation in one area, a chatbot in another, a dashboard somewhere else. Each initiative shows value on its own, but together they rarely add up to a cohesive whole. The result is more output and faster production, but not necessarily better decision-making or stronger performance.
This is where it becomes important to distinguish between efficiency and productivity.
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