My typical morning routine looks something like this: wake up, take the boy (dog) for a walk, exercise, shower, eat breakfast, then drink coffee while reading the news for about an hour.

The news I read starts with regular US and world news, then veers toward a very AI-heavy mix of tech and innovation stuff. And…as you would probably expect, a lot of the latter points to AI-related job “changes”, losses, and paradigm shifts.

That being said, rarely (as in almost never) does anyone talk or write about the fact/idea that MANY of the jobs that companies are shedding aren’t fully understood. Sure, a company may know what they hire a marketing specialist for. But do they reeeeallly know what that person does? No, they do not.

Hence, as news of studies, funds, and government initiatives meant to stem AI-related job losses, I think design-led skills/frameworks, like Human-Centered Design and Jobs-to-be-Done, may become more important than ever, as they force us to stop thinking in terms of job titles and start looking at the deeper human systems underneath the work itself.

Read on below…

— Justin Lokitz

Last week, OpenAI announced the “Economic Futures in the Age of AI” initiative, wherein the OpenAI Foundation is committing an initial $250M to building secure and abundant economic futures. On the surface, this is a big deal. Here, one of the household names of the future/frontier AI model companies/prognosticators of doom (depending on where you sit) has announced that it wants to give back to “society” as a way of mitigating the “huge economic changes [AI has] as it makes previously scarce capabilities far more widely available, and there is deep uncertainty about how far and how fast they will go.”

This may feel good-ish to many. Like the myriad other foundations run by giant tech companies, OpenAI is attempting (or at least purporting) to do some good in the world. However, underlying this is the not-so-good-sounding headline: AI will create real economic disruption. Not theoretical disruption. Not “someday” disruption. Actual labor displacement, organizational instability, and shifts in how value gets created and distributed.

By now, this is not really news to anyone, certainly not those whose jobs are already being affected by AI. That being said, what struck me most was the realization that most companies, OpenAI included, are still approaching AI transformation without a clear understanding of the work they’re trying to transform in the first place.

That. Is. Seriously. F-ing. Dangerous.

Upon scouring the (mostly tech) news from the last year, it would seem that most organizations do not actually understand how work happens inside their systems. They understand org charts, titles, departments, reporting structures, and KPIs. Alas, that’s not the same thing as understanding The Work.

They often cannot clearly explain why employees make certain decisions, where trust gets built or lost, what customers are really trying to accomplish, or which parts of a workflow create disproportionate value versus administrative noise. And now many of those same organizations are rushing to automate workflows they barely understand.

And…if that’s not enough, in many cases, companies are supplanting employees with mostly autonomous agents, which is akin to hiring a bunch of inexperienced interns to do the work of very experienced employees, who know when to ask questions before moving something forward.

This, my friends, is where Human-Centered Design (HCD) and Jobs-to-be-Done (JTBD) become critically important, not just for innovation, but for economic resilience.

One of the biggest mistakes companies are making right now is thinking about AI disruption at the level of roles instead of tasks, motivations, and systems. “Marketing manager,” “designer,” “project manager,” or “researcher” are not atomic units of work. They’re bundles of decisions, judgments, emotional labor, coordination, pattern recognition, communication, and procedural execution.

And…AI does not disrupt all of those things equally. Rather, it is exceptionally good at repetitive cognition, summarization, synthesis, formatting, translation, coordination overhead, and low-context production work. It is far less reliable when navigating ambiguity, building trust, understanding social nuance, managing organizational politics, or making judgment calls under uncertainty.

The problem is that many companies still treat work as a visible process map instead of a human system. They automate what is easiest to measure rather than what is most valuable.

You can already see this happening everywhere. Hospitals optimize scheduling efficiency while unintentionally destroying patient sleep and recovery (which I have some recent experience in). Customer support systems reduce ticket handling time while making customers feel trapped inside automated dead ends. AI-generated interfaces become faster to produce but less coherent over time because nobody is maintaining the underlying behavioral consistency of the product.

When organizations skip Human-Centered Design, they tend to optimize surface-level efficiency while degrading the actual experience underneath!

That matters because the future of work will not simply be determined by what AI can do. It will be determined by whether organizations understand what humans are actually trying to accomplish inside systems. And…this is exactly why Jobs-to-be-Done becomes such a powerful lens right now.

JTBD shifts the conversation away from “What does this employee do?” toward “What progress is someone trying to make? And…what decisions must they make along the way (that will affect progress in some measurable way)?”

That may sound subtle, but it completely changes how you evaluate disruption.

A recruiter is not simply screening resumes. They are reducing hiring risk, interpreting ambiguous signals, building confidence among stakeholders, and helping organizations make decisions under uncertainty.

A teacher is not just delivering information. They are sustaining motivation, diagnosing misunderstanding, creating accountability, and helping students persist long enough to learn something difficult.

A designer is not merely creating screens or wireframes. They are shaping trust, reducing cognitive friction, orchestrating decisions, and helping products maintain coherence across increasingly fragmented systems.

Once you start looking at work this way, you can see which parts of a role are highly automatable and which parts become more valuable after automation enters the picture. That distinction matters because AI rarely replaces entire jobs cleanly. What it actually does is redistribute cognitive leverage.

The procedural layers shrink first. The coordination layers change next. The judgment layers often become more important. This is also where people can regain agency instead of simply waiting to be disrupted.

Organizations using HCD and JTBD properly can redesign work instead of just eliminating it. They can identify where employees create unique human value and reposition them around higher-leverage contributions. They can surface latent expertise that was previously buried underneath administrative work. They can retrain around systems thinking, judgment, trust-building, and orchestration instead of repetitive execution.

That is a very different future than simply treating AI adoption as a headcount reduction exercise. It also changes who becomes valuable inside organizations.

People whose expertise is primarily procedural are more exposed. People who understand systems, customer motivations, organizational dynamics, synthesis, framing, communication, and decision-making are likely to gain leverage.

This is one reason I believe designers, researchers, strategists, facilitators, and systems thinkers may will end up playing a much larger role over the next decade than many people currently expect.

For years, design was often reduced to deliverables. Screens. Interfaces. Outputs. Artifacts. But…increasingly, the real value of design is not execution. AI is rapidly commoditizing execution.

The value shifts upstream toward understanding humans, framing problems correctly, orchestrating systems, and defining how humans and machines work together coherently.

Designers who understand Human-Centered Design and Jobs-to-be-Done are uniquely positioned to help organizations navigate this transition because they are trained to observe behavior, uncover hidden motivations, map friction, and identify where trust actually gets created.

In many ways, the organizations most vulnerable to AI disruption may not be the ones slowest to adopt AI. They may be the ones automating work they never truly understood in the first place.

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