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Why the future of work may depend less on competing with machines—and more on becoming more distinctly human
By Noé J. Ishaka, M.Ed.
IRJAR – Interdisciplinary Research Journal and Archives
A quiet revolution is taking place in workplaces around the world.
It is not happening only inside technology companies in California, research laboratories in Europe, or manufacturing plants in East Asia.
It is happening in classrooms.
Hospitals.
Banks.
Government offices.
Universities.
Newsrooms.
Farms.
Small businesses.
Construction companies.
Non-profit organizations.
And increasingly, on the smartphones and computers sitting on ordinary people’s desks.
Artificial intelligence can now write reports, summarize documents, translate languages, analyze spreadsheets, generate software code, create marketing campaigns, answer customer questions, produce images, prepare presentations, assist scientific research and perform tasks that once required hours—or even days—of human labour.
This transformation has naturally generated anxiety.
What happens to accountants when software can analyze financial information?
What happens to programmers when AI can generate code?
What happens to writers when machines can produce fluent text?
What happens to teachers when every student can have an AI tutor?
What happens to translators when language models can translate hundreds of pages almost instantly?
And perhaps the biggest question of all:
If machines become increasingly capable, what will humans still need to be good at?
The answer may surprise us.
The future of work is unlikely to belong simply to the people who know the most technology.
Nor will it necessarily belong to those who resist technology.
It may belong to people who can combine technological capability with something much harder to automate:
judgment, creativity, adaptability, empathy, ethical reasoning, communication, leadership and the ability to continue learning.
The skills revolution is therefore not only technological.
It is profoundly human.
We have been here before—but not exactly like this
Human beings have repeatedly invented technologies that changed work.
The steam engine transformed agriculture and transportation.
Electricity transformed manufacturing.
The automobile reorganized cities.
Computers changed offices.
The internet reshaped communication and commerce.
Automation transformed factories.
Each technological transition eliminated some tasks while creating others.
Artificial intelligence follows this historical pattern—but with one significant difference.
Previous technological revolutions largely automated physical labour or routine calculations.
AI increasingly touches cognitive work.
That distinction matters.
A machine does not merely lift a heavier object than a human being.
It can now participate in activities once considered signs of intellectual ability: writing, analyzing, translating, diagnosing patterns, generating ideas and producing computer code.
This is why today’s transformation feels different.
Yet major labour-market research suggests that the emerging challenge is not simply one of mass technological replacement. It is a large-scale reorganization of tasks and skills.
The World Economic Forum’s Future of Jobs Report 2025, based on surveys of more than 1,000 employers, projected that labour-market disruption could affect approximately 22% of existing jobs by 2030. Employers anticipated the creation of about 170 million jobs and the displacement of around 92 million, implying a net increase of approximately 78 million roles—but also enormous transition pressures.
The important word here is not simply jobs.
It is transition.
Because even many jobs that survive may not look the same.
Your occupation may stay. Your tasks may not.
One of the most misleading ways of discussing AI is to divide jobs into two categories:
Jobs AI will replace.
Jobs AI will not replace.
Real workplaces are more complicated.
Most jobs contain multiple tasks.
Consider a teacher.
A teacher may:
- prepare lessons;
- explain concepts;
- assess student work;
- write reports;
- communicate with parents;
- motivate students;
- manage conflict;
- notice when a child is struggling;
- adapt instruction;
- maintain classroom culture;
- mentor young people.
AI may automate or accelerate some of those tasks.
It can generate worksheets.
It can draft feedback.
It can explain fractions.
It can suggest lesson plans.
But motivating an anxious student, managing relationships, understanding classroom dynamics and exercising professional judgment involve a different category of skill.
The same logic applies to medicine.
AI can analyze images and summarize medical records.
But patients still need doctors who can interpret evidence, make decisions under uncertainty, communicate risk and respond to human fear.
A lawyer may use AI to review thousands of documents.
A manager may use it to analyze performance data.
An entrepreneur may use it to develop business plans.
A researcher may use it to organize literature.
The occupation may remain.
But the balance of tasks changes.
And when tasks change, the skills people need change with them.
Nearly everyone may need to learn again
The scale of this transition is substantial.
The World Economic Forum estimates that if the world’s workforce were represented by 100 people, approximately 59 would require training by 2030. Employers surveyed for the report identified skills gaps as the most important barrier to organizational transformation.
This may be one of the defining labour-market challenges of the next decade.
Not unemployment alone.
Not technology alone.
But reskilling.
Millions of people may continue working in familiar industries while discovering that the competencies required for those industries have changed.
A marketing professional may suddenly need AI literacy.
A teacher may need to understand AI-generated content.
A physician may need to interpret algorithmic recommendations.
A journalist may need verification skills capable of identifying synthetic media.
A manager may need to supervise employees who work alongside intelligent systems.
An electrician may use AI-supported diagnostic tools.
A researcher may increasingly combine domain expertise with computational tools.
Learning can no longer be something humans complete in their twenties.
Lifelong learning is becoming economic infrastructure.
Skill #1: AI literacy
The first skill humans will need in an AI economy is, unsurprisingly, the ability to understand AI.
But AI literacy does not mean that everyone needs to become a machine-learning engineer.
A nurse does not need to build a language model.
A teacher does not necessarily need advanced programming.
A business owner does not need a doctorate in computer science.
AI literacy means understanding what these systems can do, what they cannot reliably do, how to use them effectively, and when not to trust them.
The International Labour Organization’s 2026 report on skills in the age of AI argues that the ability to understand and use AI safely and ethically is becoming a foundational competency, much like broader digital literacy became essential during earlier stages of technological development.
The OECD likewise reported in 2026 that inadequate skills are one of the major obstacles preventing organizations from adopting AI effectively, while AI implementation is increasing demand for highly skilled workers and capabilities such as data analysis and interpretation.
So tomorrow’s basic question may no longer be:
“Do you know how to use a computer?”
It may become:
“Do you know how to work intelligently with AI?”
That involves more than writing prompts.
It requires knowing when to verify information.
Understanding privacy.
Recognizing hallucinations.
Evaluating bias.
Protecting confidential data.
Knowing when human expertise should override machine recommendations.
And using technology without becoming intellectually dependent upon it.
Skill #2: Critical thinking
The irony of artificial intelligence is that the better machines become at producing answers, the more valuable humans become at evaluating them.
Before generative AI, producing an answer was often the difficult part.
Now producing an answer can take seconds.
The difficult question becomes:
Is it right?
Imagine receiving a polished five-page analysis from an AI system.
The grammar is excellent.
The structure is convincing.
The argument sounds sophisticated.
But one source does not exist.
A statistic has been misunderstood.
The conclusion overstates the evidence.
The AI sounds confident.
A poorly trained user may simply accept the output.
A skilled user asks:
Where did this information come from?
What assumptions were made?
What evidence contradicts it?
What information is missing?
Does correlation imply causation here?
Is the recommendation appropriate in this context?
This is why critical thinking may become one of the most important forms of AI literacy.
The World Economic Forum reports that analytical thinking remains one of the most important core competencies identified by employers, while it also appears among skills expected to remain highly significant through 2030.
AI can generate arguments.
Humans must increasingly learn to interrogate them.
Skill #3: Creativity
A strange thing happened when generative AI arrived.
Many people assumed creativity would be among the safest human abilities.
Then machines began creating paintings, music, stories, advertising copy, logos and video.
So is creativity becoming obsolete?
Probably not.
But our definition of creativity may need to change.
If creativity simply means generating large numbers of ideas, machines can now do that extremely well.
Human creativity becomes more valuable when it involves:
selecting the right problem;
understanding cultural context;
connecting seemingly unrelated ideas;
challenging assumptions;
identifying opportunities;
imagining possibilities that matter;
and recognizing which ideas deserve to become reality.
The World Economic Forum places creative thinking among the fastest-growing capabilities expected through 2030.
LinkedIn’s analysis of rapidly growing workplace capabilities similarly identified innovation, strategic thinking and adaptability across multiple labour markets.
AI may therefore change creativity from:
“Can you produce something?”
to:
“Can you produce something worth producing?”
That is a considerably higher standard.
Skill #4: Communication
Technology has given humanity more ways to communicate than any previous generation possessed.
Email.
Video calls.
Messaging applications.
Social media.
Translation technology.
AI-generated correspondence.
Yet organizations continue to struggle with communication.
Why?
Because transmitting words and communicating meaning are not the same thing.
Communication involves understanding the listener.
Recognizing emotional context.
Explaining complexity.
Listening.
Negotiating.
Persuading.
Managing disagreement.
Creating trust.
Knowing when an email should instead become a conversation.
And sometimes knowing when not to speak.
LinkedIn’s 2025 skills analysis found communication among the fastest-growing competencies across several countries, including Australia, Brazil, France, Spain and the United Kingdom.
The pattern is revealing.
As digital technology becomes more powerful, employers are not abandoning human interaction.
They appear to be valuing it more.
This makes intuitive sense.
When everyone can produce technically polished communication with AI assistance, authenticity, clarity and interpersonal skill become differentiators.
A machine can draft an apology.
Only a person can decide whether the apology is sincere.
Skill #5: Emotional intelligence and empathy
Imagine two doctors.
Both have access to the same diagnostic technology.
Both understand the medical evidence.
One walks into a room, looks primarily at the screen and explains the diagnosis in technical language.
The other notices the patient’s fear, pauses, asks what the patient understands and explains the situation carefully.
Both may be medically knowledgeable.
But their impact on the human being in front of them is different.
As technology handles more technical information, human interaction may become increasingly visible.
The World Economic Forum identifies empathy and active listening among employers’ core workforce skills, alongside leadership, self-awareness and service orientation.
The International Labour Organization likewise emphasizes the growing importance of socio-emotional capabilities alongside technical competencies in AI-augmented workplaces.
This is especially relevant in occupations involving people:
education;
healthcare;
leadership;
counselling;
management;
sales;
public service;
caregiving;
social work.
AI can simulate empathy linguistically.
But human relationships involve accountability, shared experience, trust and presence.
That distinction matters.
Skill #6: Adaptability
Perhaps the most important career skill of the twenty-first century will be the ability to become a beginner repeatedly.
A professional may graduate at 23.
By 33, half the software used in the profession may be different.
By 43, parts of the occupation may be automated.
By 53, entirely new responsibilities may exist.
By 63, the worker may be learning tools that were unimaginable when the career began.
The old model was:
Learn → Work → Retire.
The emerging model looks more like:
Learn → Work → Relearn → Adapt → Work differently → Relearn again.
LinkedIn’s research identifies adaptability among rapidly growing workplace competencies in countries including Australia, France and the United States.
The ILO similarly highlights adaptability, resilience and agility as critical for workers confronting increasingly uncertain labour markets.
This does not mean workers should calmly accept every disruption imposed upon them.
Organizations and governments also have responsibilities.
But individuals who can learn new systems without interpreting change as a threat to their identity will possess an enormous advantage.
Skill #7: Judgment
There is a difference between intelligence and judgment.
Intelligence can identify possibilities.
Judgment decides what should be done.
Imagine an AI system recommending that a company eliminate 30% of its workforce because automation could reduce labour costs.
The mathematical case may be convincing.
But leadership must consider questions the calculation does not resolve.
What will happen to institutional knowledge?
What happens to employee morale?
What are the ethical responsibilities toward workers?
Will customer service deteriorate?
Could reskilling produce better long-term outcomes?
How will the community be affected?
Will the company’s reputation suffer?
Technology can provide analysis.
Leadership requires judgment.
This distinction becomes increasingly important when algorithms influence decisions involving hiring, healthcare, education, finance, policing, insurance and public administration.
The technically optimal decision is not automatically the socially legitimate decision.
Human beings remain responsible for consequences.
Skill #8: Ethical reasoning
Every powerful technology eventually forces society to confront the same question:
Just because we can do something, does that mean we should?
AI makes that question unavoidable.
Should an employer use AI to monitor workers continuously?
Should a school use facial recognition?
Should universities permit unrestricted AI-generated assignments?
Should hospitals allow algorithms to influence treatment?
Should a company train models using artists’ work without consent?
Should an autonomous weapon select a target?
Should an insurance algorithm make decisions without meaningful human explanation?
Technical skill alone cannot answer these questions.
They require philosophy, law, sociology, psychology, political judgment and ethics.
UNESCO’s AI Competency Framework for Students explicitly places a human-centred mindset and AI ethics alongside technical knowledge and AI system design. The framework treats students not merely as technology users but as responsible participants in societies increasingly shaped by intelligent systems.
UNESCO’s corresponding framework for teachers similarly emphasizes human agency, accountability and social responsibility.
This is an important signal.
The future will not need only people capable of building powerful technologies.
It will need people capable of deciding how powerful technologies should be used.
Skill #9: Leadership
Leadership has sometimes been confused with authority.
AI may expose the difference.
Managers traditionally possessed informational advantages.
They received reports.
They analyzed performance.
They made plans.
They distributed information downward.
But AI increasingly democratizes access to information and analysis.
An employee may soon have access to analytical capabilities once reserved for senior executives.
So what remains uniquely important about leadership?
Direction.
Trust.
Meaning.
Decision-making under uncertainty.
Conflict resolution.
Team development.
Courage.
Accountability.
The ability to bring people together around a purpose.
The World Economic Forum identifies leadership and social influence among both today’s important core competencies and those expected to rise in significance.
LinkedIn has also reported rapid growth in AI literacy among senior leaders, suggesting that effective leadership increasingly requires both technological fluency and the ability to guide organizations through technological change.
Tomorrow’s effective leader may therefore need to understand machines extremely well—
while understanding people even better.
Skill #10: Curiosity
Curiosity rarely appears on traditional résumés.
Perhaps it should.
In a stable environment, knowing the established way of doing something may be sufficient.
In a rapidly changing environment, the valuable employee is often the person who asks:
Why are we doing it this way?
Is there a better method?
What has changed?
What could this technology allow us to do?
What are competitors learning?
What don’t I understand yet?
What happens if our assumptions are wrong?
The World Economic Forum identifies curiosity and lifelong learning among the capabilities expected to grow in importance through 2030.
Curiosity drives adaptation because curious people voluntarily enter unfamiliar territory.
They do not wait until change becomes mandatory.
They explore first.
The rise of the “centaur worker”
An interesting idea emerged from the world of chess.
When computers became strong enough to defeat elite chess players, many assumed the game illustrated a simple future:
machine defeats human.
But another form of competition produced a more interesting result.
Humans working with computers could sometimes outperform either humans or machines working alone.
The combination became known metaphorically as centaur chess: part human, part machine.
The workplace may increasingly operate this way.
The question may not be:
Can a human accountant outperform AI?
but:
Can an accountant using AI outperform one who doesn’t know how to use it?
Not:
Can AI replace a researcher?
but:
Can a researcher who understands AI conduct parts of research faster and more effectively?
Not:
Can AI replace a teacher?
but:
Can a teacher use AI to reduce administrative burden and devote more attention to students?
This produces an important career principle.
The greatest threat may not always be AI replacing humans.
Sometimes it may be humans who use AI effectively replacing humans who do not.
But that should not become an excuse for technological determinism.
Tools become useful only when they are paired with expertise.
A beginner with AI does not automatically become an expert.
A person who knows nothing about medicine cannot safely become a physician simply because a chatbot provides medical information.
Domain knowledge still matters.
Indeed, because AI can confidently produce errors, expertise may become more important—not less.
What happens to entry-level jobs?
This is where the skills revolution becomes more complicated.
Many professional careers historically depended on junior employees performing routine work.
Young lawyers reviewed documents.
Junior accountants prepared basic analyses.
New journalists summarized information.
Beginning programmers wrote relatively straightforward code.
Research assistants organized sources.
These tasks helped beginners learn the profession.
But many are precisely the tasks AI can perform efficiently.
If employers automate entry-level work, a difficult question emerges:
How does someone become experienced if the tasks that once produced experience disappear?
This could become one of the most important organizational-development challenges of the AI era.
Companies cannot expect workers to become experts magically.
If technology removes apprenticeship tasks, organizations may need to deliberately redesign professional learning.
Mentorship.
Simulations.
Rotational assignments.
Supervised decision-making.
Case-based learning.
Work-integrated education.
Structured apprenticeships.
These may become increasingly important.
The future of talent development may therefore require employers to think like educational institutions.
The Global South cannot afford to become only a consumer of AI
The skills revolution also has a geographical dimension.
Much AI infrastructure is concentrated in wealthy economies and major technology companies.
Many developing countries face barriers involving internet connectivity, electricity, computing infrastructure, access to advanced education and research funding.
This creates a danger.
Countries may become large markets for AI technologies developed elsewhere without developing enough capacity to shape, adapt or create those technologies themselves.
That would deepen technological dependency.
AI skills strategies therefore cannot focus exclusively on individual workers.
Countries need ecosystems.
Schools that teach digital literacy.
Universities capable of research.
Technical colleges.
Reliable connectivity.
Local-language technologies.
Entrepreneurial ecosystems.
Data infrastructure.
Teacher training.
Public-sector capacity.
Investment in science.
The ILO’s 2026 analysis emphasizes that AI’s effects on skills vary across occupations and labour markets and argues that skills policies should emphasize broad capabilities, inclusion and human agency rather than narrow technical training alone.
For Africa, Latin America, South Asia and other developing regions, this distinction is particularly important.
The goal should not merely be:
Teach people how to use foreign AI systems.
It should increasingly become:
Develop people who can understand, adapt, govern and eventually create technologies appropriate to their own societies.
The danger of creating a new global skills divide
Artificial intelligence could democratize opportunity.
A student in rural Africa can access explanations from sophisticated AI tutors.
A small company can use tools previously available only to corporations.
A researcher can accelerate literature reviews.
A farmer could receive increasingly precise agricultural information.
A local entrepreneur can communicate internationally.
These possibilities are extraordinary.
But the same technology could widen inequality.
Imagine two students.
Student A attends a well-funded school with broadband internet, AI-enabled learning platforms, trained teachers, modern computers and parents with advanced education.
Student B attends a school with unreliable electricity, overcrowded classrooms and limited internet access.
Both technically live in the “AI era.”
But they are not participating in the same revolution.
The skills divide may therefore become as consequential as the digital divide.
Access to AI is one issue.
Knowing how to use it meaningfully is another.
What should schools teach?
Education systems face a paradox.
They must prepare students for jobs that may change before those students graduate.
That makes narrow occupational preparation insufficient.
Schools need foundational knowledge.
Students still need mathematics.
Language.
Science.
History.
Geography.
Arts.
But they increasingly also need competencies that travel across occupations:
research;
communication;
collaboration;
digital literacy;
AI literacy;
financial literacy;
critical thinking;
problem-solving;
media literacy;
creativity;
ethical reasoning;
self-regulation.
UNESCO’s framework proposes that students progress from understanding AI to applying it and ultimately participating creatively in AI-related systems, while keeping human-centred principles and ethics at the core.
That suggests an important shift.
Schools should not simply teach students what to know.
They must increasingly teach students how to learn.
Because no curriculum can anticipate every technology those students will encounter over a 40-year career.
What should universities teach?
Universities face the same problem at a more advanced level.
A computer-science degree cannot consist solely of learning today’s programming tools.
A business degree cannot revolve around management practices designed before generative AI.
A journalism programme cannot ignore synthetic media.
Teacher education cannot pretend students will never use AI.
A psychology programme cannot ignore algorithmic decision-making in workplaces and healthcare.
A law programme cannot ignore automated legal research.
Every discipline will increasingly need to ask:
What does expertise in this field look like when AI is always available?
That could be the defining curriculum question of the next decade.
What should employers do?
There is also a danger in telling workers simply to “upskill.”
The phrase can become convenient.
A company introduces disruptive technology.
Employees become anxious.
Management says:
“You need to upskill.”
Then workers are left to finance and organize the transition themselves.
That approach is neither sustainable nor particularly rational.
If companies benefit from technological transformation, they also have an interest in developing the people expected to operate the transformed organization.
The World Economic Forum reports that businesses increasingly recognize the importance of reskilling and upskilling initiatives as skills requirements evolve.
Effective organizations may need to provide:
paid training;
mentorship;
internal mobility;
AI education;
protected learning time;
career-transition pathways;
and opportunities for employees to experiment safely with emerging technologies.
Training should not be charity.
It is investment in organizational capability.
What should workers do?
Individuals cannot control the pace of technological change.
But they can influence how prepared they are for it.
A useful strategy may be to build what we might call a skills portfolio.
Instead of identifying yourself entirely through one job title—
I am an accountant.
I am a teacher.
I am a journalist.
I am an engineer.
—you might identify a broader set of transferable capabilities.
For example:
I can analyze complex information.
I can communicate clearly.
I understand this industry.
I can manage projects.
I can work across cultures.
I can use AI tools responsibly.
I can lead teams.
I can learn new systems.
That identity is more resilient.
Jobs change.
Capabilities travel.
Stop asking children, “What job do you want?”
Perhaps we are asking young people the wrong question.
Adults often ask children:
“What do you want to be when you grow up?”
Doctor.
Teacher.
Engineer.
Pilot.
Lawyer.
Entrepreneur.
Those answers assume relatively stable occupational identities.
But today’s child may hold several careers.
Some jobs may not yet exist.
Others may disappear.
Perhaps we should ask different questions:
What are you curious about?
What problems would you like to solve?
What are you willing to become very good at?
How do you work with other people?
What can you create?
How do you respond when something changes?
What values will guide your decisions?
What do you want to contribute?
Those questions prepare people for more than one occupation.
They prepare them for a life.
The most valuable worker may become the best learner
For much of modern economic history, expertise accumulated relatively slowly.
Workers could learn a profession and practice largely similar methods for decades.
That model is weakening.
The most valuable employee may increasingly be the person who can repeatedly transform unfamiliarity into competence.
That requires humility.
Curiosity.
Discipline.
And something that formal education sometimes unintentionally suppresses:
the willingness to say,
“I don’t know yet—but I can learn.”
That may be one of the most valuable sentences in the future economy.
Will technical skills still matter?
Absolutely.
There is a danger of romanticizing “human skills” as though technical expertise no longer matters.
It does.
AI and big data rank at the top of the World Economic Forum’s fastest-growing skills, followed by networks and cybersecurity and technological literacy.
The OECD similarly emphasizes growing needs for data-related capability and highly skilled workers as AI adoption expands.
Tomorrow’s economies will need:
engineers;
data scientists;
cybersecurity professionals;
robotics specialists;
AI researchers;
software developers;
technicians;
and workers capable of operating sophisticated technologies.
But the emerging pattern is not technical skills versus human skills.
It is technical skills plus human skills.
A technically brilliant engineer who cannot communicate may struggle to lead.
A charismatic manager who cannot understand basic technology may make poor strategic decisions.
A physician who knows medicine but ignores data may fall behind.
A data scientist who understands algorithms but not ethics may build harmful systems.
The future belongs increasingly to combinations.
The skills revolution is really a capabilities revolution
This distinction matters.
A skill can be narrow.
A capability is broader.
Knowing one software program is a skill.
Being able to learn unfamiliar technologies is a capability.
Writing one type of report is a skill.
Communicating complex ideas clearly is a capability.
Operating an AI application is a skill.
Knowing when to trust an automated recommendation is a capability.
The ILO’s 2026 report explicitly argues for moving beyond narrow collections of job skills toward broader capabilities and human agency in a labour market shaped by technological uncertainty.
This is perhaps the most important insight of the skills revolution.
We cannot prepare humans for an unpredictable future by training them only for predictable tasks.
We must develop people capable of adapting.
The future is not humans versus machines
For decades, science-fiction stories imagined a dramatic confrontation:
humans on one side,
machines on the other.
The real future will probably be less theatrical and more complicated.
Humans and intelligent systems will increasingly work together.
The central question is therefore not simply:
What can machines do?
It is:
What should humans become exceptionally good at when machines are capable of doing more?
The answer appears increasingly clear.
Humans must become better at the things that require context.
Judgment.
Responsibility.
Imagination.
Leadership.
Trust.
Ethics.
Empathy.
Curiosity.
Meaning.
And the ability to recognize when a technically correct answer is still the wrong decision.
Becoming more human in an age of intelligent machines
There is something paradoxical about the AI revolution.
The more technologically sophisticated the world becomes, the more important deeply human qualities may become.
When information becomes abundant, discernment becomes valuable.
When content becomes easy to generate, originality becomes valuable.
When automation increases, adaptability becomes valuable.
When communication becomes automated, authenticity becomes valuable.
When algorithms make recommendations, judgment becomes valuable.
When technology moves faster, ethics becomes valuable.
When machines become more capable, humanity itself becomes something we must learn to cultivate deliberately.
Perhaps that is the real skills revolution.
Not teaching human beings how to imitate machines.
And not pretending machines will disappear.
But teaching people how to use extraordinary technological power without surrendering the qualities that make human contribution unique.
The workers who thrive in the decades ahead may not necessarily be the people who know the most today.
They may be the people most capable of learning tomorrow.
They will understand technology without worshipping it.
They will use AI without outsourcing their judgment to it.
They will collaborate with machines while remaining accountable to human beings.
They will know how to acquire new skills—but also how to think, communicate, lead, care, question and create.
Artificial intelligence will continue getting better at answering questions.
Our challenge is to become better at deciding:
Which questions are worth asking?
Which answers should we trust?
And ultimately:
What kind of future do we want these technologies to help us build?
That decision still belongs to us.
IRJAR Global Questions examines major issues shaping research, education, development, organizations and human life across the world. The series brings interdisciplinary research into conversation with perspectives from both the Global North and Global South.