By Caelan Huntress
Learning and development professionals are familiar with change. New technologies, shifting organisational priorities, and rising expectations have always been part of the job. But the current wave of generative AI, automation, and digital tools feels like a different kind of change. It's not because AI is replacing our work as humans, but because it is forcing us to rethink what human work is for.
While wrestling with this existential question, manyL&D teams are being asked to do more with less: less time, tighter budgets, and an ever-expanding set of tools to evaluate, implement, and support. The pressure is increasing. But that means the opportunity is increasing, too.
As organisations navigate emerging technologies, the most important question is no longer “How do we use AI?” but “What should humans focus their attention on, now that AI can do the robotic work?”
Generative AI has reached a point where it can draft your learning content, summarise research for you, generate assessments, and analyse feedback faster than any human. For many aspects of knowledge work, AI can complete projects in minutes rather than days.
But faster output does not automatically translate into better learning.
More content≠ better clarity. More tools ≠ better decisions. If humans are still responsible for sense-making, judgment, and accountability, we don't need more. We need a different way to get to better.
For L&D professionals, this creates a paradox. AI promises efficiency, yet many teams report spending more time reviewing, editing, and managing outputs, rather than less. The problem isn’t with the technology. It’s how humans put their effort into it.
Many AI adoption strategies begin with automation: What can we speed up? What can we reduce? What can we generate at scale? These are valid questions, but they are incomplete.
A human-first approach begins elsewhere. It asks:
AI is excellent at execution. But a human should guide that execution, using creative taste - something AI does not have (yet).
Once routine execution becomes a commodity, its value drops to zero. What remains valuable is all the work that cannot be automated without a loss in quality: things that require discernment, empathy, interpretation, and choice.
Finding the uniquely human work is the task of this decade.
Historically, L&D roles combined thinking and doing in the same workflow. You might research a topic, design learning materials, draft content, refine it, and publish—all as part of one continuous process.
Generative AI disrupts this by excelling at the doing work (drafting, generating, remixing) while it struggles with the thinking work (deciding what matters, what to exclude, and why).
When these modes blur, friction appears. People feel overwhelmed by volume, dissatisfied with quality, and uncertain about where their expertise fits. They have AI generate a whole lot of slop they have to sift through, and it takes longer than doing it by hand.
Separating these modes of thinking and doing is one of the most effective ways to reclaim clarity.
One practical way to make this separation visible is to map learning work to a simple five step creative cycle:
AI can support every step of this cycle, but it should not lead every step.
In practice:
When L&D professionals consciously decide where to engage AI and where to stay human, work becomes lighter rather than heavier. Use AI to help you craft, collect and publish - but sort and polish on your own.
As AI lowers the cost of producing content, the scarce skill becomes discernment: the ability to identify what is worth keeping, to eliminate what should be discarded, and to recognise what needs human need out of a learning experience.
This shows up everywhere in learning work:
These are not technical skills in using AI tools. They are human soft skills, and they cannot be delegated to a machine.
One of the most useful shifts for L&D professionals is to stop treating AI as a tool that must get things “right” on the first try, and instead treat it as a teammate that improves with feedback.
High-performing practitioners don’t simply accept AI output. They will:
This coaching relationship between a human and an AI counterpart mirrors how learning professionals already work with other people. The difference is that AI can generate options instantly, leaving humans free to focus on evaluation, rather than production.
Another emerging practice is parallel prompting: asking the same question across multiple AI systems to surface different perspectives, strengths, and limitations.
For L&D teams, this can:
The goal isn’t to find the answer, but to create better conditions for choosing the right one.
When used thoughtfully, AI can return one of the most precious resources in learning work: attention.
By offloading repetitive drafting, formatting, and synthesis tasks, L&D professionals can redirect their energy toward:
This is where learning has always created its deepest impact.
Emerging technologies are not slowing down. The question is not whether AI will shape learning, but how intentionally we shape our relationship with it.
A human-first approach does not reject automation. It reframes the work into categories where AI excels, and where humans excel.
The future of learning doesn't depend on us mastering every new tool. We need to strengthen the uniquely human capabilities that AI tools cannot replace.
For L&D professionals navigating this moment, the opportunity is clear: redesign your workflows around what matters most, partner with AI where it adds leverage, and protect the human work that gives learning its meaning.
AITD offers several courses on both AI and eLearning:
AI Essentials for L&D Professionals: Do you want to transform your L&D workflow and enhance learner experiences using Generative AI? In this blended learning course, you’ll learn how to use Generative (Gen) AI for a range of key L&D functions, including skills gap analysis, content creation, personalised learning and feedback. Register now.
eLearning: Foundations: This is Part I of an engaging, social learning suite of courses that provides you with access to learning experiences, activities and a comprehensive knowledge base. Register now. You may also be interested in eLearning: Planning and Design; and eLearning: Production and Delivery.
Caelan Huntress is the Founder of the AiCoaching Academy, a community for ambitious professionals who want to practice using the tools of the future. He is an American immigrant to New Zealand, and spent ten years running an online business while traveling the world with his young family. With 25 years of experience in storytelling, first as a theatrical performer, and then as a marketing automation strategist, Caelan helps people learn how to use new technology to tell stories that matter, by finding the uniquely human work that robots cannot do. His AI TrainingWorkshops are fast-paced and full of interactive games, reflecting his core value that learning should be fun. For more information, visit: https://caelanhuntress.com/