Artificial Intelligence

NTT Data's Hiroaki Sato on Codex's Workflow Transformation

Hiroaki Sato of NTT Data details how their AI tool, Codex, has achieved 10,000+ users by automating complex technical analysis and enabling new workflows.

StartupHub.ai Staff

By StartupHub.ai Staff, reviewed by Daniel Singer, Editor

2 min read

Visual TL;DR
Complex Technical AnalysisDriver
tasks requiring significant time and expertise
From the article 2 mentionsIts primary contribution lies in its ability to automate complex technical analysis tasks, dramatically reducing the time required for such processes.
NTT Data's CodexCore
internal AI tool automating complex analysis
From the article 3 mentionsHiroaki Sato, Head of AI CoE at NTT Data, discusses the transformative impact of Codex within the organization.
Automates AnalysisEffect
reduces two-day tasks to just 30 minutes
From the article 5 mentionsIts primary contribution lies in its ability to automate complex technical analysis tasks, dramatically reducing the time required for such processes.
Analyzes ETL FilesContext
From the articleThe tool is instrumental in analyzing Windows kernel dumps (ETL files), identifying system slowdowns, and pinpointing the root causes, such as hypothetical kernel calls.
10,000+ UsersOutcome
From the article 2 mentionsCodex, an internal AI tool, has seen remarkable growth, now boasting over 10,000 active users and fostering a significant internal community.
Workflow TransformationOutcome
enabling new and efficient ways of working
From the article 2 mentionsThe tool's ability to generate HTML reports automatically after analysis is a key feature that streamlines the workflow for technical personnel.

Hiroaki Sato, Head of AI CoE at NTT Data, discusses the transformative impact of Codex within the organization. Codex, an internal AI tool, has seen remarkable growth, now boasting over 10,000 active users and fostering a significant internal community. Its primary contribution lies in its ability to automate complex technical analysis tasks, dramatically reducing the time required for such processes.

Sato highlights a specific use case where a task that previously took two days can now be completed in just 30 minutes thanks to Codex. This acceleration is achieved by automating high-complexity analysis and performance analysis, including the generation of detailed reports. The tool is instrumental in analyzing Windows kernel dumps (ETL files), identifying system slowdowns, and pinpointing the root causes, such as hypothetical kernel calls.

The full discussion can be found on OpenAI Youtube's YouTube channel.

What Codex Unlocks for NTT Data - OpenAI Youtube
What Codex Unlocks for NTT Data, from OpenAI Youtube

The video showcases the capabilities of Codex by demonstrating its use in analyzing a system with performance issues. The analysis environment includes Codex IDE running on Windows 11 with WSL Ubuntu. The output presented is a detailed system process breakdown, identifying high CPU usage by various processes. The tool's ability to generate HTML reports automatically after analysis is a key feature that streamlines the workflow for technical personnel.

One of the most significant unlocks highlighted by Sato is Codex's capacity to automate tasks that were once time-consuming for technical officers, such as customer list maintenance. He states, "Creating new kinds of workflow change we hadn't seen before by Codex." This suggests that Codex is not merely optimizing existing processes but is fundamentally altering how certain tasks are approached, opening up new possibilities for efficiency and innovation within NTT Data.

The AI Center of Excellence (CoE) at NTT Data plays a pivotal role in driving the adoption and integration of Codex. By focusing on the broader adoption of AI products and platforms, the CoE aims to unlock new potential and create value across the organization. The rapid expansion of Codex's user base indicates its success in meeting critical needs and demonstrating tangible benefits to its users.

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TopicsNTT DataArtificial IntelligenceAI CoE

StartupHub.ai Staff

Written by

StartupHub.ai Staff

Editorial team

The staff writers of StartupHub.ai, ranging from investment analysts to avid AI tool users, early adopters and critical enthusiasts. Backgrounds span engineering, business and the arts. We hold every piece to rigorous standards of research and review.

Daniel Singer

Written by

Daniel Singer

Editor, reviewed this article

Daniel Singer is an AI expert who has been writing about, covering and building AI technology since 2018.

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