Upskilling Sandboxes: How to Develop a Strategic Approach to Generative AI in L&D
Many L&D leaders are still unsure of how to best adopt generative AI in their practices, and as this technology continues to gain ground, those who don’t adapt risk falling behind.
For L&D professionals just starting out with AI, it can feel like there’s a major choice to make. Do you take the red pill and use AI to create a load of content to plug content gaps? Or do you take the blue pill and look at the problems unique to your organization and employees?
In this article, we explore the current state of generative AI in learning and development through the eyes of Egle Vinauskaite, Learning Strategist and Director at Nodes. We’ll look at how to use AI conversation sandboxes for upskilling, and outline a framework for developing a strategic approach to generative AI in your L&D practice.
Have the robots really taken over?
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The current state of generative AI in L&D
Looking at L&D’s exploration of generative AI, only a small percentage of departments or teams seem to have started integrating AI strategically and holistically.
So far, the industry has focused on either experimenting with or limiting the use of AI to some parts of its learning and development workflows. They tend to leverage generative AI for aspects of learning content design, some administrative tasks, or content recommendations. Despite the hype, a surprisingly big chunk of people are not using generative AI at all, for various reasons.
Despite the hype, a surprisingly big chunk of people are not using generative AI at all, for various reasons.
However, on the vendor side of generative AI, the industry is building products based on large language models (LLMs), offering tools for course and video creation, marketing, and co-pilots. These tools emerged over 2023, indicating a shift towards changing product user interfaces and underlining the importance of prompting skills in L&D teams.
Although these new AI tools can improve your workflows, L&D teams should be wary of easy, rapid content, as we might see more platforms filled with less-than-useful content than in the past. Leveraging generative AI should be targeted at helping us deliver learning interventions when and where our learners need them.
Upskilling: AI conversation sandboxes
In the near term, generative AI can offer innovative ways to help L&D teams deliver personalized skill development to ramp up performance significantly.
For example, there’s enormous potential for generative AI to enhance upskilling through deliberate practice in a sandbox—a virtual environment where employees can learn, experiment, and develop their skills without impacting real-world operations.
If the AI has data on the learner’s performance, the sandbox can facilitate a conversation simulation to provide the employee with personalized, real-time feedback on their performance, creating a transformational method for helping employees upskill.
“I can see how, in time, we will be able to provide these sandboxes and get good personalized feedback on any other valuable skills that produce any tangible output that AI can provide feedback on,” Egle Vinauskaite, Learning Strategist and Director at Nodes.
“I can see how, in time, we will be able to provide these sandboxes and get good personalized feedback on any other valuable skills that produce any tangible output that AI can provide feedback on," Egle Vinauskaite, Learning Strategist and Director at Nodes
Generative AI for analysis in L&D
Generative AI has a huge potential for enabling targeted analysis to help speed up your team’s workflows and deploy impactful learning experiences.
To start experimenting with ideas about how you can leverage AI to harvest the critical data you need, here are three ideas that some other L&D teams are testing:
1. Leverage AI to observe your employees' skills in practice rather than relying on traditional proxies, including tenure in the role, course completions, or self-evaluations.
2. Use generative AI to help your team identify pockets of expertise within the organization to connect these silos for knowledge sharing more easily.
3. Enable predictive analytics to identify emerging capabilities within your organization based on employee queries, skills data in the market, and highlighting where the skill gaps are forming.
“I think these are really important steps forward that generative AI can enable if we choose to take it in that direction,” Egle Vinauskaite, Learning Strategist and Director at Nodes.
“I think these are really important steps forward that generative AI can enable if we choose to take it in that direction.”
Develop your strategic approach to AI for L&D
It’s important to clarify your organization’s AI policy regarding how and when AI can and cannot be used.
From here, you can develop your L&D team’s strategic approach to AI by implementing the following framework:
Experimentation and Adaptation: Continually experiment with AI to discover your own use cases and skills gaps. You can also map out your processes to understand where AI’s benefits sit for you and your organization.
Targeted use of AI: To ensure you know where the business value of leveraging AI may lie, you need to have a firm understanding of your department’s functions, key performance indicators (KPIs), and priorities.
Build the infrastructure: Prepare for AI’s broader use within your team and organization by creating a data capture and analysis infrastructure to leverage AI effectively today and in the future.
Key takeaways for L&D professionals
Here are three key steps to help you roll up your sleeves and leverage generative AI in your L&D practice.
1. Understand where you are
First, start by understanding the tools and systems you already have in place. Map out your critical processes and understand how people are already using AI, and be sure to clarify your organization’s AI use policies.
2. Identify your AI goals
Next, review your team's pain points, challenges, and KPIs, prioritize them, and locate where generative AI will add value. Then, experiment with the little things that can tweak your team’s workflows.
3. Find your own AI use cases
Finally, get together with your team to identify what AI use cases may apply to your workflow. Discuss the results of your experiments and the tools people use, and extract the knowledge within your team. You can also look at other use cases within your organization.
Get even deeper insights on this topic in Egle Vinauskaite’s episode on The L&D Podcast: Where Are We At With Generative AI in L&D.
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