The impact of AI on learning designers and the learning industry
Just recently, I finished a course on LinkedIn Learning on responsible AI (RAI), and the role it plays in the workplace. This triggered me to look further, so today I’m exploring some thoughts on the impact this could have on the market, on jobs, and the learning design industry.
We know the increasing integration of AI into the global economy is transforming the job market, creating new opportunities while simultaneously displacing some traditional roles.
This shift alone calls for upskilling and retraining the workforce, especially leaders.
This trend holds significant implications for the learning industry.
- AI is driving change at an unprecedented pace: The use of generative AI has almost doubled in the last six months, with 75% of global knowledge workers currently using it, highlighting the urgency for employees to acquire or improve their AI skills.
- Leaders recognise the importance of AI but struggle with implementation: While 79% of leaders believe AI adoption is crucial for competitiveness, 59% are concerned about quantifying AI’s productivity gains, leading to implementation delays.
- AI is not just for technical roles: Non-technical professionals like project managers, architects, and administrative assistants are increasingly seeking AI skills. Myself included.
So, we have this discrepancy between leaders agreeing GenAI can increase both the quality and the speed of work, yet have no idea on how to measure the gains. Fearing being left behind, employees want to use AI at work, and they won’t wait on leaders or organisations to catch up.
- Increased demand for AI training: With 76% of professionals believing AI skills are necessary for career competitiveness, the demand for training on AI tools like ChatGPT, Claude and Copilot will continue to rise. This presents a significant opportunity for learning designers to develop and deliver targeted training programs.
- Shift in skills emphasis: AI is good at automating routine tasks, yet uniquely human skills like management, relationship building, negotiation, and critical thinking will become more valuable. Learning should adapt programs to focus on cultivating these essential skills.
- Emergence of new roles: The rapid evolution of AI is leading to the creation of new roles like “Head of AI”, a position that has tripled in the past five years. (LinkedIn). Learning designers will play a crucial role in defining the skills and knowledge required for these emerging positions and designing training to prepare the workforce.
Looking ahead, I see a lot of work for learning to do. Think governance, compliance, fairness, digital resilience etc. Likely, another section in the Code of Conduct and Cyber Security by next year. That is, without mentioning any new training on tools, procedures, and likely roles that haven’t been invented yet.
What can you do to stay ahead?
- Embrace experimentation: Actively explore different AI tools and applications. This hands-on experience will provide valuable insights for designing effective learning programs. I’ve been using pretty much every major large language model (LLM) since they became publicly available and find great value in doing so even when they fail in providing a decent answer—at times, miserably. But that’s the soul of learning, right?
- Develop AI aptitude: Invest in upskilling yourself on AI tools and technologies. Leverage resources like LinkedIn Learning courses, which have seen a 160% increase in usage among non-technical professionals. As a learning experience designer (LXD), I find copywriting an unsung superpower in this field. To endure the time constraints and the drive for quality products, I have no trouble turning to AI and leveraging their capability to generate ideas in seconds. From there, I can tailor it to my audience and needs, making the content my own.
- Focus on the “why” and “how”: Help learners understand the strategic value of AI for their roles and the organisation as a whole. Develop training that goes beyond basic functionality and focuses on practical application, demonstrating how AI can drive growth, manage costs, and improve customer value. For instance, many GenAI apps will own the rights to what they produce; other times, they will unitedly yield IP-protected content, as if they were just made for you. Note, there are great apps for plagiarism too.
- Promote a culture of continuous learning: Encourage ongoing AI skill development within organisations. Design learning programs that are flexible and modular. With the speed of this technologies, I believe many of us will have to reinvent ourselves in shorter periods of time
- Stay informed: Keep abreast of the latest trends and developments in the AI landscape.
Understanding the implications of AI’s impact on employment and proactively adapting to the evolving needs of the workforce will empower us to play a crucial role in ensuring individuals and organisations thrive in an AI-powered world.
References:
- “AI at Work Is Here. Now Comes the Hard Part“, Microsoft, 8/05/2024
- OECD AI Principles overview – Adopted in May 2019, they set standards for AI that are practical and flexible enough to stand the test of time.
- The Reality of Responsible AI – Jeanne Kwong Bickford, Katharina Hefter, Steven Mills, and Tad Roselund, BCG
- The AI Index report, 7th ed. 2024, Measuring trends in AI
- Technology Trust Ethics Preparing the workforce for ethical, responsible, and trustworthy AI: C-suite perspectives (Deloitte)
- Cisco Principles for Responsible Artificial Intelligence
- McKinsey Quantum Black AI: Responsible AI (RAI) Principles
- KPMG Trusted AI
- IBM: What is AI governance?