The time available for learning is zero
When it comes down to it, the time a worker can dedicate to their own learning is zero.
Companies are built on Parkinson’s law: the amount of work expands to fill all the time available. This is true for people who work in production, for people who work at a desk, for people who work on the move and for people who don’t have a desk at all.
So the time dedicated to your own upskilling and reskilling is always a sacrifice. It can be a sacrifice for your employer, because it loses productivity and costs go up. It can be your sacrifice, because to keep productivity high you give up your private time. Or it can be a sacrifice that falls on your customers: think of a salesperson, the time spent learning is time not spent with customers.
What typically happens is that learning slips to second place, or third, fourth, fifth.
And yet we live in a moment in history where AI is changing everything: products, roles, processes. People are asked not only to learn more things, but to learn them faster. And here there is a very clear contradiction.
Where learning breaks down
I have been working with companies small and large for almost thirty years, and I have always seen the same three situations repeat themselves.
The first is operational staff, who don’t sit at a desk. Reaching them is very expensive, so companies tend to limit the upskilling and reskilling of these people a lot.
The second is people who work at a desk but don’t have time. If you bring them into a classroom, on one side the HR operations costs needed to run the course go up a lot, on the other it becomes very hard to measure how the course is going. Did people understand, did they learn, or did they simply spend two days away from their desk?
The third is digital learning for people who have a computer. It is boring, long, it takes time, and people turn courses into what I call a skip factory: you quickly click through all the slides or videos in the LMS, and in the meantime you do something else, that is, your job.
We have always asked people to stop working. In order to start learning.
When instead people receive very specific content, targeted to the specific way they learn and to their background, and they receive it in the tools they already use for work, that is, when learning happens in the flow of work, both engagement and their ability to complete courses increase enormously.
A case: when the seniors leave
I once spoke with a large Turkish manufacturing conglomerate. Because of the economic crisis the country was going through, skilled and senior employees and workers were moving abroad to be able to support themselves.
A gap had opened in the workforce that had to be filled, not only to stay competitive, but to keep existing as an organization. And yet it was impossible to upskill the junior staff: the young people refused to train in the way the company offered, that is, with standard classroom courses.
In that case, mobile learning was aligned not only with the employer’s needs, but also with the employees’ needs and with their lifestyle: new learning content used on the phone, in chat apps, asynchronously. This made possible something that would otherwise have been impossible.
It doesn’t matter how much you study. It matters how much you learn.
If you look at companies’ annual reports, they all show off the number of training hours delivered. And for what reason? To really have an impact on company KPIs, what should matter is not how much you study, but how effectively and efficiently you learn.
Traditional completion data simply tells you whether the user has viewed the whole course. I can flip through a printed book from beginning to end in a few seconds: I hold it in one hand and let the pages run. To read it and understand it, I need much more time. In both cases I have “completed” the book.
This is why completion should not simply mean “done”, but “done how”. You need at least three parameters:
- whether the task was completed; (ditto)
- when it was completed: right away after the ask, after a few hours, after a few days, or only after a reminder;
- how it was completed: the exercise was done, and it is correct.
Every vendor, or potential vendor, that claims to have a product that measures course completion should be asked what algorithm is behind that KPI.
For HR and managers, having this data means getting much closer to understanding the impact of learning on the business, because they can relate what has really been learned, not just completed, to business data in the short, medium and long term.
Who checks what the AI writes
Not having a human being who reviews a course before it is distributed brings two main risks.
The first is the now well-known risk of hallucination: the AI writes things that sound true but are not.
The second is that what the AI produces drifts away from what the company wants to teach, that is, from the content it wants to train its people with. Think of an HR process: no AI will know a company’s process as well as the people who wrote it. Think of all the training on company culture. Culture is created by the company, not by an AI.
Decision-making power must stay in the hands of the learning designer. The AI needs to show the learning designer how it has broken down and classified the content before creating the course, so that the learning designer can step in with their own decisions and give the AI detailed instructions.
What to do starting tomorrow
Three concrete things for people who work in HR or L&D.
Move learning into the flow of work. Very simply, learning by doing or learning from peers: instead of interrupting work, while I work I have someone who helps me improve my skills.
Break learning up into different moments. A podcast, targeted emails: a complex topic that would take two days in a classroom is split into several moments over the working month.
Use the digital tools the company already has. Employee portals or company chats, which can automatically send specific teams insights or content related to the work they are doing.
And a fourth one: stop counting hours and start measuring how much and how people learn.
