OpenAI Commercial and Professional Services Asia-Pacific & Oceania

NTT DATA Group

Enterprise Knowledge Search · IT Operations Automation

NTT DATA Group needed to raise productivity across technical and nontechnical roles, automate repetitive analysis work, and make agentic AI safe enough for broad enterprise use rather than isolated engineering experiments.

-99.3% incident-analysis time change from 3 days to 30 minutes
1.4x increase in weekly active Codex users after training
9,000 active Codex users
More than 95% reported productivity gains

Reported impact

Business outcome

NTT DATA Group reduced a complex incident analysis from three days to 30 minutes, expanded Codex to about 9,000 active users, improved weekly active usage after enablement, and lowered the barrier for nontechnical employees to automate their own work safely.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

NTT DATA Group first deployed ChatGPT Enterprise companywide and wrapped it in an OpenAI Center of Excellence that handles licensing, validation, events, knowledge resources, monitoring, and internal systems so employees can develop practical AI habits. On top of that base, the company expanded Codex to roughly 9,000 employees across technical and nontechnical roles. Codex now investigates incident patterns, scripts routine work, analyzes Excel and file collections, and even automates multi-file operational tasks that previously required specialist help. One early proof point was a critical incident analysis that moved from five engineers over three days to a 30-minute Codex run. Security and governance guidance define usable data, connected systems, sandbox modes, automation boundaries, and mandatory human review so the same agentic workflows can be generalized internally under the company's Client Zero model and later adapted for customers.

Data flow

Employees start with ChatGPT Enterprise for research and drafting, then hand defined tasks to Codex for investigation, file handling, data analysis, and automation. The Center of Excellence enforces data-use and sandbox rules, and validated automations such as Playwright-based routines are packaged and shared as reusable skills across departments.

Solution architecture

4 components · 3 layers
  1. Serving
    • ChatGPT Enterprise Builds AI habits for research, writing, and problem framing across the company before agentic execution begins.
  2. Orchestration
    • Codex Automates incident analysis, file organization, data analysis, scripting, and other defined tasks for technical and nontechnical employees.
    • Playwright Helps package internal system automations that employees can reuse across the organization.
  3. Governance
    • OpenAI Center of Excellence Provides governance, technical validation, adoption support, and reusable best practices for enterprise AI use.

Architecture clues

  • Client Zero approach treats the company as the first customer for AI use cases
  • OpenAI Center of Excellence governs licenses, validation, monitoring, and enablement
  • Reusable automations are packaged and shared as skills
  • Security rules define data usage, connected systems, sandbox mode, and review requirements

Evidence from the source

"Building on that foundation, the company has expanded Codex to approximately 9,000 employees."
"Codex completed an incident analysis in 30 minutes that had previously taken five engineers 3 days"
"Creating a secure and well governed environment is essential for employees to use Codex with confidence."
"Through our Client Zero approach, we actively promote the use of AI within our own organization."
Transparency Evidence & methodology 100/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    4 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. OpenAI

    NTT DATA Group customer story

    First-party
    https://openai.com/index/ntt-data/
    Source type
    Vendor-published customer story
    Published
    Jul 22, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Jul 22, 2026 · OpenAI · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.