We Distributed AI Accounts, But Why Is the Organization the Same?
Record of C-Bridge Day Lecture with Content Startup CEOs
Topic: From Individual AI Usage to Organizational AX Transformation
2026 Scaleup Accelerating C-Bridge Day Lecture Record · Content Company AX Transformation and AI Education
Today, we are sharing the news that CEO Lee Eun-young participated as a speaker at the C-Bridge Day of the "2026 Scaleup Accelerating" program operated jointly by Chungcheongnam-do Content Promotion Agency and CNT Tech. This event was a gathering of CEOs and practitioners from content startups in growth stages.
The topic was content company IP business expansion and commercialization strategy.
Nowadays, it's rare to have difficulty doing something because of a lack of ideas.
Instead, the key is to quickly create and grow many of those ideas without problems.
How? Through AX transformation. Not just talk, but through real amazing cases that we've created.
The question that lingered longest in the lecture hall with 40 people was about organizational AX transformation across the entire organization, not individual AI usage, innovation in ways of working, and the role of leaders in the AI era.
About 40 program-participating startup CEOs and related personnel gathered at the event, and the lecture was allocated 50 minutes.
While individual productivity has clearly improved with AI Agent use,
is organizational productivity also advancing in parallel?
It was a difficult question to raise your hand and answer. In most companies, planners create plans with personal accounts, developers stack code in personal work folders, and marketers pull copy from their own chat windows. Everyone's individual speed went up, but no records or assets were left in the company.
We told people to use AI and opened accounts for them, but why does the organization seem to remain the same?
This gap is the starting point of AX transformation. AX is not a gap that can be filled by attaching a few more tools, but a structural gap created by separating where communication happens from where work happens.
UniqueGood Company previously operated in the offline entertainment sector and achieved stunning results, even setting a Guinness World Record with audience-gathering events. However, at the same time, relative to the scale that had grown to about 60 people, the company was struggling against major limitations in profitability and productivity. The profitable operations that had continued since the early days of founding turned into losses as the scale grew, and that deficit only increased. Eventually, after facing various difficulties this year, it has now shrunk to 7 people. However, remarkably, it is handling even more business this year, and it has a real case of achieving AX transformation that even generates operating profit.
So it is a company that has already experienced firsthand and knows better than anyone where organizational AX transformation fails, and why communication with organization members and AI Agent must be integrated together to create a learning loop.
The core of AX transformation is completely removing bottlenecks.
The decline in organizational productivity has three critical areas: context, time, and speed.
First, context that exists only in the practitioner's memory must be converted to shared context. When you do work, why that direction was chosen, what it was based on, and what should be done next exist only in the practitioner's memory. When people change, the remaining people hear the background all over again. This is a structure where organizational memory doesn't accumulate.
Second is time. Decisions are made in meetings, instructions are given through messenger, and then AI is opened again to explain from the beginning. Since the place for deciding and the place for creating are separate, the same explanation is repeated multiple times a day. The reason there isn't enough time to actually work despite being busy all day is here.
Third is speed. Even when urgent tasks overlap, the amount processed simultaneously is only proportional to the number of people. In organizations like content companies where proposals, production, settlement, and promotion pile up at once, this limitation becomes more apparent. If throughput doesn't increase until more people are hired, that's not a capability problem but a structural problem.
Real AX tasks are those that remain even after removing 'time reduction'
When the AX team opens a task proposal, similar lists come up. 30% reduction in report writing time, automatic meeting note organization, promotional image generation, draft answer writing for inquiries. According to the Korea Chamber of Commerce and Industry survey, the top effect companies got from AI was time reduction at 45.8%.
Creating outputs faster is only transformation at the individual level, the first step. If you stop there, the company only makes the same work slightly less difficult, but the way of working itself remains the same. The next task is to establish a structure that repeats the entire task.
Using a content agency as an example, the new task for the execution manager becomes automating the execution work they were doing. When campaign planning, content production, and performance reporting operate without human hands, the company can focus on client consultation and IP expansion. Try removing the word 'time reduction' from your task list, and see if any tasks remain.
The person who should work as an AX leader is not an AI expert.
AX teams are usually composed of practitioners, IT departments, and external experts. If there's a development team, you might think the transformation would be better, but in reality, there's no significant difference. The key is whether someone who understands the entire workflow is sitting in the design seat.
The business team requests one visible step. They might say something like 'make report drafts come out automatically.' When you build what they request, the tool is complete, but what actually needed changing might have been the reason the report was needed in the first place. What the recipient wanted wasn't a document but a single number, and that number might have already been somewhere.
Seeing the structure is a matter of position rather than capability. That is, leaders must take care of it. Only from a position where the whole picture is visible can you judge which tasks can be entirely eliminated and which flows should be combined. In a small content company, that position is the CEO, and in a large organization, it's the leader responsible for that process all the way through. The AX team handles implementation on the side.
You need to create loops instead of tools.
Small teams do the work of 100-person organizations.
The metrics in performance reports usually count how much people used something, like completion rates, satisfaction, and utilization rates. If you measure performance by the number of people, next year's plan also becomes education and accounts. What you need to see is whether the workflow created three months ago is still running today, and whether that workflow has improved compared to last month.
In a study released by MIT in 2025, 95% of enterprise generative AI pilots ended without results. Because nobody opens them once the pilot ends. AI models improve every few months, but tools tied to the level at the time of creation become outdated the moment the next model comes out. That's why you need to leave behind loops, not tools.
Execute, review results, document decisions and procedures as standards, and have the next execution inherit those standards. Once this learning loop is established, every time a new model comes out, our workflow improves with it. The proposal I gave at the end of the lecture was simple: select one of the practitioner's tasks and make it run from start to finish without human hands, then upload it to the company system. Then, because team members work in a higher-level environment, it becomes even more sophisticated and creates stronger productivity.
Source: MIT 2025 published study (95% of enterprise generative AI pilots ended without results), Korea Chamber of Commerce and Industry survey (AI's top effect is time reduction at 45.8%),
"We provided AI education and distributed accounts. But there's no sense that the company is moving. Where should I look again from?"
https://www.yes24.com/product/goods/195392421 "Nanoventer" — YES24
Checklist!
- Check that someone who understands the entire workflow participates in the design for each task.
- If AI productivity is trapped in individual context, please convert it to shared context.
- Select just one to verify that it's operating through the workflow, not by prompt instructions.
- Take one of the practitioner's tasks and make it run from start to finish without human hands, then upload it to the company system.
There's no need to change everything at once. Just by shifting your perspective of AI from a tool to an environment, the same education and the same accounts will start to leave something entirely different behind. Education will leave behind workflows, and tasks will become the work of redrawing the entire flow.
The journey of redefining existing tasks and processes and converting to agents with a small team is detailed in "Nanoventer." If you hear about your organization's situation and think we can help, please feel free to reach out. We'll find specific directions together with case studies.
[Consult on where to start with your organization's AX transformation]
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