Unleash the Power of Human+AI: 4 Takeaways of AI in L&D

We had an exhilarating two days with L&D and HR leaders from a variety of industries at the GP Strategies 2024 Client Forum. Both days were full of keynote presentations, workshops, success stories, demonstrations, and lively discussions on how and where to apply artificial intelligence (AI) strategies and technologies for the greatest effect.

Here are four takeaways we identified for AI in learning and development.

#1: AI Is Evolving Enterprise Skilling

AI is reshaping how many HR leaders view job roles and skills, and how to deploy them within an organization. In addition to optimizing job tasks of individuals themselves, some enterprise AI integrations are creating new ways to identify, analyze, and deploy skills.

AI excels at analyzing and organizing large sets of data. AI can make skills models and structures more dynamic, such as analyzing and making faster connections in clustered skill structures and skill marketplaces.

However, AI is only as good as the data it has to work with. It is critical for organizations to prepare their data for these types of integrations. The following chart highlights how the full continuum of how skills are expressed in an organization from competency models to the specific work outputs the employee generates on the job.

#2: Reprioritizing Leadership Soft Skills in an AI-Enhanced World

Large-scale and fast-paced changes, as the result of AI technologies, inevitably create fear and uncertainty. The media is full of headlines about jobs going away and how remaining jobs will evolve. Unfortunately, humans often fear the unknown and are resistant to change.

A recent poll from the American Psychology Association highlights the fear and uncertainty related to AI and its effect on jobs.

Percentage of workers intending to look for a new job in the next year by worry about AI making some or all job duties obsolete.

• 33% overall
• 25% not worried about AI
• 46% worried about AI

These number are worse among younger workers.

How Do Leaders Help Alleviate Fear and Drive Adoption?

Leaders can help their employees overcome the fears and unknowns surrounding AI by refocusing on uniquely human soft skills, gaining knowledge of AI, and communicating the benefits of AI to employees.

The following chart from Leah Clark outlines an approach leaders can take to help their employees embrace AI and enable efficiencies.

A key human differentiator in an AI-enhanced world are soft skills such as empathy. When leaders develop both soft skills and learn AI tools and capabilities, they will be able to help reduce the fear and unknowns of these technologies.

Understanding will help leaders alleviate fear among their teams with support and communication. As teams learn to embrace AI and upskill for it, they will realize new opportunities.

#3: AI Can Be an Expensive Investment: How and Where Should Organizations Get Started?

Developing and implementing AI, such as LLMs with company data and within protected company environments, can be costly projects. Many industries are split between taking a “wait and see” approach and implementing pilot AI projects within their organizations. However, it can be challenging to sit back and get left behind.

Getting Started with Individual AI Technologies

Employees and their teams should at least experiment with available AI tools and learn about short term productivity gains at a minimum. There are many AI technologies available and more emerge every day, but here is a quick list of tools to get started:

• ChatGPT, Microsoft Copilot, or Google Gemini. These tools can help write, generate ideas, summarize text, conduct research, and more.
• ElevenLabs. This tool has a limited free version where users can choose from a variety of AI voices and create text to voice audio. Human voices can also be cloned to create AI audio versions of real people.
• 7Taps. This platform includes multiple templates and features to quickly create microlearning courses. Features include quizzes, audio, video, and more. There is a free version to get started.

Getting Started with Enterprise AI: An Integrated LLM for Learning Content Libraries

Many organizations have challenges managing their learning content libraries and their components, such as eLearning, microlearning, videos, job aids, and more. It can be a daunting and tedious task to keep these resources updated, publish new resources, maintain records, and keep everything organized and easy to administer.

In a similar fashion to the evolution of skilling we mentioned earlier, AI excels at consuming, analyzing, and organizing vast amounts of data. Integrating AI technologies like an LLM within your learning content libraries and related platforms can solve many of these issues in an incredibly accelerated timeline.

The following chart outlines the challenges a recent customer was experiencing with their learning library of over 3,000 eLearning courses.

As a commercial learning provider, it is critical for this company’s users to quickly find courses they need to take via search or from recommendations. By integrating an LLM, the company solved these issues and continues to discover new capabilities, such as generating organization-specific quizzes, outlines, and more.

The benefits of integrating AI into their system can be organized into three key areas:

#4: Measurement is a Continuing Challenge

The learning and development industry has always had a challenge in measuring and proving the impact of learning. This challenge persists in the L&D and HR industries even with AI, where measuring and quantifying the impact of AI is not a structured process.

Establishing a measurement strategy along with baseline metrics to monitor and improve at the beginning of an implementation is critical to demonstrate the impact.

The following chart can help outline key criteria to set up a measurement plan.

The Human+AI Future Is Now

With adoption and technological advancement happening quickly, L&D and HR professionals need to respond, and fast. AI can offer capabilities that were not possible before, however, they need to be implemented carefully to be effective.

It is critical to not lose sight of the human component and human differentiators in the process. With careful considerations, organizations will be able to develop their AI-enhanced workforce and excel.

About the Authors

Matt Donovan

Chief Learning & Innovation Officer

Early in life, I found that I had a natural curiosity that not only led to a passion for learning and sharing with others, but it also got me into trouble. Although not a bad kid, I often found overly structured classrooms a challenge. I could be a bit disruptive as I would explore the content and activities in a manner that made sense to me. I found that classes and teachers that nurtured a personalized approach really resonated with me, while those that did not were demotivating and affected my relationship with the content. Too often, the conversation would come to a head where the teacher would ask, “Why can’t you learn it this way?” I would push back with, “Why can’t you teach it in a variety of ways?” The only path for success was when I would deconstruct and reconstruct the lessons in a meaningful way for myself. I would say that this early experience has shaped my career. I have been blessed with a range of opportunities to work with innovative organizations that advocate for the learner, endeavor to deliver relevance, and look to bend technology to further these goals. For example, while working at Unext.com, I had the opportunity to experience over 3,000 hours of “learnability” testing on my blended learning designs. I could see for my own eyes how learners would react to my designs and how they made meaning of it. Learners asked two common questions: Is it relevant to me? Is it authentic? Through observations of and conversations with learners, I began to sharpen my skills and designed for inclusion and relevance rather than control. This lesson has served me well. In our industry, we have become overly focused on the volume and arrangement of content, instead of its value. Not surprising—content is static and easier to define. Value (relevance), on the other hand, is fluid and much harder to describe. The real insight is that you can’t really design relevance; you can only design the environment or systems that promote it. Relevance ultimately is in the eye of the learner—not the designer. So, this is why, when asked for an elevator pitch, I share my passion of being an advocate for the learner and a warrior for relevance.