Application Refactoring · IT Operations Automation
CyberAgent needed a secure, governed way to spread AI across advertising, media, gaming, and development teams so employees could work faster and make better decisions without risking uncontrolled data use or fragmented adoption.
Reported impact
CyberAgent achieved broad monthly adoption, improved quality and speed in design and engineering work, and turned AI from isolated experimentation into a governed operating layer that teams choose because it helps them move faster.
CyberAgent built its enterprise AI environment around ChatGPT Enterprise and Codex so AI could become a default layer in daily work rather than a narrow advanced initiative. ChatGPT Enterprise provides the secure foundation for research, drafting, summarization, and decision support across teams, backed by account management, usage visibility, and internal guidelines for handling confidential information. The company then built culture and operations around adoption: Slack follow-ups for inactive users, prompt and use-case sharing, internal rankings for self-visibility, and more than ten OpenAI-led training sessions covering basics, custom GPTs, hackathons, and Codex. Codex extends the environment into design review, code review, documentation, and upstream planning, helping teams pressure-test proposals before implementation and even build systems such as the company’s internal usage-ranking tool. The result is high voluntary adoption without mandates and better-quality decisions earlier in workflows.
Employee work such as research, drafts, proposals, code changes, and documentation flows into ChatGPT Enterprise and Codex under internal governance rules. Usage data and Slack follow-ups then feed the adoption loop, while teams share prompts and successful patterns back across the company.
"CyberAgent used Codex to build its internal usage ranking system"
"Teams are using it for far more than code generation, especially in upstream work such as design, alignment, and evaluation"
"Today, using ChatGPT for tasks such as research, drafting, and organizing key points has become standard practice"
"reaching a monthly active user rate of 93%"
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
Related
OpenAI Sample Jun 18, 2024 Paf Paf is an international gaming company founded in 1966 in the Åland Islands by the Red Cross, Save the Children, and Folkhälsan, and is an industry leader in responsible gaming. With around 315 employees from 29… Paf is an international gaming company founded in 1966 in the Åland Islands by the Red Cross, Save the… ChatGPT Enterprise · GPT-4 · OpenAI API OpenAI Sample Oct 9, 2026 Asana Asana needed to cut the cost and latency of its StackAI browser agent so it could offer customers more capable models for browser-driven workflows without unsustainable operating expense. $0.47 average estimated model cost with the optimized GPT-6.1 Sol workflow Codex · Command · GPT-6 Astra OpenAI Sample Oct 8, 2026 Braintrust Braintrust wanted to respond to customer feature requests faster and run more engineering experiments, but slower coding tools and backlog-driven workflows made it costly to turn feedback into working prototypes in real… 50% of the Braintrust team moved to Codex in one month Codex · GPT-5.5 