Industry · Diversified groups, state-owned enterprises

Conglomerates & SOEs

See across subsidiaries, keep documents and policies in order, give audit real leverage.

Book an industry diagnostic →See the sample case

Pain points

What keeps owners up at night

Many subsidiaries and systems: group data is late and inconsistent

Drafting official documents and finding policies takes time

Heavy audit evidence collection

High bar for data security and domestic tech

Roles

Which roles AI transforms, and which it augments

“Transform”: AI executes, people spot-check. “Augment”: AI drafts and prompts, people decide.

Role	Mode	What AI does / what people keep
Group business analyst	Transform	AI consolidates subsidiary data and drafts analysis
Executive office	Augment	AI drafts documents, minutes and notices; people approve
Policy management	Augment	AI answers policy questions and compares versions
Audit assistant	Augment	AI organizes evidence and flags issues; auditors judge
Subsidiary reporting	Transform	Report filling and roll-up done by AI

Department agents

AI employees we typically deploy

Cockpit AgentDocument Drafting AgentPolicy & Training AgentAudit Assistant AgentReporting AgentCompliance Check Agent

Private deployment with domestic models and Xinchuang-compatible stacks; data never leaves your perimeter. Document sign-off and audit conclusions stay with people.

Super AI Workbench

Group control cockpit: the workbench for this industry

Demo UI with placeholder numbers.

BOOK.AI · Group control cockpitDemo UI · placeholder numbers

Cockpit
AI employees
Workflows
Knowledge
Audit log

Revenue¥ ——

Labor cost¥ ——

Output / person——

Open exceptions——

AI task completion by department (illustrative)

Sales

Ops

Finance

Supply

AI assistant · Monday briefing

Revenue, cost, output per person — one standard
Key matters and supervision closure
Document and policy workflow log
Audit issues and remediation

Sample case

How we would approach this industry

Sample scenario · not a real client

A diversified group (sample)

This is a sample scenario that illustrates our approach in this industry — not a real client. All results are modeled per company during the diagnostic.

01 Discover (2–3 weeks): map the organization and break down roles such as “Group business analyst” and “Executive office” into tasks and time share.
02 Build (90-day pilot): launch Cockpit Agent and Document Drafting Agent in parallel with the existing process.
03 Adopt & improve (months 4–12): extend to Policy & Training Agent and Audit Assistant Agent; adjust headcount only after each pool meets its guardrails.

Change in execution labor cost
Change in core process hours
Modeled payback

These metrics are modeled with your company’s real data during the diagnostic.

FDE in practice

Public FDE practice in this industry

Third-party cases from public reports — unrelated to the BOOK.AI sample above — to show how this industry is approaching AI.

Case information is compiled from public reports; figures are as reported by the original sources, and all rights belong to their owners. BOOK.AI was not involved in any of these projects.

Image from the source page: Zhejiang Communications Investment Group — © Intelligence Indeed; rights reserved by the owner · Image source

Public case · not a BOOK.AI clientConglomerates & SOEs · China

Zhejiang Communications Investment Group × Intelligence Indeed

FDE intensity: MediumCo-build (pilot → rollout)

Overall efficiency up 61%+ across 9 subsidiaries (provider-reported)

As reported by Intelligence Indeed · 2025 · provider-published

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Background

The finance shared-service center had many manual, error-prone processes.

How the FDE work was done

Piloted in the well-structured finance center in 2023, migrated 95 processes and rolled out to 9 subsidiaries.

Results (as reported by Intelligence Indeed)

Per-entity time cut to 5 minutes (83%+ faster); 61%+ overall efficiency across 9 subsidiaries.

Takeaways

Pilot in the best-prepared department, then roll out group-wide — the standard SOE playbook.

Provider: Intelligence Indeed
Delivery mode: Co-build (pilot → rollout)
Region: China

Read the original ↗Original title (Chinese): 世界500强浙江交通集团部署实在智能企业大脑+实在Agent：业务提效61% - 实在智能

English summary by BOOK.AI; figures as reported by Intelligence Indeed. Quotation marks indicate wording from the source.

Image from the source page: CRRC (CRRC Qingdao Sifang) — © Baidu AI Cloud; rights reserved by the owner · Image source

Public case · not a BOOK.AI clientConglomerates & SOEs · China

CRRC (CRRC Qingdao Sifang) × Baidu AI Cloud

FDE intensity: MediumCo-build

Simulation time cut from 24 hours to 10 seconds (single machine)

As reported by Baidu AI Cloud · 2025 · provider-published

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Background

High-speed-train aerodynamic simulation was slow and held back R&D iterations.

How the FDE work was done

Organised by CRRC, Sifang and the national high-speed train innovation center launched a simulation model with Baidu.

Results (as reported by Baidu AI Cloud)

Simulation from 24 hours to 10 seconds on a single machine.

Takeaways

Faster R&D simulation is the most tangible AI win for engineering groups.

Provider: Baidu AI Cloud
Delivery mode: Co-build
Region: China

Screenshot of the source page (captured 2026-10-19): CRRC (CRRC Qingdao Sifang) — © Baidu AI Cloud (page screenshot; content © source) · Image source

Read the original ↗Original title (Chinese): 中国中车-百度智能云

English summary by BOOK.AI; figures as reported by Baidu AI Cloud. Quotation marks indicate wording from the source.

Screenshot of the source page (captured 2026-10-19): Hangzhou City Construction Investment Group — © Tencent News / original author (page screenshot; content © source) · Image source

Public case · not a BOOK.AI clientConglomerates & SOEs · China

Hangzhou City Construction Investment Group × Zhipu AI

FDE intensity: MediumJoint project team

After “more than 5 months” of joint work: 35 dedicated agents live; ridership recognition accuracy above 93%

As reported by Tencent News (also on the Hangzhou SASAC site) · 2025-09-16 · media report

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Background

The group runs buses, emergency response, roads and bridges, and sanitation; dispatching and early warning relied on human experience.

How the FDE work was done

Over 5+ months of joint work the two sides built an “AI brain” on GLM-4.5, launched 35 dedicated agents and upgraded 26 existing business systems.

Results (as reported by Tencent News (also on the Hangzhou SASAC site))

Bus ridership recognition accuracy above 93%; 80% of scheduling tasks dispatched automatically, operating efficiency up 30%+; the flood agent predicts waterlogging risk 24 hours ahead and flagged 14 incidents in the pilot.

Takeaways

A first phase in about five months, upgrading legacy systems along the way, is a realistic pace for a large state-owned group.

Provider: Zhipu AI
Delivery mode: Joint project team
Region: China

Read the original ↗Original title (Chinese): 杭州城投发布人工智能产业大模型项目（一期）建设成果_腾讯新闻

English summary by BOOK.AI; figures as reported by Tencent News (also on the Hangzhou SASAC site). Quotation marks indicate wording from the source.

Image from the source page: CRRC Tangshan, Great Wall Motor, HBIS Digital and 3 others (Cosmic AI joint lab) — © Kingdee; rights reserved by the owner · Image source

Public case · not a BOOK.AI clientConglomerates & SOEs · China

CRRC Tangshan, Great Wall Motor, HBIS Digital and 3 others (Cosmic AI joint lab) × Kingdee

FDE intensity: MediumJoint lab + training

No figures disclosed

As reported by Kingdee · 2025-04-16 · provider-published

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Background

Complex cross-industry apps and a shortage of in-house AI talent.

How the FDE work was done

The lab provides AI planning, a platform and talent development, training client developers until they deliver projects on their own.

Results (as reported by Kingdee)

No quantified results disclosed.

Takeaways

Training client teams until they deliver on their own is the ideal end state.

Provider: Kingdee
Delivery mode: Joint lab + training
Region: China

Screenshot of the source page (captured 2026-10-19): CRRC Tangshan, Great Wall Motor, HBIS Digital and 3 others (Cosmic AI joint lab) — © Kingdee (page screenshot; content © source) · Image source

Read the original ↗Original title (Chinese): 苍穹AI共建联合实验室正式成立，携手共创智能未来！-金蝶软件

English summary by BOOK.AI; figures as reported by Kingdee. Quotation marks indicate wording from the source.

Screenshot of the source page (captured 2026-10-19): PowerChina — © Sina Finance / original author (page screenshot; content © source) · Image source

Public case · not a BOOK.AI clientConglomerates & SOEs · China

PowerChina × PowerChina in-house team

FDE intensity: LightIn-house embedded team

Rule configuration from 56 to 2 hours per set — 28x faster

As reported by Finance & Accounting journal (via Sina) · 2026-08-31 · journal

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Background

Financial review is huge in volume and complexity — the most labour- and risk-intensive step.

How the FDE work was done

Built a three-layer self-controlled stack and launched audit agents expense type by expense type, usable by ordinary finance staff.

Results (as reported by Finance & Accounting journal (via Sina))

376 form types, 8,120,000 (“812万”) reviews a year; 13 audit agents live; rule setup 56 → 2 hours (28x); review efficiency 20%+ over RPA.

Takeaways

Launch audit agents expense type by expense type so ordinary finance staff can run them.

Provider: PowerChina in-house team
Delivery mode: In-house embedded team
Region: China

Read the original ↗Original title (Chinese): 朱晨等：中国电建基于DRP的财务智能体建设实践与创新应用_新浪财经_新浪网

English summary by BOOK.AI; figures as reported by Finance & Accounting journal (via Sina). Quotation marks indicate wording from the source.

See public FDE practice across all 14 industries →

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