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Session 4: Future of Jobs & Skills With Lucy Chen
April 2nd, 2026
We asked 40 learning professionals to be honest with us, and they were.
On April 2nd, the AI Forum for Learning Professionals gathered for its latest session: a live, crowdsourced mapping of the jobs, skills, and fears shaping our field right now. Lucy Chen of Handshake co-designed the session with me and ran live AI analysis during the event. The format was simple. Everyone jumped into an anonymous Mural board. No slides. No speaker. Just stickies, perspective codes, and a room full of people who were ready to think together.
We came away with over 130 contributions across four zones. Here is what the room had to say.
We started with the hard stuff
Most sessions like this begin with the optimistic framing. We went the other way. The first zone asked: What threatens the future of our work? And where do you see new possibilities opening up?
The threats were not surprising, but they were specific. The fear of job loss. Fewer roles. The worry that AI-generated content is becoming the default and that quality is collapsing as a result. One sticky that collected a pile of votes read: “You get left in the dust if you are not using these tools every day. Hard to know what the signal is, versus noise.” Another: “No one wanting to learn anything and just using AI to do things for them. Lack of critical thinking skills.”
That second one sparked a real conversation. People talked about newer workers entering the field and how they would build foundational skills if the grunt work disappears. But then the room pivoted. Someone pointed out that those same newer workers bring creative perspectives we have never really capitalized on. Maybe, freed from rote production tasks, their energy goes somewhere more interesting.
What surprised me is how evenly the board split. The threats side and the opportunities side had nearly the same number of stickies. And as the conversation developed, people started noticing that almost every opportunity had a threat tethered to it. AI enables faster production and richer personalization, but it also makes it dangerously easy to ship content without understanding the “why” behind the tools you are using. Speed without expertise produces slop. The group kept coming back to this idea of maintaining your “first brain,” the foundational knowledge you need to validate what the AI gives you, so it does not fill your courses with confident-sounding nonsense.
People were honest about the fear and equally clear about where they see possibility: faster iterations, adaptive learning at scale, AI-powered simulations, richer feedback loops. One person wrote that L&D can be the group that brings humans together for connection in a way AI cannot. I thought that was exactly right.
The AI Slop Content Janitor
Zone 2 asked people to map evolving roles and titles. Some of the contributions tracked with what you would expect: AI Enablement Lead, Learning Engineer, AI Workflow Architect. But others were more interesting for what they revealed about the current moment.
Someone posted “AI Slop Content Janitor,” and the room lit up. It got more reactions than almost anything else on the board. People laughed. Then they stopped laughing, because it turned out to be a real job posting someone had seen. A role where a human cleans up after the AI and makes the outputs more human again.
That tension ran through the entire roles discussion. On one side, new titles with “AI” as a prefix: AI readiness manager, AI-first learning designer, AI/human learning strategist. On the other, an observation from one participant that stuck with me: “The word ‘Builder’ seems to be at the core of new AI-centric roles.” Full Stack LXD. Learning Engineer. AI Agent Trainer. The learning professionals who make things, not just design documents.
But here is the thing the room also grappled with: these AI-prefixed titles may not last. The group sensed that AI proficiency is becoming table stakes, a baseline expectation baked into every role rather than a specialty you build a title around. The same way nobody puts “internet” in their job title anymore. If that is true, the prefix collapses and what remains is the work itself: building, facilitating, connecting, evaluating.
The group also mapped roles they believe will hold steady: Teacher. Workshop Facilitator. Sales Enablement. Customer Education. The relational roles. The ones where human judgment, facilitation, and organizational context are the product.
The skills map, and where the circles overlap
Zone 3 was the centerpiece: a three-column skills map covering Mindset, Technical Skills, and Durable Skills. This is where the board got dense.
Under Technical Skills, contributions ranged from granular (“use HeyGen to generate a voiceover”) to architectural (“create an agentic workflow for generating a storyboard”). Prompt engineering appeared, but so did context engineering, intent engineering, and one that caught everyone’s attention: evals. The ability to evaluate AI outputs is emerging as a technical discipline in its own right.
Under Durable Skills, critical thinking dominated. It showed up more than any other single skill, with multiple stickies and the most votes. Collaboration, decision-making, needs analysis, and giving and receiving feedback clustered close behind. One participant added “apply expertise to check AI output,” which landed in the overlap between durable and technical. You need the domain knowledge to spot what the AI gets wrong. That is not a technical skill alone and it is not a mindset alone. It lives in the intersection.
This connected to one of the most interesting threads of the live conversation: the value of friction in learning. Real learning is a biological process. It requires failure and mistakes. It requires struggle. And as AI removes more and more of the administrative friction around content creation and assessment, the group argued that L&D’s evolving role is to strategically insert the productive friction that deep learning still requires. Strip away the busywork, yes. But protect the struggle that builds competence. That distinction felt like one of the sharpest insights of the session.
And then there was Mindset. I expected practical entries: ruthless prioritization, experiment early and often, comfort with ambiguity. Those showed up. But so did something I did not expect. Alongside delegation and professional judgment, people wrote: resilience, tolerance, confidence, mindfulness, motivation. And one person added joyfulness.
That word stopped me. In a session about the future of work and the anxieties of AI, someone planted a flag for joy. Not as a platitude. As a skill. As a choice. As something you practice alongside your prompt engineering and your critical thinking. I think that person was onto something.
When you step back and look at all three columns together, the boundaries blur. Critical thinking, curiosity, and adaptability showed up in every category. Delegation appeared in both mindset and durable skills. Quality discernment crossed all three. The diagram below maps the themes to where they sit, and more importantly, where they overlap.
The center of that diagram is where the future lives. Not in any single skill, but in the ability to move fluidly between how you think, what you can do, and who you are when the tools change again.
What we left with
Forty people showed up on a Thursday afternoon and built something together in 75 minutes. The Mural board is a snapshot of a profession in motion. It is messy and honest and occasionally funny, and it captures a moment that I think is worth paying attention to.
The room was not panicking. The room was adapting. Naming the hard things, then getting to work on what comes next.
Lucy ran the board through Claude during the session and surfaced patterns in real time as people were still adding stickies. That layer of live synthesis turned a collaborative exercise into something closer to collective intelligence. It also modeled, in the room, the exact kind of human-AI partnership the board was describing.
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