Workforce 2030: Part 2 - Building a World Our Kids Can Thrive In

The agentic organization, the skill stack, the education challenges, and many questions without easy answers

Mar 24, 2026

Video created with the help of MidJourney

In Part 1, we explored how the twentieth‑century “job”—a fixed bundle of tasks tied to a single employer—is dissolving under three forces: AI co‑pilots, the platformification of work, and a hard demand for purpose. We saw glimpses of what might emerge in its place: Gig Guilds, Human‑AI Pods, Civic Contribution models, and ultimately, more modular, problem‑centered work. If you haven’t read it yet, you can find Part 1 here.

Now, what might we need to redesign, and how?

If the workplace my daughter enters will be made of Problem‑Solving Clusters and hybrid teams, then two systems must evolve in sync:

How will we design work, talent, and leadership in our organizations?
How will we prepare people for hybrid human–AI problem‑solving through our education systems?

Both face the same uncomfortable but fundamental question that we should start with:

Are we optimizing for a past that’s disappearing (because we know it), or experimenting toward a future we can’t yet fully see?

The Agentic Organization: From Blueprint to Beta

McKinsey argues that capturing the $2.9 trillion in AI‑driven value by 2030 “may depend less on new technological breakthroughs than on how organizations redesign workflows…and how quickly human skills adapt.”

We’re moving from organizations with fixed blueprints to ones that operate in a potentially perpetual beta — constantly testing and adapting their operating models. What might that look like in practice?

Redesigning Work: From Boxes to Flows

Instead of debating job descriptions, what if we started by mapping core workflows? McKinsey’s research shows large enterprises identifying around 190 key workflows across 16 functions. This shift in perspective raises different questions:

Which parts of this workflow could benefit from AI augmentation versus human judgment?
Where do we need deep specialization versus general orchestration?
How do we design handoffs between human and AI contributors to maintain quality and trust?

Redesigning Leadership: From Supervision to Orchestration

As managers increasingly oversee systems where people, agents, and robots collaborate, their role transforms. McKinsey describes this shift toward orchestration. This raises questions about what “good management” looks like:

How do you coach someone whose primary collaborator is an AI agent?
How do you build psychological safety in teams that include non-human members?
What metrics track the health and performance of human–AI collaboration?

Redesigning Careers: From Ladders to Portfolios

With around 72% of skills applicable in both automatable and non‑automatable work, the concept of a linear career path feels increasingly outdated. McKinsey’s analysis suggests workers will need to move through roles as their skills evolve.

This prompts different organizational questions:

What would a talent marketplace look like that helps people navigate toward evolving opportunities rather than up fixed ladders?
How do we reward skill acquisition and problem‑solving capability rather than just tenure or position?
What happens to organizational memory and culture when people move more fluidly between projects and teams?

For my daughter, this evolving landscape suggests her future manager might be less of a traditional “boss” and more of a coach, resource connector, and orchestrator of multidisciplinary human–AI teams.

The Skill Stack: What’s Rising, What’s Shifting, What’s Anchoring Us

If by 2030, roughly one‑quarter to one‑third of work hours tied to the 100 most in‑demand skills could be automated, with digital and information‑processing skills most exposed and assisting and caring skills least affected.

This suggests we might think about skills in three evolving categories:

1. Sunset Skills: The Automating Foundation

These are highly automatable, narrow skills that will likely shrink in demand — think routine data entry, certain types of repetitive analysis, or standardized coding patterns. Their value isn’t disappearing entirely, but shifting from human execution to human oversight of automated processes.

2. Fusion Skills: The Evolving Partnership

These are skills that transform in partnership with AI — problem‑solving, writing, research, quality assurance, data analysis. Here, the work shifts from doing everything manually to supervising, directing, framing problems for, and interpreting outputs from AI collaborators. Demand for AI fluency specifically — the ability to use and manage AI tools — has grown nearly sevenfold in two years, making it the fastest‑growing skill category McKinsey tracks.

3. Anchor Skills: The Human Constant

These are deeply human skills that remain relatively automation‑resistant: leadership, empathy, conflict resolution, systems thinking, ethical judgment, creative synthesis. McKinsey finds interpersonal capabilities like negotiation and coaching sit in the lowest quartile of automation exposure.

For organizations, this evolving skill landscape raises practical questions:

How do we build company‑wide AI literacy without it feeling like just another compliance program?
What would internal talent marketplaces need to become true “skill navigators” rather than just job boards?
How do we identify and develop anchor skills that often get labeled as “soft” but may become our most critical competitive advantage?

For the next generation, we might focus less on teaching specific tools and more on cultivating:

The meta‑skill of learning new tools quickly
The critical thinking to interrogate and improve AI outputs
The creative confidence to combine domain knowledge, human judgment, and AI capabilities into novel solutions

Education for Problem‑Solving Clusters: From Memorization to Navigation

If we keep designing schools for memorization and standardized testing, we risk preparing children for the work AI is already becoming good at — while under‑preparing them for the work where humans will still excel.

Looking at these trends, what if education shifted from being primarily about knowledge transmission to becoming more about navigation skill development?

Project‑Based, Cross‑Disciplinary Navigation

Work in Problem‑Solving Clusters, Pods, and civic projects will inherently span domains. Research on Gen Z and millennial preferences shows they crave meaningful, connected work. This suggests students might benefit from more practice tackling open‑ended problems where success criteria are ambiguous and feedback is iterative — mirroring the complex challenges they’ll face professionally.

Collaboration with AI, Not Avoidance of It

Instead of banning AI tools, what if we taught students how to use them for research, drafting, coding, and designing — then how to critique and improve the outputs? The future workplace expects hybrid human–AI workflows; could classrooms become safe sandboxes for developing these collaborative muscles?

Explicit Development of Anchor Skills

Communication, conflict resolution, ethical reasoning, and systems thinking might need to move from the edges of curricula to their core. These are precisely the skills that McKinsey finds least exposed to automation yet most central to orchestrating hybrid teams. How might we assess and develop these differently than we assess factual recall?

Lifelong Learning as Navigational Literacy

With skills becoming more specialized and dynamic, the ability to reskill and move into adjacent roles may matter as much as any degree. What if we taught learning how to learn — including how to identify skill gaps, find quality resources, and build new competencies — as a fundamental literacy alongside reading and math?

This approach aligns with the insight from my earlier framework — we’re preparing students not for specific boxes in a 2x2 matrix, but for the ability to move fluidly between different modes of working as problems and opportunities evolve. When my daughter asks to learn digital drawing, perhaps the most valuable approach isn’t just teaching her today’s tools, but involving her in projects where she uses those tools to express ideas, solve problems, and collaborate — with classmates, with teachers, and yes, with AI systems as creative partners.

Responsible Oversight: Building Guardrails for Uncharted Territory

As we embed more capable AI into workflows, the question of governance becomes increasingly urgent. Anthropic’s “Responsible Scaling Policy” introduces AI Safety Levels — cumulative technical and operational standards that scale safeguards as systems become more capable. The core idea is both simple and profound: the more powerful the system, the stronger and more structured the safeguards must be.

McKinsey warns that key challenges remain around hallucinations, transparency, and explainability, which are critical to ensuring safety and avoiding unwanted bias as agents become more embedded.

This creates practical many questions for organizations experimenting with these new models:

How do we evaluate AI systems before deployment when their capabilities are evolving rapidly?
What does continuous monitoring look like for hybrid human–AI workflows?
Where do we place the clear “human in the loop” checkpoints for critical decisions?
How do we maintain organizational accountability when work is distributed across humans and increasingly autonomous systems?

For education, it suggests we need to teach our kids — not just our employees — how to use AI responsibly: how to question its outputs, understand its limitations, and recognize its potential risks alongside its benefits.

Questions Without Easy Answers

I’m filling out a questionnaire for my daughter’s application to the Gifted and Talented program at her elementary school. As a first time parents, I have no idea how kids are tested into such programs and am curious to learn about the standards and how they are measured at such an early age. The form asks for observations on how she learns, plays, and expresses herself — the subtle markers of a smart, curious mind.

One question gives me pause:

“Example(s) of how my child possesses a large storehouse of information about a variety of topics beyond the usual interest for their age.”

What’s “usual” for her age? I’m not sure. More importantly, why does a “large storehouse of information” still signal giftedness today?

I grew up in a system where reciting 500 ancient poems — most likely without understanding them — or 300 digits of π earned a child prodigy status. But in an era powered by AI, where recall is instant and synthesis is the new frontier, why are we still selecting and grooming kids for skills of memorization?

On one hand, I completely understand that the ability to possess such a large storehouse of information is a sign that the kid is smart and curious. But on the other, it’s too obvious in today’s world that the smartest person can’t out-smart machines if we define smart by some of the old rules. Shouldn’t the rules evolve accordingly? Shouldn’t we be making more room for empathy, creativity, problem-framing, and collaboration — the very capabilities that distinguish humans from machines?

Just like the skills to design in Illustrator, Solidworks, and Figma, I know it’s not that the skills won’t matter — it’s that how they matter is changing.

She won’t grow up to be a “designer” in the narrow, job-title sense. It will most likely be moot to master a sure-to-be outdated digital tool, or to understand the foundation of how that generation of tools works. Just like how it used to matter to learn to maneuver a horse-drawn carriage, and then to drive a car, be it a stick shift or automatic, so you can go to work, the bank, or the clinic, and now, many of us sit in a comfortable chair to get your work done, and occasionally call a (self-driving) Uber to meet with friends in a cafe; and in the future, driving might become “not a thing” at all. A designer’s job might become more about knowing what you want to build, and less about how, mechanically and manually, you will manifest that vision on paper (or screen).

She’s more likely to move through some kind of fluid Problem‑Solving Clusters where design, storytelling, data analysis, and AI orchestration intertwine. And her career won’t be a straight line; it will be a series of pivots — sometimes by choice, sometimes by necessity — that may well make her parents uneasy.

As my earlier framework suggested, she won’t occupy one box in a career matrix. She’ll navigate the intersections between physical and digital, routine and complex work throughout her life.

A Few Things We Can Do Today

I like to imagine and envision futures as much as any futurists, but I love to be able to bring it back to now: For leaders, educators, and parents, what might be some small steps we can take today to better understand tomorrow, and to test some assumptions for future success?

For organizations:

What small experiment could we run this quarter to test a more fluid, problem‑centered way of working? - Which team and project would be the most suitable for this experiment?
How might we map our key workflows to identify where AI collaboration could augment rather than just automate?
What would need to be true for our people to feel excited rather than threatened by these shifts?

For education systems, teachers, and fellow parents:

How can we create one small opportunity this month for kids to work on open‑ended problems that lack predetermined answers?
Where can we safely introduce AI as a collaborative tool rather than a cheating risk this semester?
How do we help the next generation develop both the technical fluency and the human judgment they’ll need starting from the classroom?

The future of work won’t be announced in a memo. It’s showing up in quiet, scattered experiments. The product manager who quietly assembles a cross‑functional Pod with an AI agent as a core member. The HR leader who prototypes an internal talent marketplace instead of fighting over headcount. The teacher who lets students co‑create with AI and then leads a discussion about what made the human contribution unique

Our opportunity — professionally and personally — might be to notice those experiments in our own contexts and ask: what if this small experiment contains clues to a much larger shift? What if the way we respond to these early signals shapes whether this transition feels like displacement or like the emergence of new, more human possibilities?

Before this quarter ends, what if we tried just one small thing?

Could we charter a 60‑day micro‑team to tackle one cross‑functional problem? Give them permission to work differently, to experiment with AI tools, and to share what they learn — not just what they deliver.
Could we sit with a child in our world and do one creative project with AI? Then ask them: “What do you think you did that the AI couldn’t? What did it do that surprised you?”

At the end of both experiments, maybe the most valuable question isn’t “Did we succeed?” but rather:

“What did we learn about how work and learning might need to change — for us now, and for the next generation coming after us?”

The answers might be messier than we’d like, as most true answers are. They might lead to more questions than solutions. But in asking them together, we might just start building the bridges between the world of work we inherited and the one our children will need to thrive.

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