These days, we hear a lot of news about AI. In particular, more companies are making massive investments in AI and cloud infrastructure while at the same time reducing their workforce or carrying out restructuring. Oracle, for example, has been spending heavily to expand its AI and cloud businesses while also increasing restructuring costs, and some companies are cutting jobs as they adopt an “AI-first” strategy.
At first glance, this may seem contradictory: companies are spending more money on AI while reducing the number of people they employ. But if we look more closely, this change is not simply about AI replacing human jobs. More importantly, we need to examine where companies are spending their money and how they are redesigning their organizations.
1️⃣ Why Companies Are Increasing Their AI Investment
The biggest reason companies are increasing their investment in AI is not simply to keep up with a new technology trend. The more practical reason is that AI is becoming a core part of business infrastructure that can reduce costs, improve productivity, and strengthen future competitiveness. Recently, investment has expanded beyond AI models themselves to areas such as data centers, cloud services, semiconductors, and enterprise AI software.
1) To Get More Done With the Same Number of Employees
One of the first things companies expect from AI is higher productivity. Tasks that previously required several employees such as data analysis, document preparation, customer inquiries, and software development. And this can now be partly supported or automated by AI.
- Automating repetitive tasks
- Reducing the time needed for data analysis and report writing
- Lowering customer service operating costs
- Increasing the speed of development and marketing work
For example, fintech company BILL recently reduced its workforce by around 30% while pursuing an “AI-first” strategy, and it is expanding the use of AI in areas such as growth operations and expense categorization. This suggests that the purpose of adopting AI is moving beyond simple experimentation and toward improving operational efficiency.
2) Because AI Is Becoming a New Growth Market
AI is not only a tool for reducing costs but also becoming a market that can create new sources of revenue. Cloud companies, in particular, expect demand for servers and computing resources to increase as more businesses adopt AI, which is why they are making large-scale investments.
Oracle is a good example for this. The company has been investing heavily in data centers and cloud infrastructure to respond to rising demand for AI services. More recently, strong growth in AI-related demand has significantly increased its order backlog, highlighting its future growth potential.
3) Because Falling Behind Competitors Can Be Difficult to Recover From
AI is not a technology that creates an immediate competitive advantage simply by installing new software. To use it effectively, companies need to organize internal data, redesign work processes, and train employees to work with AI. This process inevitably takes time.
That is why companies that adopt AI earlier can gradually build several advantages:
- They can learn more quickly which tasks benefit most from AI
- They can develop AI practices that fit their own operations through trial and error
- Employees can build more experience and stronger AI skills
- Internal data and AI systems can become increasingly integrated, improving efficiency over time
By contrast, companies that adopt AI late may be able to purchase the same technology later, but it can be much harder to catch up with the experience and operational know-how that early adopters have already accumulated.
In the end, AI competition is becoming less about the technology itself and more about who can learn faster and integrate AI into the organization more effectively.
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2️⃣ How AI Is Changing Corporate Organizational Structures
As AI becomes more deeply integrated into companies, the change is not limited to the number of employees. A bigger shift is taking place in how work is divided, how decisions are made, and how teams are managed. In the past, many tasks had to go through several layers of reporting and review. But as AI becomes better at organizing and analyzing information, corporate structures may gradually become simpler.
1) Some Middle-Layer Tasks May Be Reduced
In traditional organizations employees often prepared materials, middle managers reviewed and organized them, and senior managers then received the final report. But if AI can take over tasks such as report summarization, data analysis, schedule management, and progress tracking, some of these middle-layer responsibilities may become less necessary.
Tasks that could be reduced include:
- Preparing reporting materials
- Summarizing meetings
- Organizing project progress
- Basic data analysis
- Repetitive approval and review tasks
Because of this, future organizations may move away from structures that require many layers of management and toward structures where fewer people manage a wider range of work.
2) Smaller Teams May Be Able to Do More
When AI is used effectively the range of tasks one person can handle becomes broader. For example, a marketing employee may be able to conduct market research and draft content at the same time, while a developer may be able to write code and run tests more efficiently.
As a result, companies may increasingly choose structures where a small group of core employees uses AI to handle a wider range of tasks, rather than assigning a large number of people to each individual function. Teams may become smaller, but the responsibilities of each person may actually become broader.
3) The Center of the Organization May Shift From “Management” to “Judgment”
As mentioned above, many important managerial tasks such as organizing information, assigning work, checking progress, and following up with team members could increasingly be supported or partly replaced by AI.
AI can process data from multiple departments faster than people, compare possible options, and quickly present alternative choices. This means managers may no longer need to spend as much time simply collecting and organizing information.
However, this does not mean that managers will disappear. In fact, the ability to judge which decisions should be made may become even more important. AI can analyze data and suggest different options, but it is still difficult for AI to fully decide what a company should prioritize.
That means future managers may need to focus less on simply directing and checking tasks, and more on:
- Checking whether AI-generated information is reliable
- Setting priorities among different options
- Balancing short-term efficiency with long-term impact
- Deciding which tasks should be delegated to AI and which should remain human-led
For this reason, the key skill for leaders in the AI era is becoming less about how many people they can manage and more about how well they can make judgments based on the information and options created by AI.
In the end, the organizational change created by AI is not simply about reducing headcount. Companies are increasingly likely to redesign themselves around smaller teams, faster decision-making, and employees with broader responsibilities.
3️⃣ Which Jobs Are Likely to Be Affected by AI First?
As AI becomes more widely adopted, which types of jobs are likely to be affected first? It may be tempting to think of manual labor or repetitive physical work, but recently, office-based tasks that are repetitive and already performed in digital environments, such as document writing, data processing, and customer support are often being affected more quickly by AI.
The International Labour Organization (ILO) has estimated that around one in four workers worldwide are employed in occupations that could be exposed to generative AI, with clerical and administrative occupations showing particularly high exposure. However, this does not necessarily mean that these jobs will disappear entirely. In many cases, some of the tasks within those jobs are more likely to be automated first.
1) Clerical and Administrative Work May Be Affected First
AI is particularly good at reading, classifying, and summarizing information that is already digital. That makes tasks such as the following relatively easy to automate:
- Data entry and document organization
- Schedule management and meeting summaries
- Basic report writing
- Email classification and drafting responses
- Repetitive accounting and administrative tasks
The ILO’s 2026 research also found that occupations involving repetitive cognitive tasks, such as clerical work, administration, and business support, are relatively highly exposed to generative AI. This helps explain why office-based jobs are often seen as one of the areas most likely to feel the impact first.
2) Customer Service and Content Work Are Also Changing Quickly
Tasks such as answering frequently asked customer questions or providing standardized information can already be handled to a significant extent by chatbots and AI agents. In marketing as well, AI is increasingly being used for tasks that once required a large amount of human time, such as drafting ad copy, creating images, and conducting market research.
Because of this, companies may no longer need as many employees as before to handle the same volume of work. Of course, not every role will be automated immediately, but the impact on actual employment could still become significant.
3) Software Jobs Are Not an Exception
Perhaps surprisingly, programming is also one of the fields being affected quickly by AI. This is because AI can support tasks such as code writing, debugging, and testing, allowing developers to become more productive. The Bank for International Settlements (BIS) has also noted that the impact of AI-driven automation is beginning to appear not only in customer service, but also in programming and administrative work.
- Indian IT company Wipro, for example, said that the adoption of AI created productivity equivalent to the workload of around 20,000 employees. However, it is also important to note that this did not mean those workers were immediately laid off; many were reassigned to other roles.
In the end, the jobs most likely to shrink first because of AI are not necessarily entire professions. It may be more accurate to think of them as tasks that are repetitive, rule-based, and easy to digitize.
So rather than asking whether AI will replace your job entirely, a more useful question may be: Which parts of my job can AI replace, and which parts can humans still do better?
4️⃣ Does AI Investment Actually Improve Productivity?
Companies are investing huge amounts of money in AI, but in the end, there is one key question: Is that investment actually leading to higher productivity? Simply adopting AI does not automatically improve every company’s performance. Even when companies use the same technology, some manage to reduce costs and improve results, while others may only see their investment expenses increase.
1) There Are Clearly Areas Where Productivity Improves
AI is particularly effective in repetitive and time-consuming tasks. When AI supports work such as document writing, data organization, customer service, and coding, employees can complete tasks faster and handle more work with the same number of people.
Many companies are therefore expecting benefits such as:
- Shorter task-processing times
- Lower labor and operating costs
- Higher output per employee
- Faster customer response times
- Greater speed in development and analytical work
In other words, productivity gains can appear relatively quickly in tasks that are well suited to AI.
2) But Adopting AI and Using It Well Are Two Different Things
The problem is that simply purchasing or introducing AI does not mean that results will appear immediately. If a company’s internal data is poorly organized or its existing way of working remains unchanged, adding AI may actually make workflows more complicated rather than more efficient.
For example, if employees have to spend a lot of time checking and correcting AI-generated results, the expected cost savings may not materialize. Running AI systems can also require additional spending on cloud services, data centers, security, and employee training. If these systems are not used effectively, overall costs can even increase.
I can see this difference in people around me as well. Some use AI very efficiently and clearly improve the way they work, while others do not fully understand how to use it and continue working in almost the same way as before.
3) In the End, How AI Is Used Matters More Than How Much Is Invested
Going forward, the biggest difference between companies may come not from who spends more money on AI, but from who can connect AI properly to real business processes.
To improve productivity, companies need to go beyond simply providing AI tools. They also need to:
- Decide which tasks should be assigned to AI
- Redesign existing work processes
- Train employees to use AI effectively
- Continuously measure actual cost savings and performance improvements
It is important to keep evaluating whether AI investment is working through these processes.
In the end, the success of AI investment depends less on the technology itself and more on how well the organization is prepared to use it. Companies that spend heavily on AI will not necessarily succeed. Those that can connect that investment to real productivity gains and financial returns are more likely to gain a lasting competitive advantage.
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💡 Conclusion – What Makes a Company Competitive in the AI Era
In the AI era, a company’s competitiveness is unlikely to be determined simply by how much AI it adopts. What matters more is how effectively AI is connected to real business operations and used in practice. Even when companies use the same technology, some can increase productivity and reduce costs, while others may only end up spending more on investment.
In the end, the difference may depend less on the technology itself and more on how well the organization knows how to use it. It is more efficient to identify which tasks should be handled by AI and which should remain with people, especially for areas that require creativity, problem-solving, and decision-making. At the same time, companies also need management systems that can review AI-generated results and ensure accountability.
That is why the companies most likely to remain competitive will be those that can clearly distinguish what AI does best from what people do best, and connect the two effectively within the same organization. Ultimately, the key competitive advantage in the AI era may depend less on the technology itself and more on how companies choose to use it.