Will AI Build a Better Company or Break It? — Thinking About AI Utilization for Mutual Growth Between Employees and the Company
1. Will AI Adoption Truly Improve a Company?
The use of AI is spreading rapidly.
Writing documents.
Summarizing materials.
Creating meeting minutes.
Analyzing data.
Drafting customer responses.
Organizing meeting discussion points.
Preparing internal newsletters and training materials.
Brainstorming management issues.
These tasks can now be done in much less time than before.
AI is undoubtedly useful in the context of building a good company.
Interpreting the results of company health checks.
Articulating the thoughts of the president and executives.
Translating philosophy and vision into daily decision-making.
Reviewing meeting materials and evaluation comments.
Creating themes for dialogue with employees.
Organizing themes for three-line projects.
Supporting the review of improvement activities.
Looking at this, AI seems like a convenient tool to support building a good company.
However, I believe we need to pause here.
If we introduce AI, will the company automatically become better?
If document creation becomes faster, will the company improve?
If meeting minutes are organized, will dialogue deepen?
If evaluation comments become polished, will employees grow?
If the president can brainstorm with AI, will the company become self-sustaining?
If employees can use AI, will their capabilities truly increase?
I do not think it is that simple.
AI has the potential to significantly advance the building of a good company.
On the other hand, if used incorrectly, it could accelerate dependence on the president, increased management control, employee stagnation, distrust, and the dilution of relationships.
In other words, AI can either advance the building of a good company or break it.
We need to look at both sides of this.
2. AI Affects the Very Structure of a Company
In Growth Driver Theory, we view a company through five main drivers.
The President.
Management Philosophy and Vision.
Business Model.
Systematization and Standardization.
Behavioral Environment.
AI affects all five of these drivers.
In the President driver, AI organizes the president's thoughts and serves as a sounding board for decision-making.
Every day, the president faces questions that do not have easy answers.
Should you protect short-term profits?
Should you wait for employee growth?
Should you leave it to the executives?
Should you intervene in the front lines?
Should you stick to your philosophy?
Should you adapt to reality?
AI can organize these hesitations, present multiple options, verify risks, and suggest alternative perspectives.
This can be a great support for a president.
It may also slightly alleviate the loneliness of the president.
However, there is a danger here.
If only the president masters AI and the president's judgment becomes even stronger, it may actually increase dependency on the president.
The president creates policies with AI.
The president analyzes with AI.
The president refines messages for employees with AI.
The president creates instructions for executives with AI.
At first glance, the president's work becomes more sophisticated.
However, if executives and managers have less room to think and employees become passive, the company's overall ability to operate independently will weaken.
AI can help the president think.
However, it must not replace the dialogue between the president and executives, or between the president and employees.
The president becoming stronger through AI is not the same as the company becoming stronger.
3. Philosophy and Vision Become Even More Important in the AI Era
AI also has great power regarding management philosophy and vision.
AI can support the verbalization, reorganization, and daily standardization of your philosophy.
It can check whether meeting materials, evaluation systems, development policies, recruitment messages, and customer service are connected to your philosophy.
For example,
Is this decision in line with our philosophy?
Does this system support our philosophy?
Is this meeting material connected to our vision?
What words are best to explain this to employees?
Where might there be a misalignment with our philosophy?
You can think about these kinds of questions together with AI.
This is extremely effective.
Philosophy and vision do not function just by being stated.
They are only incorporated into the company's structure when they enter into daily decisions, meetings, evaluations, development, recruitment, customer service, and information sharing.
AI can support that repetition.
However, if you use AI, you can create as many beautiful philosophy statements as you want.
"Value people"
"Contribute to society"
"Value challenge and growth"
"Deliver the best value to customers"
Such words can also be created by AI.
However, if those words are not connected to the company's history, the president's sincerity, the employees' genuine feelings, and the value provided to customers, the philosophy will instead become hollow.
In the AI era, the importance of philosophy and vision is actually increasing.
AI provides many options.
It also provides efficiency proposals.
It also provides personnel reduction proposals.
It also provides sales promotion measures.
It also provides new business proposals.
However, what to choose from among them and what not to choose cannot be decided without a philosophy and vision.
AI expands 'what can be done.'
Philosophy and vision define 'what should be done.'
4. AI can refine business models, but it can also bias them toward short-term optimization
In terms of business models, AI has a very significant impact.
Narrowing down target customers.
Understanding the fundamental desires of customers.
Clarifying the value provided.
Redesigning the method of creating value.
Price design.
Customer acquisition flow.
Ongoing support.
AI can support these.
It can organize customer feedback, inquiries, sales records, reasons for lost deals, surveys, reviews, etc., and formulate hypotheses about what customers truly want.
Customers say, 'The price is high.'
However, perhaps they are actually afraid of failing.
Perhaps they feel anxious because the effects are not visible.
Perhaps they want materials they can explain within their company.
Perhaps they want a trusted partner to walk alongside them.
AI can generate these background hypotheses.
However, AI can also be used for short-term sales efficiency.
Targeting only customers who are easy to sell to.
Stimulating anxiety to make them buy.
Chasing only the closing rate.
Viewing customers only by scores.
If that happens, instead of responding to the fundamental desires of customers, it becomes a tool to efficiently acquire customers.
When using AI in a business model, it is necessary to ask not just 'will it sell?' but 'is it truly valuable to the customer?'
It is also necessary to distinguish between customer touchpoints that are fine to automate with AI and those that should involve humans.
What are customers looking for? Information?
Peace of mind?
Support for decision-making?
Human relationships?
Walking alongside them?
If you misjudge this, even if the response becomes faster, customer value may decrease.
5. AI organizes systems. However, systems can also make people rigid.
AI is extremely powerful for systemization and establishing patterns.
Manuals.
Checklists.
Meeting formats.
Evaluation criteria.
Training materials.
Information sharing rules.
Workflow.
Internal FAQs.
It can organize these things.
It becomes easier to turn work that was dependent on specific individuals into the company's shared assets.
Furthermore, AI is useful for verbalizing tacit knowledge.
Veteran judgment.
The president's intuition.
Tips for customer service.
On-site arrangements.
Management training know-how.
These can be put into words through dialogue with AI.
This has great significance for small and medium-sized enterprises.
This is because what was previously only in the minds of capable people can gradually become the company's assets.
However, the moment tacit knowledge is verbalized, it does not mean that everything is replaced by explicit knowledge.
Context, timing, the sense of reading the other person, physical sensations, and the subtlety of judgment derived from experience cannot be conveyed by words alone.
AI does not take away tacit knowledge.
It is meant to be used as material for dialogue to learn from each other's tacit knowledge.
Also, if systemization progresses too far, there is a danger that the room for employees to think will narrow and the company will become rigid.
A good system is not one that stops people from thinking.
It is one that allows people to think within their roles without getting too lost or being too afraid.
Creating patterns with AI is important.
However, creating patterns and forcing people into them are different things.
6. AI accelerates the propagation of influence between drivers
What is important in the AI era is the point that AI does not just affect individual drivers.
AI accelerates the propagation of influence between drivers.
In growth driver theory, we do not just look at the president, management philosophy/vision, business model, systemization/patterning, and behavioral environment as separate elements.
We consider that they influence each other to create the state of the company as a whole.
The president's judgment affects how the philosophy and vision are used.
Philosophy and vision affect the direction of the business model, systems, and behavioral environment.
The business model affects the necessary systems and how employees work.
Systemization and patterning affect whether employees can move without hesitation and whether there is room for them to think for themselves.
The behavioral environment affects whether employees can take on challenges, learn, improve, and increase customer value.
In this way, a change in one driver propagates to other drivers.
AI accelerates this propagation.
For example, suppose a president uses AI to analyze customer data and devise a new business policy.
If that policy is consistent with the company's philosophy and vision, the positive impact will propagate to business model reviews, meeting procedures, operational workflows, and opportunities for employee challenges.
AI helps with organizing, articulating, sharing, and systematizing this.
As a result, a virtuous cycle has the potential to spin faster.
On the other hand, if the president uses AI solely to maximize short-term profits, a different kind of propagation occurs.
Targeting only customers who are easy to sell to.
Prioritizing only closing rates.
Streamlining operations and reducing staff.
Skipping explanations and dialogue with employees.
The front lines become anxious, and trust declines.
In this case, too, AI accelerates the propagation of influence.
However, it is not a virtuous cycle, but a vicious one.
AI does not automatically create a good company structure.
Rather, it has the potential to bring the consistency or misalignment of existing structures to the surface faster and more significantly.
If the philosophy/vision and the business model are aligned, AI works in a direction that deepens customer value.
If the philosophy/vision and the business model are misaligned, AI may amplify that misalignment.
If systematization/standardization and the behavioral environment are aligned, AI supports the creation of mechanisms that make it easier for employees to think and take on challenges.
However, if systematization is biased toward control and surveillance, AI may work in a direction that weakens employee autonomy and trust.
If the president driver and the behavioral environment are aligned, AI becomes a tool that clearly communicates the president's ideas to employees and deepens dialogue.
However, in companies with a strong dependence on the president, AI might further strengthen only the president's judgment, causing executives and employees to lose opportunities to think for themselves.
As such, what should be looked at when introducing AI is not just individual efficiency.
Which driver does this AI utilization strengthen?
How does that influence propagate to other drivers?
Is it creating a virtuous cycle?
Or is it rapidly spreading misalignment and bottlenecks?
This is what must be examined.
In building a good company in the AI era, it is necessary to inspect the propagation of influence between drivers both before and after introducing AI.
Is AI strengthening only the president?
Is the philosophy/vision connected to daily judgments and systems?
Is the change in the business model putting undue strain on the behavioral environment?
Is the progress of systematization causing employees to lose room to think?
Is anxiety or distrust in the behavioral environment having a negative impact on customer value and improvement activities?
AI accelerates the propagation of influence between drivers.
That is precisely why, in the AI era, it becomes more important than ever to look not only at each driver individually, but also at how the drivers influence each other.
7. In the behavioral environment, AI is an assistant, not a substitute
In the behavioral environment, AI can support stretch, support, autonomy, discipline, and trust.
First, regarding stretch.
When employees take on work they have never experienced before, AI becomes 'training wheels for challenges'.
When a young employee creates a proposal for a customer for the first time, they can have AI generate a draft structure and compare it with their own ideas.
When a manager facilitates a cross-departmental meeting for the first time, they can create a facilitation scenario with AI in advance and organize anticipated counterarguments and key points.
Before a sales representative faces a difficult customer interaction, they can also conduct a role-play with AI to identify the customer's anxieties, counterarguments, and points to confirm in advance.
Used in this way, AI supports employee challenges.
However, if AI provides too many answers, employees may appear to be taking on challenges while actually failing to think for themselves.
AI should not be used as a tool that robs employees of their challenges, but as a tool that supports them in preparing to take on higher-level work.
Next, let's talk about support.
AI can serve as a primary support resource that employees can consult immediately when they are in trouble.
When a new employee is unsure about work procedures, they can check based on internal manuals and past cases.
Before consulting their supervisor, they can use AI to organize what they are struggling with and what they need to confirm.
Even when failures or complaints occur, AI can be used to organize facts, hypotheses on causes, recurrence prevention measures, and reports for supervisors.
For managers, it also serves as an aid in thinking about how to approach subordinates, how to conduct interviews, and how to create development plans.
However, increased support from AI does not mean that human-to-human support becomes unnecessary.
AI can organize knowledge and points of discussion.
However, accepting anxiety, offering encouragement, and watching over growth are human roles.
AI should not be thought of as a replacement for support, but as something that increases the entry points for support.
Next, let's talk about autonomy.
AI can prepare the materials for employees to think and make decisions for themselves.
What is the purpose of this work?
What are the options?
What are the merits and risks of each?
What points should I organize myself before consulting my supervisor?
These are things that can be confirmed with AI.
When thinking about improvement proposals, you can also have AI point out the weaknesses in your ideas to polish them into better ones.
Before speaking in a meeting, you can also organize your opinions and confirm the grounds for them and preparations for opposing views.
Used in this way, AI supports autonomy.
On the other hand, if the habit of immediately seeking answers from AI becomes ingrained, employees will begin to entrust their judgment to AI.
That is not autonomy; it is AI dependency.
AI utilization that enhances autonomy is not about receiving answers, but about obtaining the materials and questions needed to make decisions for yourself.
Next, let's talk about discipline.
AI is useful for clarifying standards, procedures, quality, and responsibilities that must be upheld.
Standard procedures for customer service.
Check items for reports.
Quality confirmation lists.
Decisions and action items after meetings.
Rules for information sharing.
Behavioral standards emphasized in evaluation and development.
These things become easier to verbalize.
However, if AI-driven discipline is used only for detailed monitoring and error detection, employees will become intimidated.
Discipline is essentially the foundation for doing good work.
Discipline separated from trust becomes a tool for control and punishment.
AI-driven discipline must be used not to bind employees, but to establish common standards that allow them to do good work with peace of mind.
Finally, regarding trust.
AI can support trust by improving the quality of information sharing and explanations.
Rephrasing the policies of the president and executives in an easy-to-understand way for managers and general employees.
Transforming the results of company health checks from mere score reports into explanations of what the company is trying to improve and how it wants employees to be involved.
Accurately sharing what was decided in meetings and clarifying who is doing what.
Reducing information bias and communication gaps becomes the foundation of trust.
On the other hand, if the purpose of introducing AI is not explained and employee statements, actions, and deliverables are analyzed in an invisible way, distrust will arise.
Employees will feel that they are being 'watched' rather than 'supported'.
Therefore, to protect trust, it is necessary to clarify what AI will be used for, what it will not be used for, and how it relates to personal information and evaluations.
8. The danger that the more you rely on AI, the less you interact with people
You can consult AI at any time.
You can ask questions without hesitation.
You can even ask about basic things.
You don't have to worry about how busy the other person is.
There is also less anxiety about being rejected.
This is a great support for employees.
However, despite that convenience, opportunities to consult with supervisors and colleagues may decrease.
Getting things done with AI before asking a senior.
Proceeding with AI proposals before discussing them in meetings.
Finishing by reflecting with AI before sharing failures with others.
Consulting only with AI instead of talking about worries with colleagues.
If this happens, even if work becomes more efficient, human relationships may become thinner.
This is an important issue for the behavioral environment.
Support is not just about obtaining information.
It is also about having someone acknowledge what you are struggling with.
Trust is not fostered only by getting the right answers.
It is fostered through the experience of consulting, being helped, sometimes clashing in opinions, and still working together.
Autonomy is also not about completing things alone.
It is the ability to think for yourself, consult when necessary, and make decisions while coordinating with those around you.
AI can support the environment in which we act.
However, it cannot replace the environment of action itself.
In fact, the more convenient AI becomes, the more consciously companies must design human-to-human interactions.
Things that can be resolved by asking AI.
Things that should be discussed with supervisors or colleagues.
Things that should be discussed as a team.
Things that should be confirmed directly with customers.
Things like failures or feelings of unease that should be shared with others.
It is necessary to distinguish between these.
When using AI to build a good company, it is important not to use AI to bypass human relationships, but to make human-to-human interactions more meaningful.
9. Does AI enhance or weaken employee capabilities?
AI has the potential to enhance employee capabilities.
You can articulate thoughts you couldn't organize on your own by working with AI.
You can use AI to prepare in advance when taking on a new task.
You can examine proposals for customers from multiple angles.
When thinking of improvement plans, you can have AI provide points of discussion and options.
You can organize your own opinions before speaking in meetings.
Even when you fail, you can calmly reflect on the facts, causes, and next steps.
Used in this way, AI becomes a force that supports employee learning, challenge, improvement, and autonomy.
In particular, for employees who have struggled with writing or creating materials and have been unable to express their thoughts well, AI can be a great help.
They had thoughts in their heads but couldn't put them into words.
They had insights for improvement but couldn't turn them into a proposal.
They had feelings for the customer but couldn't shape them into an explanation.
AI has the potential to draw out the capabilities of such employees.
On the other hand, AI also has the potential to weaken employee capabilities.
Seeking answers from AI before thinking for yourself.
Using text created by AI without understanding its meaning.
Settling for AI's suggestions before discussing them with supervisors or colleagues.
Not sharing failures with others and ending with only a reflection with AI.
Not listening directly to the customer's voice and assuming you understand it through AI analysis alone.
Treating AI's answers as the correct answer without having your own judgment.
If this happens, an employee's work might become faster.
However, their ability to think, judge, interact with others, and learn from failure may weaken.
In other words, AI has two sides.
AI can be an assistant for employees to think better.
At the same time, it can be a tool that creates a state where employees do not have to think.
Whether employee capabilities are enhanced or weakened depends largely on management ingenuity.
It is not enough for managers and executives to just have employees use AI.
It is necessary to design how AI should be used so that employees can think, learn, judge, and grow.
10. AI creates confidence, but also fear
The impact of AI on a company is not limited to operations and systems.
It also has a significant influence on the mindset of managers and employees.
What emerges first when you become able to use AI is a sense of possibility.
Document creation that used to take time can now be done in a short period.
Ideas that were difficult to put into words can be organized together with AI.
You can gain multiple perspectives when thinking about improvement proposals.
Proposals to customers can be shaped faster than before.
You can perform analysis and writing that were difficult to do on your own.
When you have these experiences, a feeling emerges among managers and employees:
“Maybe we can do it too.”
“Maybe we can reach improvements we had previously given up on.”
“Maybe even a small company can become better if we use ingenuity.”
“Maybe even young or general employees can turn their ideas into reality.”
This feeling is born.
This has a very significant meaning for building a good company.
Building a good company does not progress just by pointing out problems.
A sense that “we might be able to change” is necessary.
On the other hand, AI also creates anxiety and fear.
If I can't master AI, will I fall behind other companies?
Will my job disappear?
Will younger employees master AI while I am left behind?
Will competitors increase their productivity with AI, leaving my company at a disadvantage?
Will the gap between employees who can use AI and those who cannot widen?
Such feelings of fear also emerge in reality.
If this anxiety is left unaddressed, AI adoption will work against building a good company.
Employees will become defensive before learning AI.
Managers will begin to see AI as something that takes away their role.
Managers might rush into AI adoption without sufficient explanation due to impatience.
As a result, trust decreases and the working environment deteriorates.
In AI adoption, it is necessary to handle not only the introduction of technology but also changes in mindset.
Does AI create confidence?
Or does it create a fear of falling behind?
Does it increase the desire to take on challenges?
Or does it spread atrophy and resignation?
This difference is significant.
11. There is an order to AI adoption
The results of AI adoption change depending on what you start with.
If you introduce it company-wide all at once, it will cause confusion.
If you use it for personnel evaluation or surveillance immediately, it will create distrust.
If you automate customer service immediately, the relationship with customers might become thin.
When building a better company, there is a necessary order to introducing AI.
I believe it is best to start with areas that have relatively low risk and support dialogue and learning.
Brainstorming for presidents and executives.
Organizing meeting materials and internal documents.
Interpreting company health check results.
Supporting employee learning.
Developing operational procedure manuals and FAQs.
Reviewing improvement activities.
In these areas, AI supports human thinking rather than replacing people.
After that, you can move on to customer support, business automation, evaluation assistance, and business model redesign.
If you get this order wrong, AI will create distrust and resistance.
What is important in AI adoption is not just speed.
Where to start.
Who to involve.
What to use it for and what not to use it for.
What to do with the time saved by AI.
It is about designing these aspects carefully.
12. There is also fatigue from visualization through AI
When you use AI, you can see many things.
Employee voices.
Customer complaints.
Operational waste.
Meeting stagnation.
Evaluation discrepancies.
Inconsistencies between departments.
Low-score items in company health checks.
This is a good thing.
However, there is also fatigue from seeing too much.
When problems are visualized all at once, managers and executives can become overwhelmed.
Employees may also feel, 'Are we going to be pointed out for more issues again?'
AI can visualize issues.
However, visualization alone does not change a company.
You need to turn the visualized issues into small, manageable improvement themes.
You need to prioritize them.
It is also important not to try to change everything at once.
This is where small practices like the 'Three-Line Project' become meaningful.
What to change.
What to do.
What constitutes success.
Break down the issues visualized by AI into these kinds of small practices.
Otherwise, visualization can actually lead to fatigue and resignation.
13. Use AI not to eliminate failure, but to learn from it
AI works to reduce failures.
Identify risks in advance.
Refine text.
Propose countermeasures.
Run simulations.
Reference past cases.
This is a good thing.
However, in building a good company, the experience of failure is also important for personal growth.
Fail.
Observe customer reactions.
Review with a supervisor.
Improve with colleagues.
Apply to the next attempt.
This experience cultivates judgment.
If AI is used to avoid failure too much, employees may end up acting only within safe boundaries.
Furthermore, if they only choose the safe options provided by AI, both their challenges and learning will become shallow.
AI should not be used solely to eliminate failure.
It should be used to ensure that we can learn even when we fail.
In building a good company, it is important not to aim for zero failures, but to create an environment where actions allow for learning from failure.
14. We must be cautious when using AI for evaluation and human resources
I believe more companies will want to use AI for evaluation.
This is because it can analyze text, activity logs, deliverables, remarks, and work speed.
However, from the perspective of building a good company, the use of AI for evaluation should be handled with extreme caution.
AI can be used to articulate evaluation comments and organize perspectives for development.
It can also assist managers in thinking about their subordinates' growth challenges.
It can also refine the words used during evaluation interviews.
However, if AI evaluates people,
if AI scores are directly linked to compensation,
if employees cannot understand the evaluation criteria,
and if they are not explained why they were evaluated that way,
trust will be significantly damaged.
Evaluation is deeply connected to trust, discipline, and autonomy in the work environment.
AI should not be used as a tool to mechanically judge human potential or growth.
Evaluation must be connected to development, dialogue, expectations, and role formation.
AI can assist with evaluation.
However, it cannot replace the responsibility of looking at people.
15. Do not make it a company only for those who can use AI
The introduction of AI creates internal disparities.
Those who can use it and those who cannot.
Younger and older employees.
Headquarters and the front lines.
Administrative staff and operational staff.
Managers and general employees.
If this gap is left unaddressed, AI utilization will become a weapon for a select few rather than a learning tool for the entire company.
From the perspective of building a good company, AI literacy is not just an individual skill, but a part of the behavioral environment.
It is not enough to simply increase the number of people who can use AI.
It is crucial to create an environment where those who are not accustomed to it can learn without feeling embarrassed or anxious.
Not being told, "You don't even know that?"
Being able to ask basic questions.
Being able to experiment together.
Not being blamed for failures.
Being able to think about how to use it in a way that suits the workplace.
Without such an environment, the introduction of AI will widen gaps and spread distrust.
In building a good company in the AI era, it is insufficient to only train a small number of people who can use AI.
It is necessary to create an environment where the entire company can learn.
16. AI widens the speed gap within a company
When AI is used, some people and departments start moving faster.
Departments that use AI create documents immediately.
They churn out proposals.
They quickly compile improvement plans.
They increase their outreach to customers.
On the other hand, there are people and departments that continue at their traditional speed.
The front lines cannot keep up.
The administrative departments cannot keep up with rule development.
Management expects rapid change, but employees become exhausted.
The gap between departments that can use AI and those that cannot widens.
This is a problem of alignment between drivers.
AI increases speed.
However, if you do not design the speed of the entire company, the parts that have become faster will place a burden on other parts.
Only the speed of the business model increases, and systemization cannot keep up.
Structural changes progress, but the behavioral environment cannot keep up.
The president's decision-making becomes faster, but the understanding of executives and managers cannot keep up.
The front lines suffer from change fatigue.
These things can happen.
When introducing AI, it is better not to assume that becoming faster is inherently good.
Where should we speed up?
Where should we take our time?
Which changes place a burden on the front lines?
Who adjusts the speed of change?
This, too, is a management challenge in the AI era.
17. Companies that use AI vs. Companies used by AI
The number of companies introducing AI will increase.
However, there is a difference between companies that use AI and companies that are used by AI.
A company that uses AI is one that proactively positions and utilizes AI in light of its own philosophy, business model, employee growth, and customer value.
What does the company value?
Which tasks should be entrusted to AI?
Which tasks should be handled by people?
What should the time saved by AI be used for?
What should AI be used for, and what should it not be used for?
This is a company that thinks about these things before using it.
On the other hand, a company used by AI is one that rushes to introduce it because everyone else is talking about AI, and ends up being swayed by AI tools, vendors, and trends.
The introduction of AI itself becomes the goal.
Using it comes first, and the purpose for using it becomes vague.
Even if employees feel anxious, there is no explanation.
Drawn in by convenience, the company loses its own decision-making criteria.
What is important in building a good company is not just using AI.
It is about positioning and using AI within the context of building a good company for your organization.
18. Is a company made efficient by AI really a good company?
Finally, there is the biggest question of all.
Because of AI, the number of efficient companies will increase.
The number of companies earning money with fewer people will increase.
The number of automated companies will increase.
However, whether that makes them a good company is a different matter.
Even if there are few people, what if the employees are exhausted?
Even if AI leads to high profits, what if the people are not growing?
Even if customer support is automated, what if trust with customers is lost?
What if human interaction decreases and employees become isolated?
Even if work has become faster due to AI, what if employees have stopped thinking?
In the AI era, we need to ask again what a good company is.
For me, a good company is one that values its employees and where both the employees and the company grow together.
In the AI era, this definition becomes even more important.
This is because the more efficiency is improved by AI, the more we are questioned on how people grow.
The more automation is advanced by AI, the more we are questioned on what roles people should take on.
The more information increases due to AI, the more we are questioned on what the company values.
AI does not automate the building of a good company.
AI is an entity that forces us to rethink what building a good company means.
Introducing AI does not automatically make a company better.
Can you manage in the direction of building a good company while observing how AI changes the company's structure, the propagation of influence between drivers, the strength of employees, human interaction, mindset, and relationships with customers?
I believe the success or failure of building a good company in the AI era depends on that.