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.
Efficiency is about doing things faster. AI is very good at that. It can reduce the time it takes to create content, summarize information, or generate assets. In many cases, it can take work that used to require days or weeks and compress it into minutes.
Productivity is different. Productivity is about doing the right things better. It is about improving the quality of decisions, the clarity of thinking, and the ability to act effectively in real situations that can produce real, measurable results.
Right now, much of the conversation about AI in learning focuses on efficiency. How quickly can we build content? How much can we automate? How much time can we save? How do we build images like the one above quickly?
Those are valid questions. But they are not the ones that ultimately matter.
The more important question is whether AI is helping people produce at a higher level. Without that, efficiency simply leads to more activity. It does not lead to better business or personal outcomes.
Introducing the Framework
The AI in Learning Framework™ organizes AI across four connected layers:
- AI-Enabled Content Creation and Design
- AI-Enabled Learning Delivery and Experience
- AI-Enabled Decision Support for Skill Application
- AI-Enabled Measurement and Analytics
These layers are not independent initiatives. They represent a progression from information to clarity, from clarity to judgment, and ultimately from judgment to results.
Most organizations are making progress in the first layer. Far fewer are intentionally designing across all four. Here are descriptions of the layers and potential actions.
1. Content Creation and Design
The first layer is where AI is most visible today. Generative AI has significantly simplified producing learning materials. Teams can draft content, build scenarios, synthesize SME input, and create supporting assets like video and voice at a fraction of the time.
This has clear benefits in speed and efficiency. It allows learning teams to spend less time producing content and more time thinking about how learning should actually work.
But it is important to be precise about the value. This layer improves the speed at which we create information. It does not, on its own, build capability.
2. Learning Delivery and Experience
The second layer is where learning shifts from static content to dynamic experiential learning. AI begins to shape how learners engage through role-plays, coaching, and embedded feedback.
This is where more advanced forms of AI become meaningful. Systems do not just respond. They engage with intent. In our simulations, for example, we use an AI Board of Directors that reacts to learner decisions. It challenges assumptions, highlights trade-offs, and mirrors the type of questioning leaders face in real business environments.
At this point, learning becomes less about completion and more about application. The focus shifts to whether someone can think, decide, and explain under pressure.
3. Decision Support for Skill Application
This layer is already emerging in practice, often without formal design. Learners are using AI in real time to interpret data, test ideas, and prepare for real situations.
In a recent simulation, one team fed their results into AI to better understand performance and determine their next move. That was not a workaround. It was a reflection of how learning is evolving.
AI becomes a thinking partner in the moment. At the same time, it reinforces an important boundary. AI can support judgment, but it cannot replace it.
4. Measurement and Analytics
The final layer is where AI becomes truly strategic. Instead of focusing on completion rates or satisfaction scores, organizations can begin to understand how decisions are being made, how skills are developing, and where business performance and results are improving.
This includes:
- Identifying patterns in decision-making
- Tracking capability growth over time
- Predicting readiness for expanded roles
- Targeting coaching where it will have the greatest impact
At this stage, learning is no longer just a program. It becomes a capability system directly tied to business outcomes.
Designing with Intent: The Risk Conversation
Any meaningful conversation about AI needs to include risk, not as a disclaimer, but as part of the design.
AI does not fix weak systems. It amplifies them. Organizations need to think carefully about capability gaps, data privacy, governance, accuracy, and inherent bias that may be built into AI. These are not reasons to avoid AI, but they are reasons to approach it with discipline and clarity.
Looking Ahead
AI will continue to change how learning is built and delivered, but not by replacing talent development. If anything, it raises expectations.
As more of the production work becomes automated, the differentiator shifts to better design, stronger alignment to the business, and more meaningful learning experiences. It also creates the opportunity to support people closer to the moment of application, rather than relying on static programs that sit outside the flow of work.
The organizations that benefit most will not be the ones that adopt the most tools. They will be the ones who use AI to strengthen how people think, make better and faster decisions, see things that others don’t see, and perform in real situations.
AI does not replace talent development. It gives us the opportunity to do it better.
