Recently, I had a meeting with the head of the Sales Training and Development department of one of the largest beauty companies in the world, which engages with millions of product resellers. He posed a question that initially stumped me: 'How can we use AI to better educate our resellers?' We discussed some obvious ideas, such as employing a chatbot for interaction, but I must admit that I wasn't entirely satisfied with my initial response.
After the meeting, I reflected on the conversation and was amazed at the myriad opportunities for applying AI in learning, especially in personalizing the educational journey. We'll delve into all of this in this article, but let me provide a little spoiler: using generative AI, it's possible to create a much larger volume of new content. This content can then fuel personalized learning paths through recommendation algorithms.
Let's kick things off by listening to a statement from Richard Uchoa Vasconcellos, founder and CEO of Revvo, a company specializing in humanized digital learning with a focus on results, where I also create courses. Recently, Richard and I conducted a live session on the impact of AI on the corporate education landscape, and he shared the following insights. Listen up:
"During the pandemic, we tripled the number of virtual meetings. People spend 57% of their daily time in communication, whether in meetings, emails, Zoom, and various communication tools, instead of actively creating with Word and PowerPoint. Therefore, Microsoft is betting that artificial intelligence (AI) will significantly reduce this type of work. The idea is to automate tasks, increase productivity, and enable AI to summarize information, freeing up time in the schedule. Tools like Microsoft's Co-Pilot are available in Word, where you can provide prompts, and the AI will assist in creating the work. In the next 3 years, the working model will be radically transformed."
To initiate our discussion on the applicability of AI in T&D programs, let's take a step back and ask ourselves: why do Learning and Development (L&D) teams exist? What are we working to achieve in our organizations? We are here to ensure that people are ready to perform, continually improve in meeting the requirements of their roles, and possess the right skills to operate in new and effective ways in the future. So, instead of thinking about how to create a course more quickly, how about we focus our attention and energy on integrating generative AI to address these objectives in the most direct and efficient way possible? In fact, what if courses were not even necessary for this?
People may wonder if adopting generative AI technologies to design broader L&D courses is better than an individual using the same tools to meet their more specific learning needs. The short answer is that L&D teams simply cannot be as granular, meaning as specific and assertive. So, would we be out of a job assuming that our generic and broadly appealing courses will be more useful—or of any use at all—compared to the same tools in the hands of those closer to their personalized needs?
What is the solution here?
One of the most exciting elements of generative AI is its ability to leverage the data it has access to. This will be extremely valuable for L&D (and organizations in general) in seeking to understand more about their teams and potential performance and capability issues. For example, where is the organization losing valuable professionals? Where is there an excessive reliance on external hires? Could there be a pathway for talent to move into crucial roles?
Viewed through this lens, it becomes clear that the utility of generative AI for L&D teams is not to create comprehensive educational products to meet assumed needs. After all, our role is to highlight critical points of failure where we can apply our resources to make a significant difference. Therefore, by combining AI with relevant, recent, and now instantly accessible data, we can be much more powerful in addressing the real needs that are causing friction in our organizations and employees.
Once L&D teams are armed with information, they can enlist subject matter experts to refine and contextualize the generative resources created by AI to meet specific needs. This is great because one of the most impossible tasks we ask SMEs (Subject Matter Experts) to do is unpack everything they know when completing a task or doing things right. But it's always easier to refine someone else's draft than to start from scratch.
With all this in hand, we can provide highly specific resources to those who need them in no time. For example, instead of a 20-minute course, we can provide just a few guiding sentences that can be accessed during a brief work break and applied immediately. It's not about creating faster courses; it's about solving problems as efficiently as possible.
This brings us to the concept of Microlearning, which is a major theme in the field of T&D nowadays. Generative AI would be able to create highly customized and granular content, which can be more relevant to the listener. Let me give a practical example: today, I have a T&D podcast for Globo's Content department, where I already apply microlearning through short weekly leadership podcasts. However, someone working in entertainment will receive the same content as someone working in drama, but their business challenges might be very different. My leadership course available on Revvo is the same for healthcare professionals and those in Pharma, even though the areas are similar, the mix of cases is the same because I don't have the capacity to create infinite custom examples. Once I received a request to customize my leadership course for one of the world's largest animal protein companies, where it took me months in scriptwriting because to make assertive examples, I had to study a lot about this market.
Well, Generative AI has come to solve all of this: it could create content with infinite granularity, making it more relevant, and doing so in real-time.
With this increased granularity of content, the next step where AI can help is in the content recommendation part: whether via a chatbot or otherwise, the concept is that AI will be the one recommending this content based on your profile, your history, your current situation. Let's take the example of the University of Phoenix: Marc Booker, PhD, Vice Chancellor of Strategy at the University of Phoenix, said in an interview, "At the University of Phoenix, we view AI like any other new tool that has entered the learning arena and improves student access to data and information to process and acquire knowledge more quickly. Whether it be the internet, the calculator, or even the printing press - all entered into social consciousness as disruptors with some caution and skepticism but were used to change the speed at which we can learn and share information to increase the depth of information we pass on to future generations. It needs to be handled with care and administration, but definitely not feared as it has the potential to do great good in the field of practical distance education and online learning."
The truth is that current AI as a tool and linguistic model can indeed help instructional designers refine the framing around the tone and positioning of their messages or lessons. For example, an instructional designer can use highly technical words about nuclear fission and present it in a more colloquial tone within the classroom for first-year students, asking the AI to adjust the wording or tone to meet the needs and levels of the students.
Moreover, from a corporate training perspective, AI models can even be asked to embrace important concepts, such as workplace diversity, and frame these concepts at an executive or new hire level to create a curriculum or quiz questions that feed into the learning outcomes being created.
Mark Booker said, "Using AI in this way can help save time with the creation of more personalized learning materials and enable instructional designers to do more with their content to meet different parts and their learning needs. So far, our experience using AI in curriculum development has been more like having another partner to review your work, to provide suggestions or different ideas to consider, rather than replacing human capital."
That's the point: remember episode 144 of Metanoia Lab, where I talked about the impact of AI on education in general, through a statement from Sal Khan? Fundamentally, AI will not replace educators and teachers but will complement their work and help the learner, serving as a kind of personalized tutor. So, that's what we're talking about, a technology that supports us in the corporate learning process and does not replace speakers and educators. It simply complements their work, helping to have more granularity and volume of content, and then a Netflix-style recommendation algorithm. Only then can we take advantage of the enormous transformation opportunity that AI is bringing to the world of corporate education.
