The World Economic Forum predicted that artificial intelligence (AI) and big data will be among the top most in-demand skills by 2027. This makes sense, given the rapid advancement of generative AI technologies across sectors, along with businesses’ need to navigate large data sets and make data-informed decisions.

While AI and big data skills may seem unrelated at the outset, there’s a clear connection between the two. For learning and development (L&D) professionals, data and analytical skills enable you to use AI tools more effectively by enhancing your understanding of how these technologies operate. This allows you to generate more precise, relevant outputs from your prompts. On the other hand, AI tools can help you make sense of training-related data and metrics, allowing you to extract meaningful insights to prove impact and make data-driven decisions.

Of course, L&D leaders aren’t expected to have the skill set of a data scientist or machine learning engineer. However, developing foundational data and AI skills will help you stay competitive in an AI and technology-driven future of work.

Here, we’ll share tips for building foundational data analytics and AI skills as a learning leader.

Real-World Use Cases

A key part of building your AI skills includes identifying AI use cases in your job role. Some common use cases for AI in L&D include:

  • Course Outlines: AI is great for getting a working outline of a course, says Alyssa Kaszycki, learning product manager at Training Industry. While you’ll still need to review the outline and make adjustments based on feedback from subject matter experts, a generative AI tool can provide you with a working outline to get started.
  • Assessments and Quizzes: AI can also generate different types of training assessments. AI-generated assessments could include anything from formal certification exams to bite-sized quizzes for a mobile learning course.
  • Supplemental Learning Activities: “AI is also really helpful with building learning activities,” Kaszycki says. For example, it can help create job aids or hypothetical case studies for a course. “It doesn’t take expertise to come up with the details for a hypothetical case study. It just takes effort,” Kaszycki says. If a task requires ample effort yet limited expertise, “AI is great for it.”
  • Personalization and Learning Pathways: AI-powered personalization tools are designed to help learning leaders pull individual courses or modules on specific topics from their portfolio into a curated learning journey for the employee. Chelsey Groh, CPTM, director of sales enablement and training at Motus, says that some of the tools in her team’s tech stack allow them to create learner profiles and recommend relevant training programs to employees. This allows for more “personalized and interactive learning experiences,” Groh says.
  • Learning Analytics: Chris Massaro, CPTM, HR manager, talent development at Alight Solutions and instructor for Training Industry’s Learning Analytics Workshop, suggests using AI to scan written course materials to identify key metrics and measurements. AI can also create different types of data visualizations (e.g., charts, graphs, etc.) and can even select the most effective visualization for data at hand, Massaro says. Many learning technologies on the market have AI-driven analytics features. Groh says that Motus uses Highspot, an AI-powered learning management system (LMS), which provides AI-driven analytics. These analytics offer deeper insight into learners’ behavior and skills gaps, which helps Groh make better recommendations and predict future needs.

After identifying AI use cases in your job role, here are some ways to continue refining your skills in AI and learning/data analytics:

  1. Experiment, experiment, experiment.

When it comes to AI skills, hands-on experimentation is key. Groh suggests starting with a publicly available tool, like ChatGPT; other options include Copilot or Bard. Kaszycki agrees that experimentation is a great place to begin building your AI skill set. If you haven’t done so already, she suggests practicing using AI outside of work (for example, to help plan a vacation or to generate recipe ideas), before jumping in to using it on the job. Doing so will help you feel more comfortable with the technology before using it in a work context.

Then, you can begin experimenting with using it for low-stakes job activities and tasks — such as for writing an email to remind learners of an upcoming course or to complete their course evaluations — before adopting it for the key use cases you’ve identified.

Importantly, before using AI on the job in any capacity, make sure to review any company policies or guidelines around AI use to ensure compliance.

  1. Learn how to write effective prompts.

If you’re not using effective prompts, generative AI tools won’t provide you with the content you’re looking for, Massaro says. An effective prompt is specific, provides the necessary context and avoids unambiguous language. If your prompt didn’t generate the output you were looking for, iterate: Rework your prompt and provide the AI tool with feedback on how to improve its output.

Prompts related to data and learning analytics might include: “Identify 3-5 key performance indicators (KPIs) to measure the success of a sales training course I’m developing, based on the course outline I’ve uploaded.” Or, “Create two different types of data visualizations based on the spreadsheet I’ve uploaded detailing key metrics and outcomes for a leadership training program I delivered.”

(Note that not all generative AI tools currently allow you to upload files using a free version. Do your research beforehand to determine which tool to use if you’re looking to upload documents.)

  1. Keep up with industry-specific news and innovations.

Follow industry experts and thought leaders on LinkedIn and subscribe to relevant journals, websites and magazines to stay up to date on the latest AI advancements in the L&D space, including how AI can help support learning analytics processes (i.e., identifying, measuring and reporting on key training metrics and outcomes).

  1. Maintain human oversight.

Ultimately, AI is just a tool, Massaro says. That said, “It can be an incredibly useful tool, as long as we know what the parameters [are] and the strategy we want to use it for.”

It’s important to remember that just as human-generated content can be biased, plagiarized or misleading, so can AI-generated content, Kaszycki says. “Just because it’s a machine doesn’t mean it’s immune from ethical concerns, because everything AI generates is based on data uploaded by humans.” Thus, while AI can streamline certain L&D processes, human oversight is still paramount to help mitigate ethical concerns around bias, data privacy and more.

  1. Consider a course or workshop.

If you’re committed to building your AI and learning analytics skill set, consider taking a course or workshop from a trusted third-party provider. Doing so will allow you to learn from an experienced instructor and from your peers.

Many courses and workshops provide actionable job aids for continued support back on the job. They may also provide social badges designating your expertise in the subject area, which can help to verify your skills and set you apart in the market.

Staying Competitive in the Future of Work

Building AI and data skills can seem overwhelming for even experienced learning leaders. However, by investing in these future-forward skills, you’ll be better positioned to thrive in a fast-paced, AI-driven future of work.

As Groh explains, if you don’t take the time to build these skills, “somebody else will.” And they’re going to be more efficient; they’re going to be able to accomplish more, and their skill set will be more relevant to the business.

To learn more on how to leverage AI in your job role, and to begin building your AI skill set as a learning leader, check out our AI Essentials for Training Managers Certificate course.