it has already changed
After returning from speaking at DevLearn, one of the largest technology-oriented learning events in the United States, I’m still thinking about one of the audience questions.
[paraphrased] Will artificial intelligence (AI) change our roles? How?
In the context of automation and AI, the question was both what to expect and how to prepare. After four days of constantly talking about AI and experiencing hands-on technology demos in the Expo Hall, I think the audience already knew the simple answer. The short answer is yes.
Note that this article is not just about generative AI, which has exploded in popularity over the past two years. Consider diagrams of major large-scale language models [1]: gen AI is already part of a larger AI umbrella (including simple machine learning automation).
your role has already changed
However, I responded with a slightly different comment. It’s not the AI that’s changing your role. The role of learning and development (L&D) has long changed. I see AI not as a tool or technology, but as an invisible energy that accelerates change. In 2025, this “energy” will be available to the public as autonomous agents. These agents not only help you perform tasks, but also perform them on your behalf. Or instead of you.
We are not adopting AI to evolve our current L&D tool kit. We are rethinking everything we do as learning professionals to evolve as a profession and stay relevant. AI will be like wireless in the future. It’s invisible, but it’s fundamental to communication and the apps we use. In 2025, autonomous agents will emerge and literally take over your tasks. [2].
In this and the next article, we will discuss how the role of L&D is likely to change and what learning professionals can do today to evolve and stay relevant in the future.
How much will your role change?
It depends on whether you have a flip phone or a smartphone right now. Let me explain. How your role changes depends on your actions today. If your current role resembles that of a rapid content creator (flip phone) ten years ago, your role will change dramatically. In other words, if your tasks primarily involve creating content for your course, the change will be exponential. AI will accelerate the changes that have been happening for some time, with agents producing the same things you are doing today, literally 100 times faster.
If you are a learning expert and are acting as a consultant (smartphone) focused on behavior change and problem solving (some solutions may require courses), the changes may not be as dramatic. It’s probably not something like that. Still, it requires new strategies and new ways of thinking about which tasks should be prioritized to deliver value.
Should I throw away all my old stuff? no. Be strategic about what to change, stop, and start new. Much like the invention of computers and later the Internet, AI can be used for both positive and destructive purposes. Keep this in mind (along with data privacy, data security, and ethics) when building your strategy.
What can you do to prepare now?
There are some proactive steps you can take to prepare for the future of L&D amid changes in technology, AI, and the workplace. Many of the learning experts I spoke to believe there is a big difference between “playground” AI and workplace AI in terms of implementation.
Playground AI is all that is commonly available today. Without the right data (at least not your own), you can build simple prototypes and show amazing results without deep coding knowledge. But when it comes to the workplace, the rate of adoption is much slower.
Businesses are still figuring out how to provide secure and innovative tools and resources, and how to protect the security and privacy of their data. So what can we do professionally today? Here are some takeaways from speaking with industry leaders about this approach at today’s conference.
1. Invest in data literacy and analytical skills Learn the fundamentals of data
Start by building a basic understanding of data collection, analysis, and interpretation. Familiarity with tools such as Excel, Google Analytics, and Power BI is invaluable. Use analytics to demonstrate impact
Practice using data to show how learning impacts performance and aligns with organizational goals. Start by tracking key metrics like engagement rates, skill development, and performance improvements. Try A/B testing
Experiment with simple A/B tests using different learning interventions to understand what resonates with learners and drives results, and hone your experimental design and analysis skills. 2. Explore AI and automation tools Experiment with AI-powered platforms
Start exploring AI-driven learning platforms and content creation tools to understand how AI can support personalization and content curation. Automate repetitive tasks
Use automation tools to streamline administrative processes like tracking enrollments, sending reminders, and collecting feedback, freeing up time for strategic initiatives. Stay up to date on AI ethics
Familiarize yourself with ethical considerations for AI, such as data privacy and algorithmic bias, to ensure fair and transparent learning solutions. 3. Use evidence-based, practical workplace learning designs
Use your limited resources wisely. If the foundation of how you design learning isn’t built on research findings about effective learning, you’re wasting your company’s resources. practical
While academic research provides guidelines, it can be difficult to put lessons learned into practice in chaotic workplace learning environments. Make sure your design and implementation is not only desirable, but also achievable. workplace learning design
Don’t think courses are the solution. Start with the end goal in mind: your business goal. Work backwards from these to consider performance goals, key performance indicators, behaviors, and barriers to desired behaviors. Sometimes the solution is a course, but more often the problem needs to be addressed through communication, performance support, work assistance, or organizational change. 4. Work on your data storytelling skills Focus on storytelling
Use storytelling techniques to create compelling, data-backed narratives. In the future, there may come a time when AI will autonomously make decisions and perform tasks without human intervention. But now it looks like change will come through humans. You need to use data to persuade decision makers. everyone becomes an expert
Use critical thinking! One of the side effects of powerful tools powered by AI is that they “level the playing field” in terms of skills and experience. At least on the surface level. If you look at LinkedIn, everyone is an AI expert now. Because all of you (whether you understand it or not) have access to the answer. 5. Learn Agile Project Management Principles Apply Agile to Learning Design
Start using agile methods like rapid prototyping, iterative feedback, and sprints. Agile helps provide timely learning interventions and keeps content relevant. Build cross-functional collaboration skills
This may sound like one of those annual performance review clichés. In practice, this means that even L&D and HR bubbles will no longer be able to operate. You will need to work closely with HR, IT, and other departmental stakeholders to engage in cross-functional teamwork to coordinate broader business efforts and learning. Use agile tools
The tools are adopted and used by other departments that align with larger organizational goals and collaborate. Not every team needs their own project management tool.
In our next article, we’ll continue to explore five more action items and the pros and cons of autonomous agents in the workplace.
References:
[1] Visualization of leading large-scale language models (LLMs) ranked by performance using Massive Multitasks Language Understanding (MMLU), a benchmark for evaluating the capabilities of large-scale language models.
[2] OpenAI prepares to release AI agent
