Companies still debating AI adoption are already behind Paf by a year
While most companies are still running small pilot programs, Paf built ChatGPT Enterprise into the daily workflow of its entire organization, from engineers to HR, and even into a coding academy training the next generation of developers.
Routine development work was the first drain to disappear
Developers at Paf were spending hours on repetitive coding tasks that required context-switching and manual lookup. Custom GPTs replaced that cycle with a prompt and an output, keeping engineers in flow instead of in documentation.
Engineers open a custom GPT and the boilerplate writes itself
Surging developer productivity with custom GPTs lets engineers build purpose-specific assistants tuned to their stack, their naming conventions, and their internal processes. A developer pastes a task description or a code stub, and the custom GPT returns production-ready output shaped to how that team actually works. Paf extended this same setup to grit:lab, its coding academy, so students learn to build software with an AI-augmented mindset from the first day of training.
Finance and HR teams gained the most invisible time back
- Engineers who waste hours on boilerplate code and repetitive documentation tasks
- HR and finance professionals who manually format reports, policies, and internal communications every week
- Coding instructors who need to give individualized feedback to many students at once without slowing the curriculum
The 70% active usage rate across non-technical teams is the number that matters here. Most enterprise AI deployments stall outside of product and engineering.
Enterprise AI adoption just crossed a threshold that pilots cannot explain away
OpenAI competes directly with Microsoft Copilot for enterprise contracts, and deployment breadth like Paf’s is now a named benchmark buyers use in procurement conversations. When a single rollout covers developers, students, HR, and finance under one system, the case for fragmented point solutions gets harder to defend.
What teams are actually doing with it every day
- Build custom GPTs trained on internal code standards and architecture decisions
- Draft and reformat HR policies, finance reports, and support scripts in minutes
- Give students on-demand code review without waiting on instructor availability
- Replace recurring documentation tasks with a single prompt
The ceiling on this is not the tool. It is whether your organization commits to building GPTs that reflect how your teams actually work, not generic ones that almost fit.
Pricing
ChatGPT Enterprise pricing is custom and requires contacting OpenAI directly for a quote.
Honest tradeoff
Custom GPTs are only as useful as the instructions and context you build into them, which means the initial setup demands real investment from someone who understands both the workflow and the tool.
Alternatives
Microsoft Copilot for Microsoft 365 is the closest competitor at enterprise scale, with deeper integration into Teams, Excel, and Outlook. Google Workspace with Gemini targets the same buyer but leans harder on document collaboration than on developer tooling.
The enterprise AI race is no longer about access, it is about depth of adoption
Cases like Paf’s are shifting the conversation from whether to deploy AI to how far inside the organization it actually reaches. We cover tools like this every Friday — subscribe here and we’ll send the best ones straight to you.