AI tasks – LinkedIn CEO Ryan Roslansky and economic strategist Aneesh Raman argue that AI will target tasks, not identities—so workers should rebuild skills around human judgment.
AI is moving through workplaces fast enough that even seasoned professionals can feel behind—yet LinkedIn leaders frame the shift as something more controllable than it looks.. In Misryoum’s view. the key change is straightforward: automation is arriving at the level of what people do each day. not how they label themselves.
LinkedIn chief economic opportunity officer Aneesh Raman and LinkedIn CEO Ryan Roslansky lay out five ideas drawn from their book. “Open to Work: How to Get Ahead in the Age of AI.” Their message for workers is both practical and slightly uncomfortable.. Rather than trying to “out-tech” AI. they argue people should re-sort their work into what machines can handle. what they can co-pilot with AI. and what still requires human trust and judgment.
Why AI targets tasks, not your title
The first shift is conceptual: stop thinking of your role as a title and start treating it as a set of tasks.. In a traditional job market, titles helped employers categorize people and decide where they fit.. But as Misryoum observes. AI doesn’t primarily “understand” your job title—it performs patterns in specific kinds of work: routine inputs. basic drafting. standard analysis. and repetitive coordination.
Raman’s framework breaks tasks into three buckets.. Bucket one covers tasks AI can do alone—think data entry. basic research. and scheduling that doesn’t depend on conversation.. Bucket two is the collaboration zone: using AI for strategy support, creative iteration, and assisted problem-solving.. Bucket three is the uniquely human layer—work that depends on reading emotions. building relationships. or making judgment calls when the answer can’t be reduced to a checklist.
The deeper point is how careers evolve alongside technology.. As bucket one shrinks, bucket two can expand quickly, often enabling more complex projects than before.. And as people get fluent in AI-assisted work. they create time and cognitive space for bucket three—where durable value tends to concentrate.. For employers. this also hints at a rethinking of staffing: the question becomes which tasks a team can automate and which require senior human oversight.
The 5Cs: soft skills become survival skills
The second message challenges a common assumption.. Skills like communication, curiosity, and creativity are often treated as “soft” because they’re harder to quantify than technical output.. Roslansky and Raman argue that this framing will age poorly.. In the age of AI. where routine tasks become easier to reproduce. human-driven skills start to function like a competitive moat.
They group those abilities into the 5Cs: Curiosity, Courage, Creativity, Compassion, and Communication.. The claim isn’t that AI can’t generate text or remix ideas—Misryoum readers know the tools can.. The difference is the direction and intent behind the work.. Curiosity pushes questions that templates don’t anticipate.. Courage steers risk-taking when data isn’t enough.. Creativity matters when constraints must be reframed.. Compassion shapes priorities when the “correct” answer is still missing human context.. Communication turns information into meaning for other people.
There’s also a practical reason these skills rise in importance: AI collaboration is not passive. People have to decide what to ask, how to verify, what to discard, and what to escalate. That’s judgment, and judgment is where these 5Cs show up in daily decisions.
Careers as “climbing walls,” not ladders
The third idea targets another workplace myth: the career ladder.. Raman describes it as a relic of an industrial era built on stability—jobs that lasted. skills that stayed relevant. and promotions that followed predictable timelines.. AI compresses that stability by increasing how quickly tasks change. how quickly tools change. and how quickly entire workflows can be redesigned.
Instead of a single upward track. they describe a “climbing wall” model: multiple routes. sideways moves that add new capabilities. and sometimes even downward moves to reach stronger positions later.. The guidance is to ask three questions that help people design their own climb.. Why do you work?. What uniquely do you do?. Where are you going?. The answers are personal. but the structure is actionable—especially in industries where job descriptions evolve faster than the old résumé categories.
From a market perspective, this shift also changes hiring and internal mobility. Companies may rely less on linear promotion criteria and more on portfolios of task proficiency—evidence that a worker can shift between bucket two and bucket three as the organization’s needs change.
Exponential change rewards experimentation over prediction
The fourth point is about timing and mindset.. People often struggle with technological change not because it’s complicated. but because it arrives in stages that feel slow until suddenly they don’t.. Raman uses the “S curve” idea to describe adoption: early stages look almost invisible. then adoption accelerates rapidly. then growth levels off when the technology becomes mainstream.
The argument here is that AI has already moved into the steep part of that curve.. Misryoum interprets the implication as a warning against waiting for certainty.. When adoption accelerates, comfort becomes expensive.. The recommended response is to experiment now—learn in the moment. adapt quickly. and accept that forecasting will be less useful than iterating.
That approach also fits how AI tools are actually used. Rather than one-time training, most productivity gains come from repeated cycles: testing prompts, validating outputs, refining processes, and building new habits that make AI a reliable coworker instead of a novelty.
Your differences become the edge AI can’t replicate
The final lesson is the most human one.. Roslansky and Raman stress that there are billions of people in the workforce, but only one of you.. That’s not a motivational slogan; it’s a strategic claim about what AI will struggle to replace.. If AI handles standard approaches—patterns. routines. and generic outputs—then individuality becomes valuable in the “non-standard” spaces where context matters.
They point to the specific experiences that shape how a person thinks: the resilience built through failures. the pattern recognition developed through lived-in cultural perspective. and the unconventional connections formed by taking a less direct route.. Even the quirks in how you solve problems can matter. because they often reflect context. relationships. and interpretation that a model can’t genuinely experience.
For Misryoum readers, the practical takeaway is this: “be more you” only works when paired with intentional skill-building.. In the new workflow reality. individuality becomes a competitive advantage when it’s attached to clear task ownership—knowing which work you can delegate to AI. which work you can enhance with it. and which decisions require your human judgment.
In a labor market that’s already starting to reorganize around what can be automated, the workers who gain the most leverage may be the ones who treat AI as a restructuring tool—so they can redirect their time toward the parts of work that demand judgment, trust, and originality.