I recently came across an article arguing that AI-generated meeting notes mainly improve individual productivity, but create limited value for the organization itself. The reasoning was straightforward: essential information still remains among the people who attended the meeting, rather than becoming shared knowledge across the organization. Once the meeting ends, the participants understand the conclusions, but for everyone else in the organization, still knows nothing.
That made me start thinking about a bigger question: if we really want to build an AI-native organization, what would actually matter?
At this point, I believe two aspects are especially important. The first is information allocation. The second is the accumulation of insight.
1. information allocation
Today, the value created by AI meeting notes is still mostly limited. It benefits a small group, not the organization as a whole. Information is still fragmented, still trapped behind boundaries, and still unable to move freely enough to create real value to the organization.
If we look at this closely, information allocation has two parts: how information is collected, and how it is distributed.
Collecting Information
At work, information is scattered across many channels: Slack, email, and countless conversations in between. If an organization could collect information more completely across these channels and make it available internally, the efficiency gains would be substantial.
Take a simple example. If I am an AI product manager, and the operations team is discussing issues related to the product I own, then the meeting notes, chat records, and emails around that topic should ideally be available to me. If I can access all of that context in one place, I can make better product decisions much faster.
Otherwise, I have to spend time tracking people down one by one. In many cases, the problem is not just that information is hard to obtain. The harder problem is that I may not even know who has the relevant information, or whom I should ask at the first place.
From that perspective, the real challenge is not just collecting information. It is collecting enough context from every relevant channel.
Distributing Information
Information collection is only the first step. Information creates value only when access is easier for all team members.
However, every organization has boundaries. Information does not naturally move across departments. Different teams held different knowledge, and that asymmetry was simply part of how organizations worked. With AI, some of those barriers are starting to disappear.
If information can be transformed easier, the value created for the business can be amplified in others. That is when AI stops being just a personal productivity tool and starts contributing to organizational growth.
To make this possible, organizations need a shared internal platform where information can be contributed, structured, and accessed more broadly. Only then can overall efficiency improve, while also increasing both individual contribution and organizational output.
Of course, this model only works if information security is taken seriously. A shared information platform requires not just technology, but also clear principles of permissions, access control, and clear responsibilities.
The Ideal State
We can take this idea one step further.
Imagine every employee has an AI assistant: an agent, work buddy.
This agent automatically receives and organizes all the information related to that person's work. In an ideal condition, it would not even need to search for information actively. Once information is created inside the organization, the system could push the relevant context directly to the employee's agent. The agent could then learn from it, organize it, and provide useful feedback while the person focuses on actual decision-making.
In this situation, people and AI agents work in parallel.
At the end of each day, key information from each department could be synchronized to the cloud. During the night, agents could continue processing, synthesizing, and connecting that information, and by the next morning, deliver useful outputs back to individuals across the company.
If an organization ever reached the point where information was broadly shared and employees operated with roughly the same informational foundation, it would create a much larger question: what happens to organizational structure?
A large part of middle management, like all the manager roles, has historically been about information transmission. If AI dramatically reduces the cost of collecting, distributing, and explaining information, then the value of some of those roles may be completely challenged.
Should those roles be redesigned? Should responsibilities changed? These are not questions AI can answer on its own.
AI can help solve rational problems. But questions of authority, accountability, and responsibility inside organizations are ultimately human problems, can't be solved by technologies.
2. Accumulating Insights
Even though large AI models have been trained on vast amounts of public data on internet, domain experts still possess something the models do not fully have: judgment in real business experience.
Every expert has their own understanding of the industry, customers, and patterns behind the problems they solve. If those insights are never trained in AI models, then they will never know.
AI is already good at handling structured tasks and formalized workflows. But identifying the essence of a problem is different. Solving complex problems requires judgment, tradeoff awareness, and multiple ways of thinking. AI can surface knowledge from many domains, but only experts can decide where the real path forward is.
There is a fundamental difference between content generated from probabilistic patterns and conclusions reached through rigorous reasoning.
That is why expert insights matter so much.
If domain experts can systematically share their insights, the cases they have solved, and the lessons learned from project retrospectives, then agents can begin to learn from that information. Over time, these agents may be able to imitate parts of the expert thinking process and solve certain problems in a similar style.
In that sense, an agent becomes more than just a tool. It becomes a lever for expertise.
We can already see the early form of this idea in AI products that package multiple professional-grade agents. When users interact with them, they are not just using software in the traditional sense. They are, in effect, accessing an expert team through software.
Agents will not replace domain experts. But they can dramatically extend the reach of expert capability.
Knowledge that was once valuable but hard to scale can become far more powerful with AI. Over time, an expert's insights may be reused across many scenarios, many teams, and perhaps even many organizations simultaneously.
That changes how expertise itself creates value.
In the future, an expert's influence may no longer be limited to the organization they work in. With AI, their capability can become visible and useful at the level of an entire industry. And the stronger the expert, the greater the leverage AI can provide.
Conclusion
I have always believed that the purpose of using AI at work is not to make people work like machines. It is to reduce human burden.
Routine execution should increasingly be handed over to AI. The point is to help individuals work more effectively, while also enlarging the value creation of the organization.
In the long run, one of the most important skills at work may be learning how to direct AI well: what context it needs, how to shape its role, and how to make it create the maximum value inside an organization.
And ultimately, the goal is still human. We use AI for less work , make better decisions, and have a better quality of life.