How Zenken Administration Uses ChatGPT Enterprise — A Real Case Study

Hello — Okada from Zenken’s AI division here.

Following our press release on April 24, 2025 — “Zenken cuts external outsourcing costs by ¥50M/year through company-wide ChatGPT Enterprise rollout” — this is the first in a series of case-study posts (with about ten more departments to come). We’re starting with the administration (general affairs) team. The numbers should be useful as benchmarks for your own goal-setting.

Concrete back-office case studies for ChatGPT Enterprise are still scarce, so if this is useful, please share it.

Case 1: Faster access to system information

Before: Searching a 3,000-page system manual for the right information was slow.

After: The team built a custom GPT trained on the manuals, dramatically speeding up information access. PDF searching became trivial, and the time spent looking things up dropped sharply. With a custom GPT trained on a large corpus of manual data, relevant information can be pulled instantly in response to a question, vastly improving access to operational knowledge. Staff can now spend more time on their actual work, contributing meaningfully to overall efficiency.

Case 2: Higher quality internal communication

Before: Internal announcements and inquiry responses took time, and document quality varied.

After: ChatGPT Enterprise made it possible to draft clear, concise text in a fraction of the time, sharply reducing the time spent on internal notices and announcements. Email-reply templates and standardized boilerplate were also built out quickly, standardizing the response to internal inquiries and cutting handling time. Even when inquiries from new hires or other departments suddenly spiked, ready-to-go templates could be produced fast. Within the admin team, ChatGPT became an indispensable support tool, and important communications could be conveyed accurately and quickly to the entire workforce — visibly improving both the quality and efficiency of internal communications.

Case 3: Faster contract review

Before: Contracts had to be read word-for-word, and the review and clause-checking process took a great deal of time.

After: With ChatGPT Enterprise, disadvantageous terms and problem clauses can be flagged automatically, and the key points pulled out instantly for efficient review. Drafting responses to contract-clause inquiries is also faster. Time from contract scrutiny to finalized response is down by about 50%. In one real case, when there was uncertainty about specific terms during a contract negotiation with an external vendor, asking ChatGPT questions like “Is this interpretation of the clause appropriate?” or “How is this typically worded by other companies?” produced clear summaries of the relevant section and reasonable response drafts quickly. This lightened the load on the reviewer, improved tone and language adjustments for business correspondence, and made interactions with counterparties run more smoothly with faster turnaround.

Case 4: Automating application vetting

Before: Reviewing requested applications and plug-ins for permission-to-use was largely manual. Security checks, dependency-library reviews, and other items took time and effort to process.

After: Using GPTs, the team automated much of this review work — entering an application name now triggers an automated analysis. Accuracy improved, the reviewer’s load dropped substantially, and reduced review times freed up time for other support work. Tedious manual checks are now automated, with results delivered immediately based on a predefined set of investigation items. Both efficiency and accuracy improved. Limited human resources can be redirected to higher-value tasks, lifting overall IT-support productivity.

Case 5: Faster information lookup and document drafting

Before: Information lookups required browsing several pages of Google results to assemble what you needed; document drafting started from scratch.

After: After ChatGPT Enterprise rolled out, almost all information lookup shifted to ChatGPT, sharply reducing search time. For drafting, the “blank page” problem largely disappeared, freeing up energy for the substantive parts of the work. Reviewing materials and surfacing key points is also faster. Email handling time fell as well — by asking the model to adjust tone or suggest better phrasing, easy-to-read messages can be produced quickly. As one user put it: “Looking up something I didn’t know used to mean cycling through several websites. That stress is gone. Plus, being able to get clarity on my own without having to ask other staff means I’m not eating into their time either.” Even long passages and complex explanations can be condensed into key points quickly, lifting work pace and giving decision-makers exactly the bullets they need.

Case 6: Coding support (SQL and GAS)

Before: Writing SQL or Google Apps Script (GAS) involved a lot of trial-and-error and took significant time.

After: ChatGPT Enterprise supports complex SQL queries and their optimization, and accelerates building automation in GAS. Coding time dropped sharply and the burden of writing code is far lighter, raising overall productivity. As one user put it: “SQL that I used to grind out by trial and error now comes together quickly and accurately with ChatGPT’s support — the workload has genuinely dropped.” For GAS-based automation, ChatGPT’s code samples make it easy to build scripts fast, so what users want to do is implemented sooner. Speed and efficiency are up across the board, with concrete gains in productivity. Things like data-processing speed and report-generation automation — once labor-intensive — can now be implemented easily, allowing focus to shift to more sophisticated improvements.

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Across six workflows — search, drafting, contract review, programming support, and more — ChatGPT Enterprise has delivered roughly 30–50% time savings, with simultaneous gains in both quality and efficiency in routine work that requires specialized knowledge.

Quantitative impact on workload

Reductions across major tasks

Task	Before (hrs/month)	After (hrs/month)	Hours saved	Reduction
Information research / internal document drafting	20	15	5	25%
Internal announcement drafting	10	5	5	50%
Survey result aggregation	6	3	3	50%
Proofreading	2	1	1	50%
Lookup work	4	2	2	50%
Manual searching	10	5	5	50%
Contract review	1	0.5	0.5	50%
Email / document / data analysis	40	20	20	50%
Application-permission review	3	1	2	67%
Contract & notification document review	10	5	5	50%

Summary

• Average reduction: about 49%
• Most tasks landed at around 50% reduction
• Highest reduction: application-permission review (67%)
• Lowest reduction: information research / internal documents (25%)

Productivity changes

How users perceive their productivity gain

• “1.5x (about 33% time saved)”: 60%
• “No noticeable change”: 40%

About half of respondents reported productivity gains; the other half didn’t feel a clear difference. The split likely reflects differences in task type, usage patterns, and proficiency.

Established usage patterns

Document drafting and editing

• Quick drafting of internal announcements and notices
• Email tone-checking and optimization
• Contract review and risk-clause extraction
• Manual cleanup and simplification

Information gathering and analysis

• Faster lookups and searches
• Long-text summarization and key-point extraction
• Searching information inside PDF manuals

Programming and technical support

• Writing and optimizing SQL queries
• Supporting GAS (Google Apps Script) authoring
• Troubleshooting

Planning and ideation

• Brainstorming support
• Drafting initial proposal outlines
• Suggestions from multiple angles

Workload changes

Time-side impact

• 50% reduction in contract-review time
• Shorter application-vetting cycles
• Major savings on document drafting

Mental-side impact

• Less stress around information lookup
• Self-resolution without needing to bother other staff — saving organization-wide time
• Less “blank page” paralysis when starting drafts