Will they wait for us?: L&D's big reckoning in the age of AI

Keeping up with the rate of AI innovation at this point is mostly a losing game. If you subscribe to even one AI newsletter, you're getting daily major technology updates.

A little over a month ago, Claude Design dropped. Amazing and still the worst it's ever going to be. Tim Slade posted a video demoing what he built on day 1 with limited exposure to the tool, and it was lightyears ahead of what we are building with our most "powerful" eLearning authoring platforms, such as Articulate Storyline, Articulate Rise, Adobe Captivate, or ELB's Lectora in the same amount of time.

The conversation is circling around production: How many designers will we actually need now? Do we need Storyline licenses anymore? Is SCORM dead?

All good questions, but perhaps, not the right questions.

Too few are asking what our role is in this evolving landscape. As Exhibit A, I offer Emily Greene's post about software engineer, Zara Zhang. Zhang uses AI-generated mini courses to teach herself and/or other developers about the code she generated with AI so that it can be updated at a later time:

And of course, Zhang is an innovator in her space, not the average user. But the less advanced user of gen AI is using it to "train" by asking questions and getting feedback instantly.

If our savvy users are now generating their own learning materials, will they wait for us to craft our "backed by Learning Science™" experiences and materials? Or are they just going to solve the damn problem so they can go home to their hobbies and get paid? (I think you know the answer.)

And if those solutions are working for them...

what do they need us for?

This is the reckoning because for many of us the answer to "what do they need us for?" is this: they don't. Not in our current state.

Sure, we can argue that many of them are just getting information, not training, and the user doesn't even know if it's accurate.

And yes, we can argue that it's probably more time consuming for them to wander into a long conversation with a LLM rather than get targeted information from a human-designed or human-led experience.

But learners don't care because they will use what's easiest to reach for and what feels quickest, even if it is not, because they feel the pressures of our work culture.

And remember, research repeatedly shows we are not good evaluators of our own learning, so they may believe they've gotten exactly the information they need and are now knowledgeable, even if they aren't.

Then, there's the SMEs. They are also turning to AI. We don't like waiting for SMEs. They don't like waiting for us. AI promises to remove the middle man and help them get back to their "real" job, rather than discussing and reviewing training materials.

We are at a crossroads that demands L&D evolve.

But evolving past what we know is more than just changing the work we do; it feels like changing who we are.

And that's scary because many of us have our identities heavily invested in the idea that we are good at making sense of information, that we're great designers, and that we're helpers.

I know I do.

Seeing the current landscape, it can feel like we have to become what we don't want to be: machine-focused over people-focused, cold and logical, the sociopathic CEO caricature.

But, to sum up the wisdom of Dave Ramsey (who I most certainly do not always agree with):

When presented with two stupid choices, find more choices.

Desperation makes us dumb.

More Choices

As a humanist, I believe we are an amazing species. We are built for collaboration and evolved to be able to tell and make sense of stories to support that. We are emotional. We are reflective. We learn in ways other species do not.

So start there. Start with what we know.

Our work can be come any or all of these:

• experience designers who make holistic use of the entire learn + work ecosystem to deliver high-impact experiences (and that doesn't just mean tying your LMS + HRIS together for improved performance data, but actually considering how people are being emotionally, mentally, and physically affected by what you design)

• storytellers who can drive motivation and can wrap data in meaningful context

• trainers of AI "employees," not just human ones

• facilitators who can referee contact zones, guide people through meaningful action, and support collaborative ideation

• strategists who spot the gaps in human and machine performance and collaboration and craft executable plans for continuous improvement

• context engineers who excel at qualitative research, active listening, and framing of contexts to support governance, maintenance, and use of agentic AI

• coaches who help people construct goals, identify gaps, and strengthen capabilities

• people leaders who model good learning habits, continuous improvement, and build trust

Together, these roles create spaces where humans are amplifying their potential through the use of AI. These roles are not entirely new, but the ways of working will be because they operate through the lens of being and supporting AI-wielding humans.

Again, I want to be clear that our knowledge of learning science and our skills in design are still assets. The old ways of working won't disappear, but our value in the workforce comes from shifting to incorporate those old ways of working as foundational expertise, rather than our primary purpose.

Start with the Power of People

Most technology--especially AI-- designed for a system that improves efficiency, and as the old African proverb starts:

If you want to go fast, go alone.

The belief system embedded in our technology is that people slow you down (a truth my good friend, Rocío Granela, helped me see). Isolation allows you speed. Isolation allows you control. If you can ask a machine and wait for no one, you can do more in less time.

But that's not quite the whole truth. The second half of that proverb is where we find meaning:

If you want to go far, go together.

Building in isolation leaves you prone to biases. It leaves you solving problems without collective wisdom our species evolved to share--ultimately slowing you down as you try to reinvent the wheel. It leaves you vulnerable to your own shortcomings.

Together is the way forward.

It's going to take collective learning and working to ideate better systems--as IDs and within the other sectors of work.

AI can help us close certain gaps. AI can make us faster. AI can help us scale. But AI isn't going to solve the problems of our world. We're going to do that, but only if we embrace being human in the age of AI. We cannot get stuck on job titles or ways of working.

It's time to engineer the future we want.