The (AI) World Is Closing
The world is closing in
And did you ever think
That we could be so close like brothers?
The future’s in the air, I can feel it everywhere.
I’m blowing with the wind of change.
Wind Of Change: Navigating AI And L&D
It was around 1991 when the German band, Scorpions, “dropped” their best-selling single of all time: “Wind of Change.” From the collapse of the Soviet Union to the fall of the Berlin Wall, I remember a lot of wind, and a lot of change. But what does that have to do with Artificial Intelligence (AI) and L&D?
A Different Kind Of Wind: We Need A Bigger Boat
Today, I’m talking about a different kind of wind in a different era: the rise of Artificial Intelligence, and learning professionals in the workplace. The winds of change are blowing. AI is rapidly advancing and promises to transform how we inform, train, and develop talent. Just like with the real wind, to harness it, you need to understand how to work with it.
For learning professionals, AI brings both opportunities and disruptions. Those who understand AI and adapt proactively will thrive in the years ahead. Those who ignore it do so at their own peril. As the Scorpions said, the world is closing in. This article explores practical strategies to harness the wind and navigate your career in the age of AI, because it looks like we need a bigger boat.
TL;DR Of The Article
Scale the impact, not (only) the effort!
What does that mean? Today, it may take you a lot of effort to create a desired outcome. Don’t limit yourself to thinking how AI can scale your effort to make it easier to produce more of the same outcome. Think of AI in terms of scaling the outcome, not (only) the effort. Don’t just navigate to an island more easily, go and discover new places!
Navigating The AI And L&D Landscape: Four Key Impacts On Learning
While AI has been around for a long time, it was mostly used by engineers, developers, and techies. Today, it is available all around us. According to this report, there are about 58,000 AI companies in the world with over 115 million companies using it. Naturally, L&D won’t be able to ignore this worldwide wind. Personally, I can also confirm that one of the main topics of learning conference hallways (and packed halls) this year was navigating AI and L&D. The world is closing in.
Don’t think of AI as a technology to scale your current processes, as is! You don’t need to create content faster if the current content does not solve the business problem. Scale the impact, not (only) the effort. Use AI in new ways to solve problems, rather than to help with the old effort that didn’t make the desired impact. In other words, television is not about seeing people reading a book, like the radio was hearing it. It is a new paradigm.
Traditional Responsibilities Are Changing
The following components already exist today in L&D workplace learning, so naturally, we may get excited to see automation and AI support for them. With AI, however, they may shift focus.
1. Faster Content Creation
This is a common reaction. Yay, we can create content faster! AI can generate content and courseware far faster than any human. ChatGPT and tools like it can already create full learning modules from a simple prompt. Don’t get me wrong, when we do need content, this is great! Finally, maybe, we can stop creating mediocre courses that were supposed to be just checklists, communications, and information dissemination pieces. AI can do those for us now at scale. We can create more with shorter deadlines.
But, what if we used this energy to make less content and more impact? One of the reasons we have so much content already is that we use a course as an empty vessel. SMEs and stakeholders fill this vessel with the important stuff. What if we use AI to break this mental model and create many different types of learning resources instead of a single course? This would require collaboration between communications, training, talent development, talent acquisition, operations, etc. But at the end of the day, the target audience is the same employee, so why not?
2. Navigating AI In L&D Delivery And Facilitation
AI tutors and virtual facilitators will transform how we deliver training. They provide scalable, personalized, always-available instruction. This reduces the need for live instructors, trainers, and tutors. Does this last sentence sound harsh? This article suggests otherwise.
In May 2023, Intelligent.com surveyed 3,017 high-school and college students, as well as 3,234 parents of younger students, about study habits. The survey found that 10% of high-school and college students and 15% of school-aged children studied with ChatGPT over the last year. But most surprisingly, nearly all students who had been consulting a human tutor replaced at least some of their human-tutor sessions with ChatGPT, and 9 out of 10 students preferred ChatGPT over a human tutor.
3. Navigating AI In L&D Data Analytics
AI excels at gathering, analyzing, and acting on learning data. It provides insights on content effectiveness, knowledge gaps, and more. This data-driven approach reduces guesswork in L&D. Data and analytics (especially predictive analytics) are key to the future of learning.
However, we need to give up measuring the illusion of learning and start focusing on the outcome. The effect. The impact. Not just ROI but a more holistic impact of supporting growth. While we need to measure what matters at the workplace (effect of learning), we also need to level our communication and storytelling game. Outside of L&D, nobody cares about our internal lingo. It’s not the effort invested in the design or the learning itself, but the application of it that matters for them. The impact is what matters. Again, scale the impact, not the effort!
4. Navigating AI In L&D Learner Experience
AI promises hyper-personalized, immersive learning through simulations, VR, chatbots, and more. This new approach shifts control from L&D teams to the learner. And maybe, we can stop using the word “learner.” People have lives before, during, and after they are “learners.” Focusing on only the during part where they are learning often leads to ineffective applications on the job.
At workplace learning, we assume behavior changes after designing a course to change behavior. It’s time to give up the illusion of learning. There are a lot of other factors that enable or inhibit behavior change on the job. However, the fundamental challenge is to provide meaningful, authentic learning and practice.
“I learned a lot about the company, our organization, and our vision. But I’m not sure what I do.” , wrote an anonymous “learner” after onboarding. People want to know how to do their job, and how to do it well. They need skills practice to do it better, easier, faster, etc. AI, when used well, can provide true adaptive learning for individuals, while enabling the social element of learning when appropriate.
Got Wireless Wi-Fi?
I believe in a couple of years, “AI-powered” and “AI-driven” will become so ubiquitous that we can’t even imagine work without it. Like the word “wi-fi” today. Do you know what wi-fi stands for? It’s everywhere. We take it for granted. By the way, it stands for nothing. Even if “high-fidelity” (hi-fi) would make you think it’s “wireless fidelity”.
So, don’t fall in love with technology that is AI-driven today. Fall in love with the problem you’re solving for. For example, one of the favorite questions for LMS, LXP, and other learning platforms is this: how does your platform support meaningful feedback? And what evidence do you have in previous implementations that it works well?
The thing is, it’s not the answer that tells me about the quality of the platform. It’s how they think about this question. Do they understand what meaningful feedback is? To provide feedback, they need to assess people. How are they going about that? And if they just tell me learning data without any performance impact, I know they’re focusing on the illusion of learning. It may sell, but it won’t work.
How Can Humans Compete With AI?
Well, it is probably the wrong question. “How can humans harness AI?” is probably a better one. It is unlikely that AI will go away. In fact, what we’re experiencing today reminds me of the early days of the internet and the world wide web. It is just a transition into a new paradigm of work, life, and entertainment.
Some see the vision post-transition, some see the transition as the vision. So, don’t feel like you’re missing out if you haven’t mastered the latest prompt engineering techniques yet. This co-performing, co-creating world is changing superfast. Microsoft has just rolled out its Copilot in MS 365.
All AI Roads Lead To Productivity!
We often think of workplace productivity as how efficiently we produce the desired output. For example, how long would it take to build a 60-minute eLearning course. When we want to increase productivity, we can reduce the resources used (time, effort, money, energy, etc.) and/or increase the volume of outputs (creating two courses with the same effort). This single focus can backfire when navigating AI in L&D!
Back in the early days of the web, a prominent client asked me how long it would take to upload their PowerPoint deck to the web. It was an onboarding deck with about 60 slides. I said I could convert it into a PDF and upload it within ten minutes (through a very slow connection). The client was ecstatic! But then I asked: “Why would you want to do that?” Long story short, focus on both ends of the coin: efficiency and effectiveness. Using AI to produce something faster that did not solve a problem in the first place would be more efficient. But not more effective.
Final Thoughts: How To Prepare For The Changes?
The World Economic Forum has revised the top skills we need (please, take these with a healthy dose of suspicion). What are the top five skills?
- Thinking
- Thinking
- Attitude
- Attitude
- Mental model
Okay, just kidding. Somewhat. The top five skills involved analytical thinking, creative thinking, resilience (flexibility and agility), motivation (and self-awareness), and curiosity (and lifelong learning). There are a ton of courses on these online. But, before you run and take courses on these “skills,” I suggest something else:
- Analyze what you do in your job
Look at the tasks you complete, the output you create, the processes you use, the decisions you make, etc. Then take these “skills” from above and match them to your actual work. Create a matrix of how you apply these skills in your role. Then, you can find courses (and a lot of other resources) to grow your skills. Learning about doing something is not the same as learning how to do something.
Do We Need AI Literacy?
More and more courses pop up online on AI literacy. The premise is that you need to understand the fundamentals of how AI works in order to be successful in using it at work. It doesn’t mean you need to become a programmer. However, you may need to invest time to understand the key technologies behind AI. Knowing AI’s capabilities and limitations lets you spot opportunities. It also builds credibility with technical teammates.
Activities like taking online courses, reading articles, or volunteering for AI pilots are great ways to boost know-how. The challenge is the pace of change. You won’t keep pace with engineers, but learn enough to speak their language. Think of it as eventually achieving functional fluency, not native proficiency.
That said, personally, I would not jump from zero to one in AI literacy without having data literacy. I strongly suggest practicing data literacy first before you ask AI to lead, clean, analyze, summarize, and predict based on your raw data, so that when you ask AI to do this for you, at least you can check the answer’s validity.
Reimagine Your Role
Preparing for low-stakes task automation by AI, the role of a learning professional will shift to flying the plane rather than putting the plane together while flying. Or, going back to the wind of change metaphor, building the ship while navigating the sea. Now we just have to make sure we’re going in the right direction.
Rather than clinging to old job titles, reimagine your role. Find adjacent skills like design thinking, change management, and building partnerships. Develop expertise in an emerging practice like responsible AI implementation or multimodal learning. Evolve from doing tasks to solving problems and delivering outcomes. I wouldn’t be surprised that AI would force humans (as in HR) to work together more closely by merging the siloed efforts of L&D, talent development, organizational development, leadership development, talent acquisition, internal communications, content and knowledge management, etc.
Collaborate With AI
The most successful learning leaders will partner with AI, not compete against it. Setting AI assistants to handle repetitive work frees you up for strategy and innovation. Treat AI like any specialist teammate: understand its capabilities and align work. Give it clear guidelines and feedback to refine outputs. Co-create is the verb I often hear about this relationship. Of course, we want to feel superior to AI, so co-create sounds pretty safe. 🙂
In the early stages of co-creative work, an Instructional Designer can prompt AI to generate a course draft and then refine it by adding examples, tweaking activities, and ensuring accuracy. Together, they achieve far more than working separately. But I doubt it ends there. AI allows scaling, which brings volume and speed into our current workflow. Volume and speed mean complexity. Therefore, while our focus should be innovation, we shouldn’t forget that innovation does not always mean addition. Simplification and detraction can be as powerful! Again, scale the impact, not the effort.
Proactively seek out AI collaboration opportunities rather than waiting for change. Be the learning professional who pilots new uses for chatbots, AI recommendation engines, simulated environments, and more. That said, my advice stands: start with data literacy and have a clear strategy for measurement and evaluation. If you don’t know how to measure the impact of your work, how will you know what AI experiments worked better?
Champion People-First AI
Easier said than done. AI brings risks like bias, misuse, and displacement of human roles. As learning leaders, we must advocate ethical, socially responsible practices. This is not a policy question. It is becoming much bigger than just a standard procedure. Understanding the potential biases AI already inherited during training (that is training the model, not training people) coupled with human biases (for example, confirmation bias to seek out and accept only what we believe is true), at the very minimum we should be transparent about the consequences. Of course, this effort at navigating AI is not an L&D initiative, but L&D need to have a voice.
- Prioritize transparency
How does the AI make decisions? What data is it using? - Monitor for bias
Ensure diverse representation in data and testing. - Validate machine outputs
Don’t blindly trust AI recommendations. - Keep humans in the loop
AI should augment people, not replace them. - Upskill impacted workers
Provide training and career development for those displaced. - Focus AI on the hard stuff
Use it for dangerous tasks and tedious work, not everything.
These are some of the suggestions ChatGPT provided to deal with AI, by the way. I’m not sure ChatGPT has ever worked in a corporate setting! 🙂
The Winds Of Change
AI brings uncertainty but also enormous potential. Learning leaders who understand this technology and adapt their skills will remain essential in an AI-enabled world. While shiny things may grab your attention (such as generative AI), make sure to define the challenges and opportunities first, and then seek the solutions for them, rather than having a solution and then seeking a problem for it.
With human strengths like creativity and empathy complemented by AI’s capabilities, we can transform learning like never before. But we must take action today to ready ourselves. There is no reversing the winds of change blowing through our industry. But by being proactive, learning professionals can steer their careers in the right direction, towards a bright future ahead.
References:
- Bughin, J., E. Hazan, S. Ramaswamy, M. Chui, T. Allas, P. Dahlström, N. Henke, and M. Trench. 2017. “Artificial intelligence: The next digital frontier?” McKinsey Global Institute.
- Dellarocas, C., and D. M. Hanssens. 2018. “The power of social media analytics.” MIT Sloan Management Review 60 (1): 1.
- Wilson, H. J., P. R. Daugherty, and N. Morini-Bianzino. 2017. “The jobs that artificial intelligence will create.” MIT Sloan Management Review 58 (4): 14.
Originally published at www.linkedin.com.
