Part 61 - What will be the most important role of humans?
As artificial intelligence can perform more and more tasks, human judgment, expertise, creativity, relationships and responsibility become more important.
Artificial intelligence can write.
Analyze.
Summarize.
Translate.
Program.
Compare.
Finding patterns.
Generate images.
Process huge amounts of information.
And make knowledge available in seconds.
Tasks that previously required hours of human labor can now be
some cases are completed in minutes.
It is therefore natural that one of the biggest questions in the AI debate is about work:
What are humans supposed to do when machines can do more and more?
But perhaps the question should be asked differently.
Not only:
What can AI take over?
But:
What becomes more valuable when AI can do more?
This opens up a much more interesting perspective.
Because artificial intelligence can produce an answer.
But a person must consider whether the answer is good.
AI can find patterns.
But someone has to understand what the patterns mean.
KI can suggest alternatives.
But someone has to choose.
AI can streamline a work process.
But someone must ask whether the work process should exist in its
current form.
AI can help the customer.
But someone has to decide what good customer service actually means.
AI can calculate.
But people must still take responsibility for the consequences.
Thus, the most important role of humans in the AI age may become less important.
related to the production of each individual response and more related to:
judgment,
professionalism,
creativity,
critical thinking,
relationships,
cooperation,
empathy,
curiosity,
responsibility,
and the ability to learn.
That doesn't mean that all routine tasks disappear.
It also doesn't mean that technology automatically makes work better.
But the division of labor between humans and technology can change
fundamentally.
And then we have to understand what people add that not only can
reduced to faster information processing.
Artificial intelligence is changing tasks before it necessarily replaces entire professions
The future of working life must be understood through tasks, competences and new combinations between people and technology.
A job is rarely one task.
A teacher doesn't just teach.
A nurse doesn't just measure.
An engineer doesn't just calculate.
A store employee doesn't just sell.
A leader doesn't just decide.
A customer service representative doesn't just answer questions.
Occupations consist of many different tasks.
Some are routine.
Others require professional expertise.
Some require physical presence.
Others require relationships.
Some can be automated.
Others can be supported by KI.
And some may become more important precisely because technology takes over parts
of the rest of the work.
Therefore, it may be misleading to discuss the future of work only as:
human or machine.
The more interesting question is:
How are tasks divided between humans and technology?
AI augmentation can make human expertise more valuable when the technology is used as an amplifier
Artificial intelligence can free up time from routine work and give people greater capacity for assessment, problem solving and value creation.
Think of a professional who spends several hours finding information
in documents.
If AI can reduce this work to minutes, the business has a choice.
It can use the profits to produce more documents.
Or it can spend its time on what the professional is actually good at:
analyze,
consider,
talk to people,
solve problems,
develop new ideas,
and improve the service.
This is the difference between automation and augmentation .
Automation asks:
What can technology do instead of humans?
Augmentation asks:
What can humans do better with the help of technology?
Both aspects will likely affect working life.
But the second approach opens up an important thought:
AI doesn't just need to reduce the need for human labor.
It can also make human skills more productive .
Human judgment becomes more important when artificial intelligence can produce many plausible answers
Being able to generate a response is not the same as knowing which response should be used
Generative AI can present an answer with impressive linguistic certainty.
But the world is full of situations where there is no obvious right answer.
A leader must balance:
economy,
employees,
customers,
risk,
ethics,
time,
and long-term consequences.
A doctor may have extensive data, but still must consider a
person in a specific situation.
A customer service representative may have rules, but meet with a customer whose problem does not fit into the standard process.
A teacher can get suggestions from KI, but must understand the student.
This requires judgment .
Discernment is not just about having information.
It is about using knowledge and experience in a concrete
context where several considerations must be considered.
"Knowledge is a process of piling up facts; wisdom lies in their simplification.” — Martin H. Fischer
In a world where facts, proposals and formulations are becoming cheaper to produce, the ability to assess what actually matters may become all the more important.
Professional competence becomes more important when humans are to control and challenge artificial intelligence
You need to know something to detect when a convincing AI answer is actually wrong
There is a paradox in the notion that AI makes expertise
less necessary.
The more we use AI for professional tasks, the more important it may become that someone can control the result.
An experienced lawyer may discover that an argument is missing a crucial premise.
An engineer can see that a proposal does not work in practice.
An electrician may respond to dangerous advice.
An economist may discover that the model is based on an incorrect assumption.
An experienced salesperson can understand that the recommendation does not suit the customer.
If no one in the organization has the expertise to detect the errors,
can a seemingly effective AI system make the business
quicker to make bad decisions .
Therefore, professional competence is not just something people use to perform the task themselves.
It will also be necessary to:
ask good questions,
consider answers,
detect errors,
understand uncertainty,
and know when AI should not be used.
Critical thinking and source criticism become core competencies as generative AI increases the amount of information
Future employees must be able to distinguish between plausible formulations, documented knowledge and uncertain claims
We have returned to this several times throughout the KI series.
More information is not the same as more knowledge.
Generative AI makes it possible to produce massive amounts of well-crafted content.
It increases the need for people who ask:
Where does this come from?
What is the claim based on?
Is there documentation?
Is the source credible?
Is the information current?
What's missing?
Are there other explanations?
What don't we know?
This is critical thinking .
And paradoxically, a technology that makes it easier to get answers can
make the ability to ask critical questions more important.
Creativity in the AI age is about more than producing text, images and ideas quickly
Human creativity is also about understanding problems, breaking patterns, and imagining something that doesn't yet exist.
Generative AI can produce a hundred ideas in seconds.
That's impressive.
But innovation is not just about the number of ideas.
It's about understanding:
what problem is worth solving,
why existing solutions are not good enough,
what needs people have,
what opportunities the technology opens up,
and which ideas should actually be realized.
Human creativity is closely linked to experience, curiosity, culture and context.
It can arise when someone asks:
Why do we actually do it this way?
What if we turn the problem on its head?
What does the customer need that she is unable to articulate herself?
What can we do that no one has asked us to do yet?
AI can be a powerful creative sparring partner.
But man must still be able to see which problem deserves creativity .
"Creativity is thinking up new things. Innovation is doing new things." — Theodore Levitt
The difference becomes important in the AI age.
It becomes cheaper to generate ideas.
The ability to translate good ideas into actual value creation remains challenging.
Empathy and relational competence become more important as more transactions and routine tasks are automated
Human contact creates value when the situation requires understanding, security, trust and care.
Not all human encounters should be automated.
Sometimes the customer just needs a quick answer.
Then AI can be excellent.
Other times the situation is difficult.
A customer is frustrated.
A patient is scared.
A relative is worried.
An employee experiences a conflict.
A resident is in a difficult life situation.
Then it may be crucial to meet another person.
Not because humans necessarily have more information than machines.
But because the relationship itself has value.
To listen.
Understand.
Interpret the situation.
Show respect.
Create security.
Build trust.
And sometimes simply being present.
The more of the routine that is automated, the clearer the value of these human encounters can become.
Responsibility cannot be automated away even if the decision support becomes intelligent
Businesses and people must still be responsible for how artificial intelligence is used
KI can suggest.
Analyze.
Prioritize.
Ranks.
Recommend.
But when an AI system is used in a business, responsibility does not disappear into the model.
Someone has decided that the system should be used.
Someone has selected the data.
Someone has defined the work process.
Some have determined the degree of human control.
Someone has to deal with the consequences if something goes wrong.
This makes accountability a central human role.
The more decision support we leave to technology, the more important it becomes to know:
Who is responsible?
Who can override?
Who controls?
Who is following up?
And who can explain why the business did what it did?
Technology can automate actions.
It cannot automate away the business's responsibilities .
The ability to ask good questions becomes more important as artificial intelligence makes answers cheaper
Problem formulation can become a scarcer skill than the production of the answer itself
Throughout large parts of the knowledge society, it has been costly to produce answers.
Find information.
Analyze it.
Write.
Rain.
Visualize.
Program.
AI reduces the cost of many of these activities.
Then another part of the knowledge work may become relatively more important:
To define the problem.
What are we really trying to understand?
What assumptions do we make?
What is the real customer need?
What is missing from the analysis?
What question has no one asked?
What should we not optimize?
What will happen if our premises are wrong?
A bad question can produce an impressively bad answer.
A good question can open up a whole new way of understanding the problem.
This is about far more than prompt engineering .
It's about curiosity, professionalism and the ability to formulate problems.
Learning ability becomes crucial when both technology and work tasks are constantly changing
The competence of the future is not just about what the employee knows today, but how quickly new knowledge can be developed.
No one can learn "AI" once and be done.
The models change.
The tools change.
Work processes are changing.
The regulation is being developed.
Customer expectations are changing.
And new uses are emerging.
Therefore, continuous learning becomes part of working life itself.
This applies to both technological and professional competence.
The employee must be able to learn new tools.
But also understand when they should not be used.
She must be able to combine new technology with existing ones
professional knowledge.
And she must be able to reject working methods that no longer make sense.
"The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn." — Alvin Toffler
In a world of rapid technological change, the ability to learn is not just about personal development.
It becomes part of the company's competitiveness.
Collaboration between humans and AI becomes a new form of division of labor
The employee of the future must understand both what technology does well and where human expertise creates the greatest value.
It's easy to make two camps.
Man versus machine.
The optimists versus the pessimists.
But working life will likely become far more complex.
An employee can use AI for the first draft.
Then use your own professional expertise to improve it.
Another can use AI for analysis and interpret the results themselves.
A third can automate routine tasks and spend more time with
customers.
A fourth can use AI as a sparring partner in idea development.
A fifth can quality assure AI-generated work.
This means that AI literacy becomes important.
Not just being able to use a specific tool.
But to understand:
what AI can do,
what it cannot do,
what data it uses,
what mistakes it can make,
how the results should be assessed,
and when human control is necessary.
Leadership in the AI age is about people, organization and direction – not just technology
Leaders must create frameworks where employees can experiment, learn, and use AI responsibly
A business does not become AI-mature because management buys licenses.
Employees must understand why the technology is being used.
They must be given the opportunity to learn.
They need to know what rules apply.
They must be able to report when the systems are not working.
They must be able to share experiences.
And the business must tolerate the failure of some experiments.
This requires leadership.
Not just technology management.
But people management through technological change .
If employees experience AI solely as a control or
downsizing tools, it can affect both trust, learning and the will
to share knowledge.
If, however, technology is used to support employees in solving tasks better, a different dynamic can arise.
"Culture eats strategy for breakfast." — commonly attributed to Peter Drucker
Regardless of the origin of the phrase, it points to an important point:
The technology strategy always meets the actual culture of the organization.
Human experience and tacit knowledge become part of the business's strategic capital
Artificial intelligence makes it possible to make more knowledge available, but the knowledge must first exist.
Think about the experienced employee.
She sees a problem and responds immediately.
Not because she has looked up the answer.
But because she has seen similar situations a hundred times.
She knows the pattern.
She knows what questions need to be asked.
She knows when standard procedure works.
And when it doesn't.
This is tacit knowledge .
Experience that cannot always be reduced to an instruction.
The business may attempt to document parts of it.
AI can help structure and make it available.
But we should not confuse the representation of experience with
the experience itself.
People continue to learn through practice.
And that is precisely why the workplace also becomes a place where new knowledge
is created.
Human value creation becomes clearer when routine production is automated
The value of work can be shifted from producing more to understanding better, improving more, and taking greater responsibility.
If AI can produce the first draft, humans can spend more time on quality.
If AI can find the information, humans can spend more time on the assessment.
If AI can handle the standard questions, humans can spend more time on the difficult conversations.
If AI can analyze the patterns, humans can spend more time understanding the consequences.
If AI can automate the routine, humans can spend more time on the improvement.
There is no guarantee that working life will actually develop in this way.
Businesses can also use technology primarily for cost cutting, control or higher work pace.
Therefore, technology is not destiny.
How the profits are used is an organizational and societal choice.
Invisible capital may become more visible in an economy with artificial intelligence
Competence, experience, relationships, trust and organizational culture may become more important as technology becomes easier to copy.
A competitor can buy the same AI tool.
It can subscribe to the same model.
It can acquire similar software.
But it can't be bought as easily:
thirty years of experience,
customer trust,
employee collaboration,
the company's culture,
historical learning,
the professional community,
the relationships,
or the understanding that has grown through thousands of meetings
between people.
These are parts of the business's invisible capital .
And as the technology itself becomes more readily available, such
complementary human resources gain greater strategic
importance.
Magne's perspective – technology has changed many times, but people have created the values each time
From telephones and the internet to e-commerce, digitalization and artificial intelligence, technology has been a tool
Magne has worked through several major technological shifts.
The tools have changed dramatically.
But behind good digital solutions there have always been people.
Someone has understood the customer.
Someone has had the idea.
Someone has written the code.
Someone has designed the solution.
Someone has tested.
Someone has explained.
Someone has challenged.
Someone has said:
“This is not working well enough.”
And some have taken responsibility to make it better.
Artificial intelligence is changing how much technology can contribute.
But it does not eliminate the need for people who know why we do something and who we do it for .
The buddy's perspective – the best use of artificial intelligence may be to make people better
AI should not be judged by how human it seems, but by how much value humans can create with it.
The buddy doesn't have to become human.
That's not the goal either.
The friend can do something else.
Be available.
Process information.
Finding connections.
Create drafts.
Challenge an argument.
Suggest alternatives.
Help with structuring thoughts.
And maybe ask the question that makes man think for once
to.
Then AI will not be the main character.
It becomes the tool around man .
And perhaps that's a better ambition than trying to build technology that pretends to be us.
Technical terms for human competence, AI augmentation and the future of work
The concepts that describe the new division of labor between humans and artificial intelligence
AI Augmentation - The use of AI to enhance human capacity, competence or productivity.
Automation – Automation- The use of technology to perform tasks with reduced human involvement.
Human Judgment - The ability to consider information, context, experience, values, and consequences before making a decision.
Critical Thinking - Systematic evaluation of information, arguments, assumptions, sources and alternative explanations.
AI Literacy - Understanding how AI can be used, what limitations the technology has, and how results should be critically assessed.
Tacit Knowledge - Experience-based knowledge that is often difficult to formulate or fully document.
Explicit Knowledge – Knowledge that is documented and can be shared through text, data, models or other forms of expression.
Metacognition – The ability to reflect on one's own thinking, learning and understanding.
Problem Solving – The process of understanding a problem, developing alternatives, evaluating them, and implementing solutions.
Continuous Learning - Ongoing development of knowledge and skills through work, experience and education.
Human-in-the-loop - A working model where humans actively participate in assessment, control or decision-making regarding the AI system's results.
Relational Competence - The ability to understand, communicate and collaborate with other people in a way that builds good relationships.
Complementary Skills - Human skills that complement technological capabilities and together create greater value.
Accountability - That people and organizations can be held responsible for decisions, actions and consequences.
The most important role of a person will not be one skill – but the ability to combine knowledge, judgment and responsibility
Future value creation occurs when humans use artificial intelligence without handing over human responsibility to technology.
So what will be the most important role of humans?
There is hardly one answer.
It's not just creativity.
Not just empathy.
Not just professional competence.
Not just critical thinking.
Not just management.
Not just relationships.
The interesting thing lies in the combination .
Humans can understand context.
Use experience.
Ask questions.
Consider consequences.
Create relationships.
Challenge established truths.
Learn.
Take responsibility.
And decide what is worth doing.
Artificial intelligence can enhance much of this.
But it requires the business to develop its people at the same time as it develops its technology.
From the role of the human being to the question of how the entire business should be built
The final challenge is to connect people, knowledge, learning, customers and artificial intelligence into one functioning system.
We are now approaching the end of the AI journey.
We have researched the technology.
We have seen how it finds and processes information.
We have been working on content.
Websites.
Knowledge.
E-commerce.
Product information.
Customer advice.
We have asked what happens when everyone gets AI.
We have seen how customer expectations can change.
And now we have returned to man.
It's no coincidence.
Because the more capable technology becomes, the more important it becomes to understand how people and technology should be organized together .
It's not enough to buy AI.
It is not enough to collect data.
It is not enough to document knowledge.
It is not enough to send employees on courses.
Everything must be connected.
People must be able to learn.
Knowledge must be shareable.
Customers must be understood.
Technology must be used with purpose.
Management must create direction.
The organization must be able to change.
And the experiences must be used to make the system better.
This leaves only one question remaining in the entire KI series.
How do we build the knowledge business of the future?
That's where we're going to gather everything.
Not to end with artificial intelligence.
But to end where the whole Invisible Capital really begins:
with people who learn, share knowledge and create value together.
Academic background and further reading
This series also builds on my own professional journey through the Web Design study , the eMarketing study , Innovation and Commercialization and professional seminars in San Francisco and Oxford . Here you will find the background, professional environments and experiences that have followed the development from the early years of the web to today's digitalization.
Recommended literature
Developments in artificial intelligence are moving faster than perhaps any other field of study in our time. No single book can provide all the answers, but good books can provide a solid foundation for understanding the technology, the opportunities, and the challenges.
In the KI-Kompis series, we therefore recommend a selection of books that illuminate artificial intelligence from different perspectives – technology, strategy, management, innovation, ethics, digitalization and practical application. Together, they provide a broader understanding of how artificial intelligence affects people, businesses and society.
Click on the book icon to see the full literature overview with recommended books on artificial intelligence.
Recommended books from our library
Build a Large Language Model (From Scratch)
Author: Sebastian Raschka
Short review
This book takes the reader behind the scenes and shows how a modern language model is actually built – step by step. Sebastian Raschka explains advanced concepts in an educational way and provides a unique understanding of how large language models like ChatGPT work. Although the book contains code examples, it is also very valuable for anyone who wants a deeper understanding of the technology behind artificial intelligence.
Why we recommend the book
One of the most talked about books on large language models. Perfect for those who want to understand how artificial intelligence works beneath the surface and why language models have become a revolution in digitalization and knowledge sharing.
Quick Start Guide to Large Language Models
Author: Janelle Shane
Short review
This book provides a practical and easy-to-understand introduction to large language models (LLMs). Sinan Ozdemir explains how language models are used in modern businesses, how they can be integrated into work processes, and why they have become one of the most important technologies in artificial intelligence.
Why we recommend the book
A very good book for anyone who wants a quick and practical introduction to language models. It is suitable for both beginners and professionals who want to understand how LLMs are used in practice.
Artificial Intelligence: A Modern Approach: The Future Is Coming! Discover How Artificial Intelligence Will Change Your Life!
Authors: Stuart Russell & Peter Norvig
Short review
This is the world's most famous textbook on artificial intelligence and is used in universities worldwide. The book covers the entire subject area – from problem solving and machine learning to language understanding, robotics and ethics – and is considered a classic in the AI field.
Why we recommend the book
If you are only going to own one academic book on artificial intelligence, this is one of the very best choices. A timeless classic that provides a solid academic understanding of artificial intelligence and is still used as a syllabus at leading universities around the world.
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