CyberAgent accelerates development using ChatGPT Enterprise and Codex

CyberAgent, the Japanese digital advertising and media company, offers a useful example for any organization wondering how to roll out AI tools widely without sacrificing security or quality. By adopting ChatGPT Enterprise alongside Codex, the company has moved AI from a handful of experiments to a working tool used across its advertising, media, and gaming businesses. This article breaks down what CyberAgent is doing, why the enterprise version matters, and what lessons other companies can take from its approach.

Why a big company needs the enterprise version

Consumer AI tools are fine for individuals, but companies face a different set of concerns. Employee data, client campaigns, and unreleased game content all need protection. ChatGPT Enterprise addresses this by keeping business conversations out of model training, offering stronger administrative controls, and giving IT teams visibility into how the tools are actually being used.

For CyberAgent, this mattered because the goal was not a small pilot. The company wanted AI to become part of everyday work for a large workforce spanning very different business lines. That only works if security and legal teams are comfortable, and enterprise-grade controls are what get them there.

Improving quality, not just speed

A common mistake companies make is treating AI purely as a speed boost. CyberAgent's approach goes further: the tools are used to raise the quality of output as well.

In advertising, teams can use AI to draft and refine copy, explore more creative variations, and polish deliverables before they reach clients. In media operations, writing and research tasks that used to eat up hours can be done faster while still meeting editorial standards. Codex adds a coding dimension, supporting developers as they build and maintain the systems behind the company's services.

The point is that AI acts as a collaborator. A human still reviews and approves the work, but starting from a strong AI-assisted draft tends to produce a better final result than starting from a blank page.

Faster decisions across three business lines

CyberAgent operates in advertising, media, and gaming—three areas that move quickly and reward fast, well-informed choices. When employees can gather background information, summarize documents, and test ideas with an AI assistant, the time between a question and a decision shrinks.

In gaming, for example, planning and development involve mountains of documentation and coordination. Being able to condense and explore that material quickly helps teams act sooner. In media, faster turnaround on research and drafts means stories and content stay timely. Across the board, quicker internal workflows translate into quicker responses to clients and audiences.

The lesson: security first, then scale

The most transferable takeaway from CyberAgent's experience is the order of operations. Rather than letting tools spread informally and cleaning up later, the company paired adoption with enterprise-level safeguards from the start. That made it possible to expand usage confidently instead of constantly walking things back.

For other organizations, the formula looks like this: choose tools with proper data protections, get security and legal teams on board early, and then encourage broad use across departments. Adoption grows faster when employees know the tools they are using are approved and safe.

Who should follow this approach / who should look elsewhere

Companies handling sensitive client data, large teams spread across multiple business units, and organizations that have struggled with informal "shadow AI" usage are the best fit for this enterprise-first model. It is also a strong match for firms in fast-moving industries like advertising and entertainment, where speed and quality both matter.

On the other hand, very small teams with minimal data-sensitivity concerns may find consumer plans sufficient and the enterprise cost harder to justify. And organizations without clear internal guidelines on AI use will want to establish those before scaling up, no matter which tools they choose.