Artificial intelligence (AI) continues to fascinate organizations with its seemingly endless ability to boost productivity and improve business results. In this blog post, discover how learning and development (L&D) departments are leveraging AI to improve processes and streamline workflows.

L&D leaders are beginning to explore how AI capabilities can augment and support their workforce development strategies. AI has the potential to help L&D teams scale their programs, improve efficiency for all users, and offer workers more personalized development opportunities.

The explosion of interest in AI promises to create a climate in which there’s a need for more sophisticated upskilling and development. The World Economic Forum’s Future of Jobs Report found that more than 75% of organizations are likely to adopt AI-powered technologies by 2027, and 42% of companies will prioritize AI training for their workers. Individuals are also hungry for more AI skills. LinkedIn’s 2024 Workplace Learning Report found that four in five people want to learn more about how to use AI in their jobs. In short: skills needs are evolving in rapid and unexpected ways, and AI isn’t just an example of that fact—it’s also a great way to address it.

With both employees and organizations eager to adopt this technology, it makes sense that more companies are exploring how to implement AI concepts in L&D strategies.

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4 Potential AI Use Cases in Learning Management

Busy professionals need accessible learning opportunities that fit into the flow of work. L&D teams need their development programs to align with organizational goals while also considering employees’ career aspirations. L&D professionals can respond to both of these needs with the emergence of various AI concepts, tools, and features. Below are four concepts that L&D leaders can explore to save time, improve learners’ experiences, and enhance the effectiveness of training programs.

1) Personalized Learning With Recommendation Systems

AI-powered recommendation systems can guide employees through personalized learner journeys. These systems analyze vast amounts of data from your learning platform, such as each employee’s current skills, job role, and career aspirations. The AI also considers learner preferences, performance information, and employee behavior patterns. The system analyzes this data quickly and uses it to recommend relevant courses and resources that align with each employee’s specific development needs.

Recommendation systems save employees and learning leaders a lot of time. Employees don’t have to figure out which career courses they should take next to progress professionally. A recommendation system can identify gaps in their skill set in minutes and suggest a relevant learning program that helps them bridge those gaps while connecting the learning pathway to their organization’s strategic goals. By doing this, instructors no longer need to spend hours reviewing data to assign relevant content to learner groups, giving them more time to support employees and help them understand essential concepts.

2) Streamlined Employee Feedback Via NLP-Powered Chatbots

L&D leaders can rely on natural language processing (NLP) to streamline a variety of tasks. NLP can give employees immediate feedback based on their responses to a professional development assessment, empowering them to progress in their learning journeys without waiting for notes or suggestions from a human instructor. 

Employees could also leverage a chatbot embedded in their LMS, LXP, or employee engagement platforms to deepen their understanding of a particular topic or professional skill as they work through learning modules. NLP technology can help instructors streamline content development, lesson planning, and assessment grading. Chatbots can also help differentiate lessons for specific groups of learners.

3) Course Content Creation at Scale

Implementing AI tools can also lead to significant cost savings. While L&D professionals are certainly capable of manually developing training content, that isn’t the best use of their skills. These valuable internal resources will be better spent handling other essential tasks that can’t be easily managed with technology, like facilitating training and coaching learners.

For organizations that don’t have internal learning designers, generative AI tools can help them develop professional learning content that’s compatible with their LMS. This could save significant costs for companies that previously had to rely on outside vendors to meet their professional training needs.

4) Predictive Learning Analytics

Predictive analytics involves analyzing historical data and applying machine learning algorithms to make predictions about future outcomes. L&D leaders use this generative AI tool to:

• Anticipate employee behaviors
• Identify skills gaps
• Forecast which employees might be at risk of not meeting compliance expectations
• Optimize instructional strategies
• Assess training effectiveness

Predictive learning analytics offer valuable insights and inform decision-making processes. By analyzing data points like employee engagement, assessment outcomes, and interaction patterns, this generative AI tool helps L&D leaders anticipate their employees’ professional learning needs and make strategic decisions to improve their development strategies.

Let’s say an employee consistently demonstrates low engagement with a mandatory compliance course. They exhibit poor performance on their assessments and appear not to understand the material. A predictive analytics model may proactively generate an alert for learning administrators that ensures they’re aware of the employee and can intervene with the necessary support to help them improve and meet training expectations.

READ MORE | ‘3 Success-Driving Functions Every Compliance-Focused LMS Must Have’

Tips for Integrating AI into Your L&D Programs

The specific tools and strategies learning leaders use in their professional development programs will depend on their organization’s goals. For many organizations, the goals include saving time, increasing productivity, and reducing overall costs. The key to effectively implementing AI into training programs is understanding those goals and choosing the tools that are best suited to achieving the desired outcomes.

Companies hoping to save their learning designers time and increase their productivity should consider AI in their L&D strategies. In addition to the use cases discussed above, generative AI may also be used to automate time-consuming tasks, such as:

• Sourcing course materials
• Developing lesson plans
• Creating assessments
• Evaluating learner responses and providing feedback

Learning designers and other L&D professionals can save hours or even days of work by automating tasks with generative AI tools. With so much time saved, they can focus on higher-level, labor-intensive activities like designing engaging training environments and supporting learners’ individual needs.

Generative AI tools also help organizations implement data insights in a more timely manner. It could take human workers hours or days to study the engagement and performance data, but generative AI can offer intelligent analytics and insights in mere minutes. AI-powered tools and plugins rapidly collect and analyze large volumes of information, saving L&D teams time and increasing their productivity. With AI, L&D departments are empowered to make data-informed decisions about how to improve their training programs and best meet employees’ learning needs.

AI-powered learning technologies have the potential to dramatically improve efficiency and enhance professional development initiatives. Organizations need to evaluate their specific needs and purposefully choose an AI strategy and the related tools that will help them reach their goals.