The challenge
NTT DATA Group is a Japan-based global IT services company that spans consulting, systems development, and operations. Like many large firms, it faced a familiar problem: complex, high-stakes work that tied up its most experienced people for days at a time. One clear example was incident analysis for a critical system. According to OpenAI's case study, that specific piece of work had previously required five experienced engineers and taken three days to complete. The company was also rethinking how it grows, shifting away from adding headcount to add revenue, and towards creating more value through AI across its whole workforce.
The solution
NTT DATA Group began a global strategic partnership with OpenAI in May 2025 and deployed ChatGPT Enterprise across the company. It then established an internal OpenAI Center of Excellence (CoE) to support adoption through licence distribution, technical validation, events, use case development, usage monitoring, and knowledge resources. Building on that base of everyday ChatGPT use, the company expanded Codex to roughly 9,000 employees across both technical and nontechnical roles.
The results from the foundation were strong: an internal survey found more than 96% of respondents were satisfied with ChatGPT Enterprise, and more than 95% reported productivity gains. The company describes Codex as more than a coding assistant, noting it can independently investigate, execute, test, and revise based on an instruction.
The results
The standout outcome was the incident analysis itself. The full process that once took five engineers three days was completed with Codex in 30 minutes. That early proof point drew attention from senior leaders and helped build momentum. Other reported results include a 1.4x increase in weekly active Codex users after the company published a usage guide and ran hands-on training, plus automated internal system operations using Playwright, packaged as reusable Skills for wider adoption.
A real-world example anyone can picture
The case study gives a very practical, non-engineering example. Employees use Codex to extract transportation expenses from credit card statements and transfer them into travel expense forms. Codex can work across multiple files, understand the structure and entry rules of each sheet, and help verify the completed transfer. A simple instruction to try in this spirit: "Read my credit card statement CSV, find all transport charges, map them into the travel expense template, and flag any rows that don't match the sheet's entry rules for review." The point is clear: people set the direction and check the results, while the AI moves the work forward. Nontechnical staff can now produce an analytical report by working with raw data directly, rather than first preparing data in a business intelligence tool and building a dashboard.
Key takeaways
NTT DATA Group's lessons are worth copying. Make AI part of daily work so people build the habit of collaborating with it. Deploy broadly to create peer learning and word of mouth. Set clear security guidelines covering what data can be used, which systems the tool can connect to, and where human review is required. Treat the rollout as a start, then keep improving with usage data, surveys, and interviews. Finally, have a central team identify high-impact use cases and share them as reusable practices.
Brain.mt can help you using AI for your business. Contact me for more information. I also offer dedicated workshops and training about this subject.
