Human Skills and Future of Work
Learning Objectives
- You understand how AI is reshaping the nature of work across industries.
- You can identify which job tasks are most affected by automation and augmentation.
- You know the importance of re-skilling and up-skilling for adapting to AI-driven change.
- You can evaluate strategies for individuals, organizations, and societies to prepare for the future of work.
First things first, AI rarely — if ever — replaces entire occupations outright.
However, it can automate or augments specific tasks within jobs, reconfigure how remaining work is performed, and potentially create new types of work.
The current wave differs from previous automation waves that primarily displaced manual labor or routine physical tasks. AI affects cognitive and knowledge-based work: research synthesizing information, data analysis interpreting patterns, programming designing solutions, writing for specific audiences, and creative work involving design or content generation.
Automation, augmentation, and creation
The current impact of AI systems is primarily at the task level. Most occupations comprise numerous distinct tasks, and AI affects different tasks in different ways.
Automated tasks are those AI can perform independently — routine report generation following standard formats, data entry and formatting between systems, simple customer service queries with straightforward answers, and scheduling following clear rules. The work still needs doing, but AI systems could performs it with minimal human intervention.
Of course, this requires that information is up-to-date and accurate — e.g. scheduling meetings requires that every participant’s calendar is up to date.
Augmented tasks are where AI enhances human capabilities rather than replacing them. Complex data analysis becomes more accessible when AI identifies patterns that humans interpret and validate. Decision-making improves when AI provides real-time information while humans make final judgments considering context and values. Collaborative writing becomes more efficient when AI generates drafts that humans refine and adapt. Research accelerates when AI rapidly searches and synthesizes literature while humans frame questions and evaluate reliability.
In augmentation, humans and AI work together. AI handles processing speed, tireless attention, and pattern recognition across large datasets. Humans provide judgment about relevance, understanding of context, ethical reasoning, and creative insight.
The key challenge is designing workflows where AI complements rather than overwhelms human contributions. Poorly designed systems can lead to over-reliance on AI outputs, reduced human engagement, and skill atrophy.
Created tasks and roles emerge as AI becomes prevalent: designing AI systems and workflows, monitoring and oversight ensuring correct operation, ethics and governance addressing appropriate use, human-AI integration optimizing collaboration, training helping others work with AI tools, and quality assurance verifying outputs before use for important decisions.
Consider a financial analyst: previously, significant time went to collecting data, formatting it, generating reports, and performing calculations — increasingly automated tasks. Now more time goes to interpreting patterns, advising clients on strategic decisions, considering qualitative factors, and explaining findings to stakeholders. The job title remains the same, but actual work has shifted toward tasks requiring human judgment and communication.
Human strengths
As AI capabilities expand, certain distinctly human strengths become increasingly valuable:
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Understanding emotions, building trust, and connecting meaningfully with others. AI can simulate empathy but cannot genuinely feel or care. These capabilities remain essential in healthcare, education, counseling, and leadership.
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When values, fairness, or justice are at stake, humans should (or must) remain the final decision-makers. Ethical questions involve considerations that cannot be fully captured in optimization objectives — what is fair, what rights should be protected, what long-term consequences matter, and how to balance competing values. Ethical and moral questions also often involve human debate and consensus-building.
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AI can remix and generate, but it lacks personal history, cultural nuance, and emotional resonance. Human creativity adds depth, originality, and meaning — understanding what will resonate with audiences, what statements matter culturally, what beauty or meaning looks like in context.
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Providing context, discernment, and the ability to question whether AI outputs make sense in the real world. Complex problem framing often matters more than optimization — determining what questions to ask, what factors are relevant, how to understand ambiguous situations, and what outcomes we should pursue. Even a seemingly simple — but important and frequent in software engineering — question “which of these features would add the most value to users?” requires deep understanding of user needs, business context, and trade-offs that AI struggles to grasp.
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Defining goals, articulating purpose, and inspiring others — roles that go beyond executing tasks and require genuine human presence.
Rather than viewing AI as an omnipotent entity, it is more accurate to see it as a complement. AI can excel at scale, speed, and pattern recognition. Humans excel at judgment, meaning-making, and value-driven decisions.
Need for continuous skill development
The pace of AI-driven change makes continuous skill development essential. The traditional model — acquire skills during education, then apply throughout a career — no longer suffices when required skills can shift substantially within years.
Thank you, by the way, for joining this course! :)
With the increase of AI systems in workplaces, there is a need for digital and AI literacy which involves understanding how AI systems work conceptually, what they can and cannot do reliably, their limitations, how to interpret outputs critically, when to trust recommendations and when to question them, and what risks and ethical considerations arise.
Furthermore, much of AI use is essentially tool use — like using Excel or word in the past — but more complex. This requires tool fluency: knowing which tools to use, how to use them effectively, understanding their strengths and weaknesses, knowing how to verify their outputs, and integrating them into workflows.
Finally, there’s a need to design and evaluate AI-augmented workflows, monitor system performance, evaluate outputs critically, provide feedback that improves behavior, and manage handoffs between automated and human-handled work. Not to mention all the other skills that remain essential regardless of AI: communication, collaboration, empathy, ethical reasoning, creativity, and strategic thinking.
See also the Future of Jobs Report from the World Economic Forum, which provides a detailed analysis of how job roles and skills are expected to evolve in the coming years.
Learning versus performing
There’s a critical distinction between learning and performing with AI. AI can be a powerful tool for both, but the implications differ significantly. When people use LLMs to complete tasks, they learn how to use the model — but not necessarily how to do the underlying task themselves.
The calculator debate in mathematics education provides a useful parallel: calculators are beneficial for experts who understand underlying concepts but risky for novices who need to develop foundational skills. The same logic applies to LLMs.
In education, the objective is almost always to learn first. Over-reliance on LLMs risks undermining the outcomes and making future learning harder.
LLMs can help students finish assignments without grasping the underlying material, reducing long-term retention and skill development.
In professional life, however, the objective is often performance first. LLMs can accelerate tasks, even when users don’t fully understand the subject. However, relying too heavily on AI may reduce an individual’s long-term value to their organization, and lack of underlying knowledge can cause problems in future contexts where AI support is unavailable or unsuitable.
The core challenge is learning to use AI critically in the task-specific context, be it in learning or in professional life, balancing short-term productivity with long-term learning.
Alternative futures
Think of the following alternative futures and the idea on learning versus performing.
Scenario 1: Augmented excellence (2050)
Agatha logs into her workspace, where her AI collaborators are already active. Orion, a research agent, has pulled together the latest climate models overnight. Luma, a design agent, has generated three visual prototypes for community presentations.
Agatha doesn’t just accept their outputs — she critiques them. She notices Orion included projections from a disputed source and asks it to rerun the analysis with stricter filters. She praises one of Luma’s designs for clarity but suggests warmer colors to better resonate with local culture.
Later, she joins a team meeting. Half the participants are human colleagues, half are specialized agents. The humans debate policy trade-offs, weighing social and ethical concerns the AI can’t resolve. The agents contribute precise calculations, scenario modeling, and draft text for proposals.
By the end of the day, Agatha hasn’t been replaced — she’s been amplified. Her time is spent not on collecting data or formatting slides, but on guiding strategy, applying judgment, and ensuring work reflects human values. The ecosystem around her is rich, adaptive, and collaborative — a future of hybrid intelligence where both humans and AI thrive.
Scenario 2: Atrophied capacity (2050)
Ethan wakes to his AI assistant’s summary of the day. He hasn’t read a full article in years — the AI summarizes everything. At work, he approves AI-generated reports without reading them carefully. “The AI handles it,” he tells himself.
His team struggles with a supplier dispute requiring negotiation and contextual judgment. But years of delegating such tasks to AI systems mean no one has practiced these skills. They ask their AI for help, but it suggests a generic approach that ignores the relationship history and context. The negotiation fails.
Ethan’s teenage daughter asks him to help with her algebra homework. He tries to explain but realizes he can’t remember how to solve systems of equations — he’s used AI calculators for a decade. “Just ask the AI,” he tells her, recognizing the irony even as he says it.
The technical infrastructure still works, but something essential has been lost. Creativity has become formulaic — people generate ideas with AI but increasingly lack the judgment to recognize which are actually good. Problem-solving has become rigid — people can execute known procedures but struggle when facing novel situations. Critical thinking has atrophied — people can identify what AI suggests but can’t evaluate whether it makes sense.
This isn’t a sudden collapse but a gradual erosion. Each individual delegation of cognitive work to AI seemed reasonable at the time. Each skill that went unpracticed seemed acceptable when AI could compensate. But the cumulative effect has been profound: a society technically advanced but intellectually diminished, surrounded by powerful tools but increasingly unable to use them wisely.
The above scenarios are in part written in a tongue-in-a-cheek fashion, painting extremes. A more likely outcome is somewhere in between, with some professions and individuals maintaining high levels of skill while others experience more atrophy. The future will likely be uneven, with different sectors, regions, and demographics experiencing varying impacts based on access to technology, education, and economic opportunities.
We might even end up seeing a third AI winter if the technology doesn’t mature as expected. The gap between expectations and reality has been significant — for a while, GPT-5 was discussed alongside AGI milestones, with language about ‘PhD-level’ intelligence. When it finally launched in August 2025, many users and critics felt it fell short of the revolutionary leap that had been anticipated.
Choosing a path
These scenarios represent different possible futures based on how we choose to integrate AI into our lives and work.
The augmented excellence scenario emerges when we maintain human skill development, use AI as a tool that extends rather than replaces human capability, practice critical evaluation of AI outputs, preserve expertise even when AI handles routine execution, and design systems that keep humans meaningfully engaged rather than passively accepting AI decisions.
The atrophied capacity scenario emerges when we outsource cognitive work without maintaining underlying skills, accept AI outputs without critical evaluation, fail to practice the capabilities AI automates, allow convenience to override the importance of skill development, and design systems that minimize human involvement rather than optimizing human-AI collaboration.
The difference is not the technology itself but how we choose to use it. AI systems are tools — powerful ones that can either amplify human potential or enable its decline. The outcome depends on deliberate choices at individual, organizational, and societal levels about how to integrate AI while preserving and developing human capabilities.
Looking ahead
We are entering — and partially already are in — an era of hybrid intelligence.
Expect to see more fluid roles where individuals work alongside multiple AI systems daily, new professions focused on AI oversight, ethics, and orchestration, cultural shifts toward valuing human uniqueness as a counterbalance to machine capability, and ecosystems of collaboration where both humans and AI contribute distinct strengths to shared goals.
The rapid shifts in skill requirements will continue, with the half-life of specific technical skills potentially shortening. Adaptability and learning capability become more valuable than specific current skills.
Further, the expansion of re-skilling initiatives across companies, governments, and institutions will grow, with more varied learning pathways and systematic approaches to helping workers navigate transitions.
The path forward requires paying attention to what we do — there’s a need to ensure that as we gain the benefits of AI augmentation, we don’t inadvertently lose the human capabilities that make meaningful work and innovation possible. The goal is not to maximize AI use but to optimize human-AI partnership in ways that preserve and enhance rather than diminish human potential.
This means finding new ways to work with AI tools rather than being replaced by them. Values like ethical judgment, problem framing, empathy-driven work, and creative vision remain distinctly human contributions that no amount of computational power can replicate; all the basic skills must also be maintained to ensure we can use AI tools wisely.