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OpenAI Commercial and Professional Services Japan

Zenken

Sales Automation

Zenken needed to reduce manual research, proposal drafting, translation, and meeting preparation work that constrained sales productivity and slowed international expansion across a lean organization.

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5 to 15 additional hours per employee every month
50 million yen in annual outsourcing cost savings
Approval rate for final proposals up about 30%
Average time savings of 30 to 50% across knowledge work tasks

Reported impact

Business outcome

Zenken improved sales win rates and proposal quality, saved large amounts of knowledge-work time, reduced outsourcing spend, and enabled a smaller team to sustain international growth with far more AI-assisted throughput.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

Zenken rolled out ChatGPT Enterprise as an AI-first operating layer across sales, web marketing, and its overseas human-resources business. After evaluating solutions against twelve requirements around security and advanced reasoning, the company standardized on ChatGPT Enterprise and layered in GPT-5, reasoning models, custom GPTs, and image-generation tools to support both strategic analysis and day-to-day execution. In sales, teams use ChatGPT for prospect research, meeting preparation, live question handling, proposal drafting, and personalized follow-up, shifting conversations from checklist discovery to consultative selling. In the HR business, the same platform translates contracts, job postings, and multilingual communication with better context retention than earlier tools. Employees also use it for marketing content, internal documents, and strategic hypothesis generation, allowing work once outsourced to move in-house while managers and staff refine AI-assisted outputs together.

Data flow

Prospect information, proposal requirements, multilingual documents, and internal planning tasks are fed into ChatGPT Enterprise and supporting models. Outputs become research briefs, translated content, sales emails, proposals, and strategic hypotheses that employees refine during meetings and client work.

Solution architecture

4 components · 2 layers
  1. Compute
    • GPT-5 Supports higher-value analysis and content tasks across Zenken’s core businesses.
    • Reasoning model Provides deeper support for management-level analysis, market strategy, and new-business evaluation.
  2. Serving
    • ChatGPT Enterprise Acts as Zenken’s secure organization-wide AI workspace for sales, marketing, translation, and internal knowledge work.
    • Custom GPTs Tailor AI support to repeatable internal tasks and business-specific workflows.

Architecture clues

  • Employees send an average of about 900 messages per person each month
  • Reasoning models are used for market analysis, competitive strategy, and new-business evaluation
  • Solution was selected because it met twelve requirements including security and complex reasoning support
  • Teams now ask ChatGPT during meetings to answer questions on the spot

Evidence from the source

"ChatGPT Enterprise was the only solution to meet all of these conditions."
"Proposals passing initial review up 15 to 20%"
"Weekly active usage of ChatGPT Enterprise exceeds 90%"
"Win rate for new deals up 5 to 10%"
Transparency Evidence & methodology 100/100 reference value · 87% 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
    7 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • 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 provenance8/10
    The publication date comes from publisher-controlled metadata.
  • 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

    Zenken customer story

    First-party
    https://openai.com/index/zenken/
    Source type
    Vendor-published customer story
    Published
    Jan 13, 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

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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