Part 59 - What happens when everyone uses artificial intelligence?
When artificial intelligence becomes common knowledge, the competitive advantage shifts from access to technology to knowledge, people and execution.
Artificial intelligence is still new enough that simply using the technology can feel like a competitive advantage.
A business uses an AI assistant.
Another automates parts of customer service.
A third uses AI for product descriptions.
A fourth analyzes large amounts of information in minutes.
And a fifth is building digital customer advisors.
But what happens when everyone does it?
When the competitor has access to the same language models?
When the customer uses artificial intelligence before visiting your website?
When does the supplier use AI?
When do employees use AI?
When small businesses gain access to tools that previously required
large technology environments?
When artificial intelligence is no longer something special, but simply part of the digital infrastructure around us?
Then the question changes.
It becomes less interesting to ask:
Does the business have artificial intelligence?
The more important question becomes:
What is the business able to do better than others when everyone has access to artificial intelligence?
And right there begins the final part of our AI journey.
Artificial intelligence is gradually becoming a general technology that more and more businesses have access to.
Access to AI is not the same as a lasting competitive advantage
Throughout the history of technology, we have seen this several times.
When the internet came along, having a website was special.
Later, almost every business had a website.
It was no longer the website itself that distinguished the businesses.
The difference was in what they did with it.
When the smartphone became a natural part of customers' everyday lives, it was no longer enough to say that the business was "mobile."
As cloud services became available to more and more people, access to computing power alone did not become a lasting competitive advantage.
The same thing can happen with artificial intelligence.
When the same or similar basic models and AI tools become available to millions of people and businesses, the value of access alone diminishes .
A competitor can buy access to the same model.
A small business can use advanced AI tools without building them themselves.
An entrepreneur can gain access to analysis, programming, and production capabilities that previously required far more resources.
Technology can thus help democratize capacity.
But that is precisely why the competition is being moved forward.
"The essence of strategy is choosing what not to do." —Michael E. Porter
When many people have access to the same tools, strategic choices become more important – not less important.
AI commoditization makes the company's own resources more important
When technology becomes accessible to everyone, the uniqueness of the technology becomes more valuable.
In economics and strategy, the term commoditization is used to describe the development in which something that was previously differentiating becomes widely available and more difficult to use as a unique competitive advantage.
That doesn't mean artificial intelligence is becoming unimportant.
On the contrary.
AI can become very important at the same time as access to general AI becomes less unique .
Then we have to look for the differences elsewhere.
They can be located in the business's:
professional competence,
proprietary and quality assured data,
customer insights,
work processes,
organizational culture,
brand,
relationships,
innovative ability,
trust,
distribution,
experiential knowledge,
and ability to implement changes.
Two businesses can use the same AI model and get completely different results.
Why?
Because the technology is part of two different organizations.
Artificial intelligence amplifies differences in organization, expertise and work processes
The same AI model can create different value in different businesses
Imagine two competing companies.
Both purchase access to the same AI tool.
In the first business, the product data is outdated.
The knowledge is scattered in emails and documents.
Employees do not know which sources are authoritative.
Customer data is rarely analyzed.
The work processes are unclear.
No one has clear responsibility for quality assurance.
In the other business, the product data is well structured.
The professional knowledge is documented.
Customer questions are used for learning.
Employees share experiences.
AI is used for clearly defined tasks.
The results are checked.
And the business systematically learns from what works and what doesn't work.
Both have AI.
But they do not have the same organizational capacity to create value with AI .
This is an important distinction.
Artificial intelligence can be powerful.
But technology does not work in a vacuum.
It meets the quality of the organization it is placed in.
Productivity with artificial intelligence is about more than producing faster
AI productivity must be measured in value creation, quality and better results – not just production volume
Artificial intelligence can make certain tasks significantly faster.
Texts can be produced faster.
Documents can be summarized.
Code can be suggested.
Data can be processed.
Questions can be answered.
But increased production is not necessarily the same as increased value creation.
If a business uses AI to produce ten times as much
content that customers don't need, it has increased production.
That doesn't necessarily mean it has created ten times more value.
If customer service responds faster but worse, speed alone is not a good result either.
The relevant question therefore becomes:
What actually got better?
Was the quality higher?
Did the customer make a better decision?
Did the employee become more productive?
Was the waiting time reduced?
Did knowledge become more accessible?
Were there fewer errors?
Did new products or services emerge?
Did employees have more time for tasks where human expertise creates greater value?
AI shouldn't just make business faster.
It should help the business improve .
AI augmentation can make human expertise more productive
Competitive advantage may lie in the interaction between human judgment and machine capacity
A large part of the AI debate has been about which tasks the technology can take over.
But there is another perspective.
What happens when AI augments human capacity ?
An experienced employee can use AI to analyze multiple options.
A customer service representative can find relevant knowledge faster.
A developer can work faster with code.
A professional can spend less time on the first draft and more time on assessment.
A small business can analyze volumes of data it previously couldn't
had the capacity to process.
This is often referred to as augmentation – technology that enhances
human work.
"The key is using AI to augment human capabilities, not replace them." — Erik Brynjolfsson
The interesting competitive advantage can therefore arise in the combination of:
human knowledge + AI capacity.
Not necessarily in one or the other alone.
When everyone can generate content, credible knowledge becomes a scarcer resource
Generative AI increases the amount of information and makes source criticism, professionalism and trust more important
Generative AI makes production cheaper.
It concerns text.
Pictures.
Video.
Presentations.
Code.
Analyze.
Summaries.
This allows the world to access vast amounts of new content.
But more information does not automatically mean more knowledge.
When the cost of producing a convincing response falls
dramatically, it becomes even more important to be able to ask:
Where does the information come from?
Is it correct?
Is it updated?
Who is behind it?
What competence is the basis?
Can the claim be documented?
What do we actually know?
What is uncertain?
Paradoxically, a world with more artificially generated
Content makes credible human and institutional knowledge more valuable .
Businesses that can document what they know, why they know it
and where the information comes from, can gain an important advantage.
The company's proprietary data and experiential knowledge can become strategic AI resources
General AI models may be available to everyone, while the company's own knowledge is not.
A general language model can know a lot about the world.
But it doesn't necessarily know the business's:
customers,
history,
products,
processes,
error,
experiences,
internal decisions,
service inquiries,
or the thousand little lessons the employees have learned
over many years.
There is an important difference here.
The general model can be common. The knowledge base does not have to be.
The company's own data and documented experiences can make general AI far more relevant for specific tasks.
But only if the knowledge is available, legally applicable, understandable, up-to-date and of sufficient quality.
This directly connects AI to knowledge management.
Tacit knowledge becomes more valuable when the business manages to make it accessible
Employee experience is part of the company's invisible capital
Here the KI series meets the core of The Invisible Capital .
Businesses are not just made up of buildings, technology, money and products.
They also consist of what humans can do.
The employee who knows the customer.
The engineer who understands why the product fails under certain conditions.
The customer service representative who knows the most common misunderstandings.
The salesperson who knows what questions need to be asked before recommending a product.
The leader who knows the history of the organization.
The professional who, over twenty years, has learned to distinguish between a standard response and the situation that requires judgment.
Much of this knowledge is tacit knowledge .
It is not necessarily in the database.
AI can make it more valuable to document parts of this knowledge and make it available where appropriate.
But AI does not automatically create the experience on which the knowledge is based.
It still comes from people.
Organizational learning becomes a competitive advantage in the AI economy
Businesses that learn faster can get more value from the same artificial intelligence
If AI technology is developing rapidly, it may be difficult for the business to treat AI implementation as a one-time project.
It's not enough to install a tool and declare the transformation complete.
Employees must learn.
Work processes must be adjusted.
Errors must be detected.
Knowledge base must be improved.
New opportunities must be considered.
Old ways of working must be challenged.
And some AI experiments must be discarded because they do not create value.
This makes organizational learning important.
The business that learns faster from technology can develop better
work processes than the competitor – even though both use the same
AI.
"The ability to learn faster than your competitors may be the only sustainable competitive advantage." — Arie de Geus
The point is not that learning alone guarantees competitive advantage.
The point is that as technology changes rapidly, the organization's ability to learn, adapt, and put learning into practice becomes increasingly important.
AI democratization can give small and medium-sized businesses new competitive opportunities
SMBs can access analytics, knowledge work, and digital capabilities that previously required larger organizations
One of the most interesting aspects of generative AI is that advanced digital capabilities can become available to far more people.
A small business can use AI to:
process customer feedback,
analyze documents,
prepare marketing content,
structuring product data,
develop software,
build a knowledge base,
create a first draft,
or support customer service.
Tasks that previously required specialized departments can, in some cases, be performed by much smaller teams.
This doesn't mean that size stops mattering.
Large companies can still have significant advantages through capital, distribution, data, brand and expertise.
But AI can reduce the cost of certain knowledge tasks.
This allows small businesses to compete in areas where resource differences were previously greater.
When everyone uses AI, mediocrity can also scale
Standardized AI tools can lead to more consistent content, services, and customer experiences
There is also a less discussed consequence.
If a thousand businesses:
using the same models,
with similar instructions,
on similar data,
to produce similar content...
the results may also start to resemble each other.
The same formulations.
The same pictures.
The same recommendations.
The same "personal" emails.
The same smooth communication.
AI can thus both democratize creative capacity and create risk.
for digital homogenization .
Then distinctiveness becomes more important.
The business's own professionalism.
Own experiences.
Own voice.
Own ideas.
Own culture.
And the human understanding of what business actually is
wants to be.
Trust becomes a strategic competitive advantage as artificial content becomes harder to distinguish from human-made content
The customer must be able to trust both the information, the business and the way AI is used
When artificial intelligence can produce increasingly compelling content, a question of trust arises.
Can the customer trust the product information?
Is the recommendation based on the customer's needs or the business's margin?
Does the customer know when she is communicating with an AI?
Can the information be documented?
What happens to the data the customer shares?
Who is responsible when the system fails?
Businesses that take these questions seriously can build something technology alone can't buy:
trust over time.
And trust is harder to copy than a language model.
Responsible AI and KI governance moves from technical control to business management
Management must understand where AI is used, what decisions it affects, and who is responsible.
When AI is used by a few enthusiasts, the technology can be perceived as a tool.
When AI is used throughout the organization, it also becomes a management issue.
What data can be used?
Which systems are approved?
What should always be quality assured?
When does a human need to be involved?
How is privacy handled?
How are important decisions documented?
Who is responsible when something goes wrong?
This is often referred to as AI governance .
As usage grows, the business must develop both technical, legal,
organizational and ethical competence.
Responsibility cannot be delegated to the algorithm.
Competitive advantage is shifted from artificial intelligence to the business's overall capabilities
Strategy in the AI age is about how technology, people, knowledge and organization combine
Here we begin to see the answer to the question we started with.
What happens when everyone uses artificial intelligence?
AI is not disappearing as a competitive factor.
But it can become a basic capacity that many are expected to master.
Then competitive advantage arises to a greater extent through the combination of resources.
Technology.
Data.
Knowledge.
People.
Culture.
Processes.
Customer insights.
Innovation.
Trust.
Management.
And implementation.
This is close to a fundamental strategic point: It is rare
one isolated resource that explains a lasting competitive advantage.
It is more difficult to copy a fully functioning system than one
simple tool.
"Competitive strategy is about being different." —Michael E. Porter
When everyone can buy the hammer, the difference becomes who can build the house.
Magne's perspective – every technological shift makes human application more important
From the internet and e-commerce to digitalization and artificial intelligence
Magne has seen this story before.
Not the same technology.
But the same excitement.
The Internet was going to change everything.
E-commerce was going to change everything.
The mobile phone was going to change everything.
Social media was going to change everything.
Digitalization was going to change everything.
And now artificial intelligence is going to change everything.
In many areas, technologies did just that.
But one thing remained.
Technology did not create the values alone.
Some businesses used the internet brilliantly.
Others created a website.
Some understood e-commerce as a new way to create customer experiences.
Others put the product catalog online.
Some used digitalization to improve work processes.
Others digitized the old process without asking about it
should be done differently.
The same distinction can arise with AI.
There is a difference between using artificial intelligence and getting better with the help of artificial intelligence .
The buddy's perspective – AI can scale capacity, but not determine what is valuable
Artificial intelligence needs human goals, professional context and quality-assured knowledge
The friend's perspective is different.
Artificial intelligence can do things that previously took humans a long time.
It can analyze.
Compare.
Summarize.
Generate.
Classify.
Finding patterns.
Process huge amounts of information.
And make knowledge available in new ways.
But the fundamental challenge remains.
What do we want to achieve?
What problem is worth solving?
What is a good result?
What is true?
What is fair?
What does the customer need?
What risk is acceptable?
What should be automated?
What should a person decide?
Technology can contribute capacity.
Humans still have to define much of the direction, context, and responsibility .
The real AI transformation happens when work processes are redesigned
Businesses should not just add AI to old processes, but investigate how work can be better organized.
The easiest way to introduce AI is to place the technology into existing work processes.
We wrote ten product descriptions a day.
Now we use AI and write fifty.
But the more interesting transformation begins with another question:
Why do we do this this way?
Do we need fifty product descriptions?
Can product data be structured differently?
Can the same quality-assured knowledge be used in multiple places?
Can the customer find the answer themselves?
Can the employee receive better decision-making support?
Can information flow between systems in a smarter way?
Can the entire task be redesigned?
Then the business moves from AI as a tool to AI as
opportunity for organizational innovation .
The future of digital competition is about complementary resources around artificial intelligence
Data, expertise, customer insights, brands and people can be more difficult to copy than the AI model itself
When technology is widely available, the resources around it become crucial.
This is what economists and strategy researchers often describe as complementary resources or complementary capabilities .
AI becomes more valuable when combined with something else.
A language model + good product data.
A language model + subject competence.
A language model + customer insights.
A language model + good work processes.
A language model + a strong brand.
A language model + trust.
A language model + people who understand the problem.
Thus, the question does not become:
How good is our AI?
But:
How well does the whole system around it work?
From AI lead to learning lead
The most important difference may be how quickly the business learns to combine people, knowledge and artificial intelligence
The first AI advantage can come from being early.
The next step may come from using better models.
But over time, another type of advantage may become more important:
the learning advantage.
The business learns which tasks AI solves well.
It learns where technology fails.
Employees are developing new ways of working.
Customers show which services actually create value.
The data is improving.
The knowledge base is growing.
Work processes are redesigned.
And the lessons learned from one improvement are used for the next.
Then the competitive advantage is not just the technology, the business
have bought.
It lies in what the business has learned to do with it .
Technical terms for competitive advantage, AI strategy and organizational learning
The concepts that will become important when artificial intelligence becomes available to everyone
AI Democratization – That advanced AI capacity becomes available to more and more people and businesses, even without large internal technology environments.
Commoditization – A development in which technology or capacity becomes so widely available that access itself provides less differentiation.
Competitive Advantage – Conditions that allow a business to create greater or more lasting value than its competitors.
Complementary assets – Resources that make a technology more valuable, such as data, distribution, expertise, brand or customer insight.
Organizational capabilities – Organizational The overall ability of the business to coordinate resources, people and processes to achieve results.
Capabilities - The organization's overall ability to coordinate resources, people, and processes to achieve results.
Dynamic Capabilities - The ability of a business to detect changes, seize opportunities, and transform resources and working methods when the environment changes.
AI Augmentation – Using artificial intelligence to augment human capacity rather than simply automating or replacing tasks.
Organizational Learning – Processes in which the business develops and applies knowledge based on experience.
Tacit Knowledge – Experience-based knowledge that is often found in people without being fully documented.
Proprietary Data – Proprietary Data Data that the business itself controls or has exclusive access to, within relevant legal and ethical frameworks.
AI Governance Structures, roles and principles for responsible management, control and use of artificial intelligence.
Digital Homogenization – A possible development in which extensive use of the same digital tools contributes to content, services or expressions becoming more similar.
When everyone has artificial intelligence, the question becomes what does the business have that AI can't buy?
The technology can be shared – the knowledge, culture, relationships and execution skills do not have to be
We started this article with a question:
What happens when everyone uses artificial intelligence?
The answer is not that everyone will be equally good.
Rather, it may become clearer which businesses have actually built something around the technology.
Knowledge.
Competence.
Culture.
Customer insights.
Good data.
Trusting relationships.
Learning ability.
Innovation ability.
And people who can use technology with judgment.
Artificial intelligence can make knowledge more accessible.
But the business must have knowledge that is worth making available.
AI can make production faster.
But someone has to know what is worth producing.
AI can analyze customer actions.
But someone needs to understand what the business is going to do with the insights.
AI can generate alternatives.
But someone has to choose.
AI can help us answer.
But someone still has to ask the important questions.
Magne and Kompisen look towards the final part of their AI journey
When technology becomes commonplace, attention shifts back to the people who will create the value with it.
Magne started this journey with curiosity about artificial intelligence.
How does it work?
What can it do?
How does it find information?
How does it learn?
How does it affect websites?
What does it mean for content?
What does it mean for e-commerce?
Over time, Kompisen has become less interesting as a technological phenomenon and more interesting as a work tool .
Because now we know something we didn't know when the journey began.
The businesses of the future will likely not compete between those who use AI and those who do not in the same way as in the technology's first phase.
More and more people will use it.
Thus, the real question begins to shift.
From technology...
to the customer.
Because the customer also gets artificial intelligence.
The customer can apply differently.
Compare faster.
Investigate more.
Expect more relevant answers.
And perhaps meet the business with far more knowledge than
previous.
Thus, the next question in our final stage is inevitable:
How are customer expectations changing?
Because when artificial intelligence becomes available to everyone, it's not just business that changes .
The customer does too.
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.
© 2025 - 2026 Portico Publish | The invisible capital Privacy and use of the website | Developed and operated by Magne Bjella | Powered and secured by Wix
Comments