What Does a Workflow Look Like When AI-Supported Learning Connects Back to Facilitated Learning or Manager Coaching?

In the mid-2010s, asynchronous learning became very popular because it was so easy to scale. You could build an eLearning course, put it on an LMS, and reach a large audience without needing to schedule live sessions or rely on facilitators.

But coaching and mentoring can also be scalable. They just require a different kind of instructional design. 

Every business knows that creating eLearning is expensive and time-consuming, which is why AI is so exciting. It can help learning teams move faster and reduce some of the heavy lifting involved in building training. But many organizations are still less willing to invest in coaching, mentorship, and the infrastructure that supports human learning, including facilitators and manager development. It can be harder to sell up the chain because it does not look as immediately scalable as eLearning.

But there is a massive opportunity here. AI-supported learning can help people build knowledge asynchronously, but then that learning needs to connect back to a real human conversation. It might connect to a facilitator-led session, a manager check-in, or a coaching conversation where someone helps the learner apply what they have learned in context.

That is what the workflow can look like: AI supports asynchronous learning, but the manager or facilitator helps turn that learning into reflection, practice, feedback, and action. Everyone can do better with a little more intentionality and a little more training.

What is the first step for someone who wants to start blending AI scale with human led coaching?   

You can’t solve everything at once. You have to start with one thing. Choose one workplace behaviour that matters. Consider starting with something easy to quantify, such as customer satisfaction, safety, sales coaching, or a specific process that people find challenging.

Then, talk to people about how they are currently getting help with that task or process. Is there training available? Are they using it? If they are not using it, why not? Who are they asking instead? What hidden experiential knowledge are they relying on to get the work done?

Once you understand that, map it out and ask, “Where could AI plug in?”

For example, if there is a person in the organization who has very specific knowledge about a task or process, you may be able to train a chatbot to support others using that person’s tacit knowledge. That one use case can become a pilot or test case. Work within the tools your organization is already allowed to use today, and try something small and practical.

You will likely benefit just as much from the conversations you have with people as you will from the solution itself. Those conversations will help you uncover what people actually need, where the current training is falling short, and where support would make the biggest difference.

Then, close the loop. Use the tool, assess the results, and measure what changed.