On June 21, 2026, OpenAI announced that Samsung Electronics would deploy ChatGPT Enterprise and Codex to all Samsung Electronics employees in Korea and to all global employees in its Device eXperience division. The stated scope includes technical and non-technical work, from research and development and manufacturing to marketing and corporate functions. This is not simply news about the number of AI accounts. It signals a shift in how large companies are viewing AI: from an experimental tool for a few teams to a layer of capability embedded in everyday work.
The important point is not that every employee will use AI in the same way. An engineer, a marketer and a plant manager work with very different data, quality standards and risks. Therefore, when AI is deployed at scale, the management question is no longer “which tool have we bought?” It is “how will the organization permit, guide and measure use of this tool in each workflow?”
From giving access to designing work
Many companies begin by providing accounts and running a prompt-training session. That step is necessary, but it addresses only access. Operational value appears when a company clearly identifies which work AI supports well, which outputs require human review, which data may be used and how results will be recorded. For recurring workflows such as summarizing customer feedback, preparing reports, reviewing documents or creating content drafts, a company can define processing time, rework rate and satisfaction to assess effectiveness.
By contrast, if AI is used only as an improvised utility, the organization finds it difficult to learn. One team may save time but not know how to turn its experience into guidance for another team. Another may produce unreliable outputs because it uses uncontrolled data. Large scale makes the gap between a few successful experiments and a repeatable operating capability more visible; it does not automatically close that gap.
Three things to prepare before expanding
First is work context. AI needs to know which documents are authoritative versions, which terms are internal and which standards define a good output. Without this context, a model may create a fluent draft that does not fit the real process. Second is authority. Companies need to distinguish what AI may read, recommend, prepare or execute; each level needs an accountable person and an appropriate checkpoint. Third is a feedback loop. Errors, exceptions and strong revisions need to be captured so that processes, guidance and evaluation improve over time.
Management example
A marketing team can allow AI to summarize campaign feedback and propose themes that need checking. Publishing figures externally or changing brand messaging, however, still requires approval from the responsible person. When objectives, data sources, authority and approval steps are written clearly, AI becomes part of the process. If the team is merely encouraged to “try it,” it is hard to know who is accountable when the summary misses an important signal.
Implications for Vietnamese companies
Vietnamese companies do not need to wait until they reach Samsung’s scale to begin asking this question. In fact, a smaller scale is often an advantage for selecting one specific workflow, testing within a limited boundary and adjusting quickly. A good starting point is work with high frequency, relatively clear output standards and controllable risk: summarizing customer feedback, preparing a weekly report, helping employees find internal documents or drafting recurring communications.
The measurement should go beyond usage volume. An implementation team can track the time from request to first draft, the share of outputs accepted after review, the number of data-related errors and user satisfaction. These indicators do not prove that AI always creates value, but they let managers see which workflows are genuinely improving and which ones need redesign.
Conclusion
Samsung’s broad deployment of ChatGPT Enterprise and Codex is a signal that AI is entering the structure of work, not only innovation projects. The management lesson is not to copy the scale of deployment. It is to turn a tool into capability: connect AI to specific workflows, trusted context, clear authority, human review and disciplined measurement. Without these conditions, expanding access may simply expand inconsistency. When they are prepared well, AI can support a faster and more controlled way of working.
Sources
OpenAI. (2026, June 21). Samsung Electronics brings ChatGPT and Codex to employees. https://openai.com/index/samsung-electronics-chatgpt-codex-deployment/
OpenAI. (2026, February 5). Introducing OpenAI Frontier. https://openai.com/index/introducing-openai-frontier/
