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Session 6: Making Sense of Skills Survey Data
July 1st, 2026
“A lot’s changing under our feet. Some of us are pushing that change forward. Some of us are grappling with that change. Some of us are holding on with their fingernails.”
That’s how James opened our most recent AI Forum, and it set the tone for what turned out to be one of the most honest conversations we’ve hosted. The occasion: the results of the JLX Learning Professionals Future Skills Survey are in. 197 learning professionals across six world regions told us which skills they believe will matter most over the next three years, and where their organizations are falling short.
If you work anywhere near the future of work, upskilling, or AI adoption, and at this point that’s everyone, the findings will feel familiar. Uncomfortably familiar.
The findings: three tiers, one gut punch
Respondents rated nine skills on importance for the next three years. They didn’t produce a single winner. They produced three clear tiers.
At the top, statistically inseparable: learning strategy and design thinking (4.48 out of 5), AI tools and automation (4.46), and stakeholder influence and strategic communication (4.39). Two durable human skills and one technical one, sitting together at the summit.
At the bottom sits the profession’s traditional production core: learning technology and LMS platforms, facilitation and delivery, content development. The skills the field built its identity on ranked last in its own three-year priorities.
James didn’t dress it up: “If you start to think about the 20, 40 years that we’ve all spent on those things and how little they’re ranked as compared to all of the other ones, I do think that that’s pretty important.”
The storytelling gap (and the goosebumps metric)
The single largest disconnect in the dataset: storytelling and narrative design. Practitioners rated it 0.60 points higher for themselves than they believe their employers value it. Half of respondents rated it higher for themselves; fewer than one in ten rated it higher for their employer.
One attendee nailed the diagnosis in the chat: “Storytelling takes time and employers see that as costly. Learning professionals understand that humans learn best through stories.”
James offered a fix that got the room laughing, then thinking: “If we started adding how many times you get goosebumps to a learning experience, and that got it onto dashboards, then maybe we’d start to see ROI of storytelling.”
Goosebumps per learning experience. Someone build that dashboard.
On the workshop board, one sticky got to the root of it: “Finding a shared definition will unlock the power of storytelling for both sides.”
Employers don’t undervalue storytelling because they’ve weighed it and passed. They undervalue it because nobody has shown them how to value it.
The one skill employers want more than we do
Flip the storytelling gap and you get data analysis and reporting, the only skill where perceived employer expectations exceed practitioner priorities.
As James put it: “Learning designers don’t want to do a whole bunch of data analysis and reporting, but they understand that their employers need it and want it.”
The report’s read on this is sharper: data outputs are legible to stakeholders who lack domain expertise. Dashboards get budget conversations. Goosebumps don’t. Yet.
“Go figure, with AI we don’t have enough time”
Time was named as a barrier to skill development 141 times across 197 responses. The chat had the best summary of the whole event: “Go figure, with AI we don’t have enough time.” Another attendee gave it a name that deserves to trend: “AI dissonance.”
The report breaks “time” into three structurally different problems: workload displacement, organizational indifference, and personal life constraints.
One respondent wrote about “the tyranny of the urgent.” Another, more bluntly: “I feel like I’m being set up to fail.”
And still, in the same chat thread, this: “It takes so much time to learn how to be effective with AI and it changes almost daily. But I also built a whole training yesterday using AI… it took a few hours rather than weeks.”
Both things are true. That’s the AI paradox every worker is living right now, in learning and far beyond it.
Then the room pushed back
This is the part a report can’t give you. We moved into a shared Mural board and asked the group to validate, challenge, and poke holes.
The best challenge landed fast: is AI even a “technical skill,” or a different class entirely? “It’s like saying the internet is the tool,” one participant offered. “It is a different class of itself. And we are still figuring that part out.”
The stickies sharpened it further. One read: “AI can do 90% of the work. Learning professionals have to do the 10% that AI can’t do. We provide the human in the loop.” Others flagged what the survey missed, naming “ability to learn” and “flexibility” as skills that belong on the list, and asking whether a field made up largely of women carries an unequal share of the family obligations hiding inside the “time” barrier.
Another attendee described tool overload with precision: newcomers see “what seems like 5,000 different things,” while the people actually using AI four or five hours a day have anchored on three or four tools and given themselves permission to ignore the rest. Expertise, it turns out, looks like ignoring things.
Then came the quote of the day. One participant described asking her IT director for access to new systems: “I feel like I’m a little kid asking to use dad’s power tools.” She went from one of the least technical people at her company to a go-to AI expert. Silos broke down. “Suddenly they’re willing to do all kinds of things for me that they weren’t before.”
And the counterweight, from a participant who is the only person at her workplace using AI beyond making pictures: “It just feels like a chasm with no echo.”
To which someone in the room replied, simply: “Ya found the tribe here!”
The job titles are already mutating
The workshop board carries over from an earlier forum, and one zone asked participants to list roles they’re seeing, hiring for, or aspiring to. Reading it feels like a job board from 2028: AI enablement lead. AI workflow architect. AI agent trainer. Learning engineer. Human learning specialist. One participant spotted the pattern: “The word Builder seems to be at the core of new AI centric roles.”
The sticky that drew the most reactions? “AI slop content janitor.” Half joke, half job description. Somebody has to clean up the flood of machine-generated content, and learning professionals may be first in line.
The column of roles that will stay the same reads as its own quiet argument: teacher. Professor. Workshop facilitator. The human-connection jobs aren’t going anywhere.
“People have answers. Nobody has the answer.”
Jason and James went deeper on all of this in Episode 24 of The Learning Experience Ops Show, and it’s worth your next commute.
One number from that conversation stands out. 44% of respondents named something AI-related as the single most important competency for the next three years. Sounds like a headline. It’s an umbrella. Underneath it, the research team found five distinct orientations, from practitioners building agentic workflows at scale to practitioners actively resisting. Some responses held both at once. As James described one: “This has transformed my ability to do stuff… in a way that I’ve imagined for my whole life, and I hate it.”
And for anyone feeling the pressure to “learn AI” right now, James offered a memory worth keeping: “In 1995, I was in college. People were saying you must learn HTML. Sounded right at the time because everyone was saying it. And I did it. How much did I really use that?”
Everyone is being told to learn AI. AI is hundreds of different skills. Nobody can tell you exactly which ones. That’s why the data matters.
A Rosetta Stone for learning professionals
We closed on our hypothesis: cross-sector collaboration matters more now than ever. K-12, higher ed, corporate learning, workforce development, customer education. These worlds barely talk to each other, and right now they’re all solving the same problem separately.
One participant proposed the idea we can’t stop thinking about: “Let’s build the Rosetta Stone for learning professionals.” A shared piece of work that translates the terms blocking us, so the field can tell its story across sectors and finally articulate its own value. It collected more hearts than any other sticky on the board, sitting right beside the barriers it answers: “not as much of a common vocabulary” and “I don’t know where to find my people.”
We’re in. The report’s ultimate goal is a cross-sector skills roadmap, built from practitioner data. It needs builders.
Keep going with us
Download the full JLX Learning Professionals Future Skills Report
Join the AI Forum Slack community (160+ learning professionals figuring this out together)
Watch Jason and James unpack the full report on The Learning Experience Ops Show, Episode 24
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One question to leave you with, whatever field you’re in: if the skills that built your career ranked last on your own list, what would you start learning tomorrow?
The AI Forum for Learning Professionals meets monthly. These sessions are not recorded so participants can speak freely. If you want to be in the room for the next one, you can join us on Luma. And if the conversations between sessions matter to you as much as the sessions themselves, join us in the AI Forum Slack community.