How generative AI is changing the L&D landscape

Generative AI is changing how L&D teams do their jobs. Rapid advances in generative AI are opening up new opportunities for L&D to respond to the rapid need for new skills, with adaptive, personalised learning. Studies are showing that using generative AI can potentially save L&D between 10 to 25% in efficiency gains, depending on the activity.

Philosopher Niccolo Machiavelli once said, “Whosoever desires constant success must change his conduct with the times.” Success today hinges on our ability to roll with the changes. The mainstream rise of generative AI models like ChatGPT and Microsoft Copilot are testaments to this, taking the world by storm within mere weeks and drastically changing how we work. Such AI models are also changing the skill mix needed by employees, as more work becomes automated including (in the case of ChatGPT) summarising research, drafting marketing strategies, and writing apologies to customer complaints. In a somewhat meta way, generative AI is also changing how L&D teams do their jobs. Rapid advances in generative AI are opening up new opportunities for L&D to respond to the rapid need for new skills, with adaptive, personalised learning. Studies are showing that using generative AI can potentially save L&D between 10 to 25% in efficiency gains, depending on the activity.

Accelerating content creation and curation
Content creation and curation is one of the most obvious use cases for generative AI in L&D. Creating learning materials and quizzes can be extremely time-consuming, taking anywhere from 10 hours for infographics to 74 hours for online learning simulations. Speeding this up by automating some elements of this, can free up L&D time and resources for other tasks. It can also help to scale a learning program across an organisation, or even globally. 

Efficient Content Generation: Generative AI models can quickly generate high-quality learning content, reducing the time taken to produce engaging resources. A course that previously took half a year to develop could potentially be created within weeks using AI. This allows L&D teams to keep pace with rapidly evolving industry standards and emerging skills.

Personalised Learning Plans and Pathways: Every employee has specific developmental needs, which is a mix of ‘collective’ (AKA, they do a course because they are an HR professional working in Europe) and ‘individual’ (they want to learn a new skill to advance their career). Using AI, L&D teams can analyze individual learner data, such as their current skills and goals, and tailor learning plans accordingly. Personalized learning allows L&D to respond more quickly to individual learning needs, making each employee feel taken care of, which then leads to better engagement, retention, and satisfaction. Plus, by identifying each learner’s strengths and weaknesses, AI can create personalised learning paths, enhancing the overall effectiveness of the learning experience.

Instant localization: More companies, like Spotify, are experimenting with generative AI models that can automatically translate content into an individual’s native language. In a similar way, learning content can be automatically translated to make it accessible to a wider audience and to save time and resources on manual translation. A learning pathway can scale globally, in multiple languages, thanks to generative AI. 

Keeping employees engaged and challenged

Another benefit of ChatGPT et al is how it can dynamically adapt to different individuals’ preferences, skill levels, and interests. This can help keep employees engaged with continuous learning, building the kind of learning habit that’s needed to keep up with the pace of change in society today. 

Adaptive Learning Pathways: AI can dynamically adjust learning pathways based on learner performance, ensuring that they are continually challenged and engaged with content that suits their current skill level. Adaptive learning, in this way, meets every learner where they are at. Recommendations change depending on an individual’s proficiency level, so they don’t have to waste time (and get frustrated) with basic courses when they’re already an intermediate in a skill. 

Learning Recommendation Engines: AI-driven recommendation engines can suggest specific learning resources based on a learner’s history, preferences, and performance. This ensures that learners receive content that is directly relevant to their needs — similar to how Netflix recommends shows based on someone’s watching habits. This is especially important in an increasingly crowded content ecosystem where learners have less time to sift through literally thousands of pieces of content to find something relevant. 

Real-time feedback and coaching

Generative AI extends its capabilities beyond content creation. It can also serve as a real-time support system for learners, providing valuable feedback and coaching through chatbots and other interactive tools.

Chatbot Interaction: AI-driven chatbots can engage with learners, answering questions and providing instant feedback on assignments and assessments. This real-time support fosters a more dynamic and responsive learning environment.

Continuous Improvement: AI can track learner progress and identify areas that need improvement. It can then offer tailored suggestions and coaching to help learners reach their goals faster and more effectively. This becomes even more powerful when combined with human feedback, creating a data-driven approach to development with human insight. 

Strengthening learning strategies

On a leadership level, generative AI can also help with strategy research and development. For instance, in creating research surveys to understand the current state and perceptions of L&D among workers. 

Starting Point Surveys: Leaders can ask AI to help create starting point surveys to assess employee perceptions and attitudes about their access to learning, development, and upskilling resources. AI can consider contextual factors like company size, industry, and learning resource usage (based on data from learning platforms).

Promoting L&D Programs: AI can analyze learner behaviour, social interactions such as likes and shares, consumption and frequency habits to better segment audiences into communities who share the same developmental goals or interests. It can use this to recommend and promote learning resources that are likely to most align with what each individual wants to achieve.

Diversifying Learning Resources and Modalities: Is your content strategy lagging? Do you have too much or not enough content? Is the quality of your content “good enough”? These are the questions that AI can help answer by analyzing your assets and learning catalogue and exposing the gaps of needed skills vs missing content, for example.

Pitfalls to address

Although there are benefits to using generative AI in L&D, it pays to be mindful of the limitations and potential pitfalls of using AI in the workplace. Part of this is using generative AI to create too much irrelevant and poor-quality content. Because ChatGPT lowers the effort level needed to generate content, it can lead to far too many materials being created. Quality control and human oversight are vital to avoid this. 

Indeed, human oversight is needed with any use of AI. Any recommendation made needs to be clearly explainable to the human teams overseeing the AI. The use of employee data to train an AI model, and then to get insights from it, also needs clear consent from individuals. Otherwise, L&D might find itself falling foul of the EU AI Act, GDPR, and other legislation.

A changing landscape

Generative AI is reshaping the landscape of L&D, making it more efficient, effective, and adaptable to changing skill needs. Embracing this technology now will help L&D keep up with the times. 

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