AI at Work: 3 Stages From Individual to Team to Company

You use ChatGPT every day, so why does your team still work exactly the way it did a year ago? If everyone drafts faster on their own but sharing and handing off work stays the same, the time each person saves never adds up to better team results.

This post is based on what the daglo team heard at OpenAI Builder Bootcamp Seoul, held in Seoul on October 27, 2026. It covers the opening session, where an OpenAI engineer described how AI at work grows from individual use into team and company-wide workflows.

Three stages of AI at work: individual, team, organization

AI at work grows through three stages: individual, team, and organization. The speaker explained that each wider stage brings bigger opportunities, but also more dependencies and handoffs between people and systems that you have to design for.

As an individual, you stay in control of your own work. At the team stage, who receives which result starts to matter. At the organization stage, the real job is lining up different processes and systems across departments so the pieces actually fit together.

Build small, ship, improve, repeat

The speaker described how OpenAI moves its technology forward along three tracks: Research, Apply, and Deploy. The key point is that the loop never runs in just one direction. What the team learns from deployment flows back into research and product work.

• Research: Push what models can do and understand their limits.
• Apply: Turn research into features and software people can actually use.
• Deploy: Put it in front of real users and see how it holds up.

The same mindset applies when your team builds an agent or an automation. Instead of polishing something internally for months, build a version that works, let colleagues try it, and improve it based on what they tell you. Real use surfaces problems no planning session will catch.

Models are getting faster and cheaper. How do you keep up?

The speaker noted that the gap between model generations used to be more than a year and has now shrunk to a matter of weeks. Models are also spreading beyond text into images, video, and voice, and the cost of using them keeps falling. Cheaper intelligence makes it realistic to put agents on everyday tasks that were not worth automating a year ago.

At that pace, you cannot relearn everything with each release. The advice was to build a framework for judging new capabilities and a way to evaluate them on your own work first. The rest of the bootcamp split that framework into three tracks:

• Codex: handing off the work of writing, understanding, and fixing code to AI
• Agents: combining models and tools into real business workflows
• Realtime: talking to AI by voice while it gets work done

The first step to team-wide AI: shared meeting records

The session closed with advice to help colleagues find where AI fits their own work, start small, and share results. The easiest way to move AI from individual to team use is to make sure everyone is looking at the same record.

Transcribe meetings with daglo and keep them in a shared team space, and a colleague who missed the meeting can pick up the full transcript and summary as is. If they have questions, they can ask BoardChat something like "Summarize only the decisions made in this meeting." When one person's notes become the whole team's record, AI stops being a personal tool and becomes a team tool.

In meetings with many people, Speaker ID makes handoffs even easier. It remembers voices and fills in real names instead of "Speaker 1" from the next recording on, and daglo is the first service in Korea to offer it to every user. Learn more about Speaker ID

For the key takeaways from every session, see 7 Lessons Before You Build an AI Agent. To decide which task to hand to AI first, read how to choose your first AI project.

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