It Has Already Changed
Coming back from speaking at DevLearn, one of the US’s largest tech-focused learning events, I’m still thinking about one of the audience’s 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. I think the audience knew the short answer already after four days of constant talking about AI and hands-on experience with technology demos in the expo hall. The short answer is “yes.”
Note that this article does not only refer to productive AI, which has exploded in the last two years. Consider a chart of major language models [1]: Gen AI is already part of the larger AI umbrella (including pure machine learning automation).
Your Role Has Already Changed
However, I answered on a different note: it’s not the AI that changes your role. Learning and Development’s (L&D’s) role has changed over time. I don’t see AI as a tool or technology but as an invisible force to accelerate that change. By 2025, these “powers” will appear as independent agents open to the public. These agents will not only help you in doing the jobs but they will do the jobs for you. Or, instead of you.
We are not using AI to transform our current L&D toolkit. We rethink everything we do as learning professionals to improve and stay relevant as a profession. AI will be like a thread in the future. It’s invisible, but it’s important for communicating with the applications we use. With independent agents coming out in 2025, they are taking over your jobs [2].
This article and my next one will cover how L&D roles may change and what learning professionals can do today to improve and remain relevant in the future.
How Much Will Your Role Change?
It depends on whether you are a flip phone or a smartphone today. Let me explain: how your role changes depends on what you do today. If your current role is similar to that of the fastest growing content creators ten years ago (answering phone), your role will change dramatically. In other words, if your activities mainly involve the creation of course content, change is exposed. AI will accelerate the transformation that has been happening for a while, with agents changing your speed literally 100x, producing the same thing you do today.
If the learning specialist works as a consultant (smartphone) focused on changing behavior and solving problems, where other solutions may involve lessons, then the change will be very small. However, you need a new strategy and a new mindset about which activities to prioritize to deliver value.
Should we throw away everything old? No. Have a plan for what to change, what to stop, and what to start new. Like computers or later the invention of the Internet, AI can be used both for good and for harm. Keep that in mind (along with data privacy, data security, and ethics) when building your strategy.
What Can You Do to Prepare Now?
To prepare for the future of L&D amid rapid advances in technology, AI, and the changing workplace, you can take a few forward-thinking steps. Many learning experts I spoke to believe there is a big difference between “playground” AI and workplace AI in terms of performance.
Playground AI is all that is available today in the community. Without any validation data (at least not yours), you can create a quick prototype and show amazing results without deep coding knowledge. However, when it comes to the workplace, the adoption rate is very slow.
Companies are still figuring out how to provide secure but innovative tools and resources and how to monitor data security and privacy. So, what can we do today professionally? Here are some takeaways from speaking with industry leaders today at the conference:
1. Invest in Teaching Data and Analytics Skills
- Learn the basics of data
Begin by developing a basic understanding of data collection, analysis, and interpretation. Familiarity with tools such as Excel, Google Analytics, or Power BI can be invaluable. - Use statistics to show the impact
Practice using data to demonstrate how learning impacts performance and aligns with organizational goals. Start by tracking key metrics such as engagement rates, skill development, and performance improvement. - Experiment with A/B testing
Try simple A/B tests with different learning interventions to understand what resonates with students and drives results, improve skills in test design and analysis.
2. Explore AI and Automation Tools
- Experiment with AI-powered platforms
Start exploring AI-powered learning platforms and content creation tools to understand how AI can support personalization and content curation. - Perform repetitive tasks
Use automation tools to streamline administrative processes such as tracking registrations, sending reminders, or collecting feedback, which will free up time for strategic plans. - Stay up-to-date on AI ethics
Familiarize yourself with ethical considerations surrounding AI, such as data privacy and algorithmic bias, to ensure fair and transparent learning solutions.
3. Use Evidence-Based but Effective Workplace Learning Design
- It is based on evidence
Use your limited resources wisely. If the foundations of how you design learning aren’t built on what research shows about effective learning, you’re wasting your company’s resources. - Useful
Research findings in higher education institutions can provide guidelines, but applying lessons learned in a dirty workplace can be challenging. Ensure that design and performance are not only desirable but feasible. - Workplace learning design
Don’t think the course is the solution. Start with the end in mind: the business goals. Work backwards from those to performance objectives, Key Performance Indicators, behaviors, and barriers to that desired behavior. Sometimes, the solution is a course, but often, the problem must be solved through communication, operational support, job resources, or organizational changes.
4. Work on Your Storytelling Skills
- Focus on storytelling
Use storytelling techniques to create compelling narratives backed by data. There may be a time in the future when AI will make decisions and perform tasks without human intervention. However, right now, change can happen through people. You will need to convince decision makers with data. - Everyone will be an expert
Use critical thinking! One of the side effects of powerful AI-driven tools is that they “level the playing field” in terms of skills and knowledge. At least at the top level. Just look at LinkedIn: everyone is an AI expert now. It’s because you all have access to the answers (whether you understand them or not).
5. Learn Agile Project Management Principles
- Use 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 a variety of collaborative skills
This may sound like one of those annual performance review sections. In fact, what you are saying is that you can no longer operate in an L&D or HR bubble. You must engage in cross-team collaboration by working closely with stakeholders in HR, IT, and other departments to align learning with broader business initiatives. - Use sharp tools
Find tools that align with your larger organization’s goals and are used by other departments you work with. Not every team needs their own flavor of project management tool.
In the next article, we will continue to explore five other elements of action and the pros and cons of independent agents in the workplace.
References:
[1] The visibility of massive language models (LLMs), measured in performance, using the MMLU (Massive Multitasks Language Understanding) benchmark to assess the capabilities of massive language models. [2] OpenAI is preparing for the release of an AI agentSource link
