Roles, not Jobs
Beyond the Hype - a short series on AI and the future of work
The most important conversations I’m having right now aren’t with clients, they’re with my kids.
Three of them are in university. One is finishing school. And like many parents, I find myself being asked for advice about the future of work.
The honest answer is that I am less certain than I used to be.
When I was their age, career advice was relatively straightforward. My father and school counselors could point to established professions and relatively stable paths. Some of that advice held up better than other parts, but it was at least grounded in a world that was more predictable.
Today, that predictability is no longer a given.
I see the same uncertainty in my students.
The question is no longer just what to study or which profession to choose. It’s becoming harder to say what a “job” will even look like in ten or fifteen years.
A shift beneath the surface
Much of the current discussion around artificial intelligence still focuses on jobs: which ones will disappear, which ones will be created, and which skills will be needed.
This framing may already be too narrow.
What is changing is not only the content of work, but its structure.
Research consistently shows that AI often reshapes tasks within jobs rather than simply replacing them. Up to 30 percent of current work activities could be automated by 2030, while most occupations will be transformed rather than eliminated.
At the same time, roles built around highly standardized and repeatable tasks may disappear entirely.
In fact, almost all roles already contain elements that can be automated. Around 60 percent of occupations include at least 30 percent of activities that could be handled by existing technologies.
This suggests something important:
We are not simply moving from one set of jobs to another. We are moving toward a more fluid model of work.
From jobs to roles
If tasks are being redistributed between humans and machines, then the traditional idea of a fixed job begins to loosen.
We may be entering a world where work is increasingly organized around:
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roles rather than positions
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projects rather than long-term assignments
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temporary affiliations rather than permanent structures
This shift is already visible. Project-based work, cross-functional teams, and platform-based collaboration models are expanding. AI is accelerating this development by making it easier to break work into smaller, modular components.
In that sense, the comparison to earlier transformations is not entirely far-fetched.
During the Industrial Revolution, work moved away from guilds and inherited professions toward structured employment and defined roles within organizations.
Today, we may be witnessing another transition, this time away from stable job definitions toward more dynamic forms of contribution.
A partnership, not a replacement
Despite the intensity of current debates, the evidence does not point to a simple replacement of human work. It points to a reconfiguration.
AI is expected to generate significant productivity gains, while simultaneously requiring organizations to rethink how work is structured and how people contribute.
What emerges is less a story of substitution and more one of partnership.
Tasks that are structured, repetitive, and clearly defined are increasingly handled by systems. Tasks that require judgment, interpretation, and human interaction remain with people.
Some describe this as a shift from execution to orchestration.
Or more simply: from doing the work to shaping how the work gets done.
What this means in practice
If this direction holds, then staying relevant may depend less on deep specialization in narrowly defined tasks and more on a different set of capabilities.
In my own work, and in conversations with clients and students, a few patterns are becoming visible.
The ability to work with AI without outsourcing thinking to it is becoming critical. The tool is powerful, but its value depends on how it is used.
The ability to frame problems is becoming more important than the ability to execute predefined solutions.
There is increasing value in moving between contexts, connecting ideas, translating perspectives, and integrating different sources of knowledge
And there is something that remains difficult to automate: judgment. Not as a vague concept, but as the ability to decide what matters, what is appropriate in a given situation, and what should not be done, even if it could be.
A more personal reflection
When I speak with my children or my students, I find myself offering a different kind of advice than I might have given twenty or thirty years ago.
It is less about choosing the “right” profession and more about developing the ability to adapt, to learn, and to position oneself within changing contexts.
And perhaps also this: not to outsource thinking too quickly.
AI can support us in remarkable ways, as a sounding board, a research assistant, or even an editor. But it also introduces a quiet temptation: to let it do the work entirely, and in doing so, to disengage from the very capabilities that may become more important over time.
Closing thought
Perhaps the question is no longer:
“What job should I prepare for?”
But:
“How do I remain relevant in a world where the boundaries of work are shifting?”
- Chris Newman
Newman Seminars
This reflection draws on recent research into AI adoption, organizational transformation, and the evolving nature of work.
Sources and references
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McKinsey & Company (2025), The State of AI
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McKinsey Global Institute (2023), Generative AI and the Future of Work
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McKinsey Global Institute (2025), Agents, Robots and Us
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Harvard Business Review (various articles on AI and work transformation)
Key themes explored
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AI and the future of work
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Changing structures of employment
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Human-AI collaboration
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Skills and relevance
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Leadership in times of transformation
Further Reflections
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Roles, not Jobs