Boutique learning: How L&D can add value in a world of AI-generated content

AI is poised to change industries and the world as we know it. For learning and development (L&D) leaders who already struggle to position themselves strategically within organizations, demonstrating their value and finding ways to adapt to AI will be crucial.

One key opportunity for L&D lies in boutique learning — providing the right skills at the right level — rather than using AI to create generic content with a low level of skill transfer. 

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But how will this look in practice? We talked with L&D strategy consultant Peter Meerman to unpack the topic. Working with leading companies across different industries, including Shell, Phillips, and Novartis, he has a deep knowledge of learning strategy, data analytics, and other tools and processes that can take L&D leaders to the next level.

In our conversation, we cover:

• How AI will change the L&D profession
• The importance of “boutique learning” in a world of AI-generated content   
• A step-by-step look at how L&D can quantify its impact.

Ready to delve deeper into the topic? Mark your calendar for our panel discussion on November 22nd and discuss AI in learning with experts Peter Meerman, Ross Stevenson, and Dr. Clemens Lechner.

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How do you envision AI shaping the future of skills training and L&D profession itself?

In terms of skills, it's going to change the whole landscape. 

My concern is that if we don't step up in the world of corporate training, technology like ChatGPT will just take over most of it. I think you will only survive in L&D if you have very specific added value. 

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We need to start thinking about what I refer to as “boutique training programs” or training offerings that provide practice, feedback, and the right context to truly help people learn new skills. If you can provide those elements, I think you'll stay relevant. If you keep making very generic content like everybody else, you'll be replaced by chatGPT and other generative AI solutions.

This goes both for commercial companies as well as internal L&D departments. That's why I'm passionate about the topic. We need to take skills seriously, and I don't think we're doing that enough. Everybody talks about it, but I think very few people try to understand what it really means to learn new skills and very few L&D teams adjust their strategy accordingly. 

What do L&D teams have to keep in mind when starting to use AI for training?

We need to build the right skills in L&D to be able to fully leverage the potential of AI. The trapdoor is ensuring we don’t use AI to generate more meaningless, generic content that has a very low level of skill transfer. We can use AI to make that stuff faster and cheaper, but if you spend $100K on a series of meaningless programs that would have cost you $200K without using AI, AI is still not going to generate a lot of value. It’s the difference between being efficient and effective.

We need to start realizing that AI will take away a lot of the need for training programs that we currently create because people can just go to their personal AI coach. So we need to ask ourselves the question: why would you go to an L&D company or department?

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This is what I mean by boutique learning; being more selective in what you do and tailoring programs to individual needs.

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There may be topics that are very strategic for a company or topics at the core of a company’s intellectual property, and I think those are areas where L&D should focus.

Boutique learning is also much more skills-focused rather than knowledge-focused. Since we have so much knowledge available at our fingertips these days, knowledge doesn't really matter anymore.

In practice, this could look like focusing on in-person training with excellent facilitators who pick up on signals from the audience and adjust the program as they go. Or doing a more serious intake to really understand where people struggle and where they need help, rather than just giving them a one-size-fits-all program. 

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We also need to be explicit in how we demonstrate our value. 

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“We'll make you a better leader” is not tangible. But if you would say, “People who follow our programs typically get 10% better ratings in 360s,” and you have well-structured evidence to back that up, then people might be convinced to follow the program. 

How can L&D teams evaluate if a training program has had the desired impact?

It comes down to a few key steps.

1. Set your targets & define your audience

The starting point is to make your objectives explicit. Set a target for what skills you want to build for which people (specifying their level), because you don't want people to build the wrong skills. In an ideal situation, you should also be very explicit about your audience, not in terms of numbers, but in terms of data characteristics: job function, location, people who are structurally underperforming, etc. If you express your audience in terms of data, then you can start to generate insights into if you are actually training the right people. 

2. Quantify performance

Then, if we talk about impact, the ultimate goal is to correlate your training data with business performance data, i.e., looking at people who went through the training to see if they show improved performance. It’s also important to quantify what performance looks like. Hopefully, the business has some idea of KPIs that can give a quantified target — 5% more sales or 10% less turnover, for example. 

3. Design for analytics & measure early

Lastly, start measuring as soon as possible because the results can give you very fast indications of whether the questions and content used in your program are actually understood by your audience, and you can start improving as you go. Also — and this is critical — make sure that whatever you design supports the analytics. I refer to this as data-driven design. Typically, we think about data too late in the process. So, if you want to track progress across modules, design your modules in a way that you can do so. One example would be streamlining evaluation forms across different programs to enable cross-program analytics. 

All industries are going through some kind of transformation because of digitalization and AI. What does that mean for the future of upskilling?

I honestly believe that we don't know what future skills are going to be needed because we have very little reliable data or evidence to back things up. I think it's more important to build a structure that can track and analyze data on an ongoing basis to predict what skills you think your organization is going to need, rather than running these analytics as an annual project.

Ongoing skill analytics is going to be key, and it will allow you to react faster if things develop in a different direction than expected or wanted.

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And there are skills you can track more easily than others, so I would start with those.

For example, if you think one of the skills would be “taking ownership,” think about what that means in practice. How would you measure If somebody is taking ownership? Do you analyze email text? Do you scan action lists? These questions are really hard to answer. So I would start with skills that are easier to track and analyze.

What I do hope is that all L&D professionals upskill themselves in data and analytics, because if not, they will struggle to keep up. No doubt, we'll see a lot of startups using AI training rather than the talking heads approach. 

L&D professionals will have to think harder about where they add value and be more explicit about it to their customers and employees. 

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We also have to reinvent ourselves and be very vocal about it. Otherwise, we’ll end up being tucked away in a dusty corner of the organization and nobody will come to our programs anymore because they have found a solution that's quicker, easier, and equally good. We are not alone in this — a lot of industries are asking these existential questions as well. So now is the time to imagine what that change could be.