It Has Already Changed
Returning from speaking at DevLearn, one of the biggest technology-oriented US learning events, I am still reflecting on one of the audience questions:
[paraphrased] Is Artificial Intelligence (AI) going to change our roles? How?
In the context of automation and AI, the question was both about what to expect and how to prepare. I think the audience knew the short answer already after four days of constant talk of AI and hands-on experience with tech demos in the expo hall. The short answer is “yes.”
Note that this article does not only refer to generative AI, which has exploded in the last two years. Consider a chart on major Large Language Models [1]: gen AI is already a part of the larger AI umbrella (including plain machine learning automation).
Your Role Has Already Changed
However, I responded on a slightly different note: it’s not AI that is changing your role. Learning and Development’s (L&D’s) role has been changing for a long time. I see AI not as a tool or technology but as an invisible energy to accelerate that change. In 2025, this “energy” will manifest as autonomous agents open to the public. These agents will not only assist you in doing tasks but will do the tasks for you. Or, instead of you.
We’re not adopting AI to evolve our current L&D tool kit. We’re reconsidering everything we do as learning professionals to evolve and stay relevant as a profession. AI will be like wireless in the future. Invisible, yet fundamental to communication and the apps we use. With autonomous agents coming out in 2025, they are literally taking over your tasks [2].
This article and my next one will cover how L&D roles might be changing 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 are a flip phone or smartphone today. Let me explain: how your role changes depends on what you do today. If your current role is similar to that of rapid content creators a decade ago (flip phone), your role will change dramatically. In other words, if your tasks mainly involve content creation for courses, the change is exponential. AI will accelerate the change that’s been happening for a while, with agents literally 100x-ing your speed, producing the same thing you do today.
If you’re a learning professional acting more like a consultant (smartphone) focusing on behavior change and problem-solving, where some solutions may involve courses, then the change will be less dramatic. Still, you need a new strategy and a new mindset about what tasks you should prioritize to bring value.
Should we throw out everything that is old? No. Be strategic about what to change, what to stop, and what new to start. Just like computers or later the invention of the internet, AI can be used both for positive and destructive things. Keep that in mind (along with data privacy, data security, and ethics) while building your strategy.
What Can You Do To Prepare Now?
To prepare for the future of L&D amid rapid advancements in technology, AI, and changing workplace dynamics, you can take several proactive steps. Many of the learning professionals I talked to believe that there is a vast difference between “playground” AI and workplace AI in terms of implementation.
Playground AI is everything available today for the public. Without any propriety data (at least not your own), you can build a quick prototype and show amazing results without any deep knowledge of coding. However, when it comes to the workplace, it is a much slower adoption rate.
Companies are still figuring out how to provide safe but innovative tools and resources and how to guard data security and privacy. So, what can we do today professionally? Here are some of the takeaways from talking to industry leaders about this approach today at the conference:
1. Invest In Data Literacy And Analytics Skills
- Learn data fundamentals
Start by building a foundational understanding of data collection, analysis, and interpretation. Familiarity with tools like Excel, Google Analytics, or Power BI can be invaluable. - Use analytics to demonstrate impact
Practice using data to show how learning impacts performance and aligns with organizational goals. Begin with tracking key metrics like engagement rates, skill development, and performance improvements. - Experiment with A/B testing
Try simple A/B testing with different learning interventions to understand what resonates with learners and drives outcomes, honing skills in experimental design and analysis.
2. Explore AI And Automation Tools
- Experiment with AI-powered platforms
Begin 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, or gathering feedback, which will free up time for strategic initiatives. - Stay informed on AI ethics
Familiarize yourself with ethical considerations around AI, such as data privacy and algorithmic bias, to ensure fair and transparent learning solutions.
3. Use Evidence-Based But Practical Workplace Learning Design
- Evidence-based
Use your limited resources wisely. If the foundations of how you design learning do not build on what research shows about effective learning, you are wasting your company’s resources. - Practical
Research findings in academia can provide you with guidelines, but implementing the lessons learned within a messy workplace learning environment can be challenging. Make sure the design and implementation are not only desirable but feasible. - Workplace learning design
Do not assume that a course is the solution. Start with the end in mind: the business goals. Work backward from those to performance goals, Key Performance Indicators, behaviors, and barriers to those desired behaviors. Sometimes, the solution is a course, but often, the problem has to be addressed through communication, performance support, job aids, or organizational changes.
4. Work On Your Data Storytelling Skills
- Focus on storytelling
Use storytelling techniques to create compelling narratives supported by data. There may be a time in the future when AI will autonomously make decisions and execute tasks without any human intervention. However, right now, change is likely to happen through humans. You’ll need to convince decision-makers with data. - Everyone will be an expert
Use critical thinking! One of the side-effects of powerful tools driven by AI is that they “level the playing field” in terms of skills and experience. At least on the surface level. Just look at LinkedIn: everyone is an AI expert now. It’s because you all have access to answers (whether you understand them or not).
5. Learn Agile Project Management Principles
- Apply Agile in learning design
Start using Agile methods such as rapid prototyping, iterative feedback, and sprints. Agile helps deliver timely learning interventions and ensures content remains relevant. - Build cross-functional collaboration skills
This may sound like one of those annual performance review cliches. In reality, what it means is that you can no longer operate in the L&D or even HR bubble. You have to do cross-functional teamwork by working closely with stakeholders in HR, IT, and other departments to align learning with broader business initiatives. - Use agile tools
Adopt tools that are aligned with your larger organization goals and used by other departments you work with. Not every team needs its own flavor of project management tool.
In the next article, we will continue to explore five more action items and the pros and cons of autonomous agents in the workplace.
References:
[1] A visualisation of major large-language models (LLMs), ranked by performance, using MMLU (Massive Multitasks Language Understanding) a benchmark for evaluating the capabilities of large language models.
[2] OpenAI readies AI agent release
