Anonymized provincial state power company subsidiary · Multi-line AI workflow and organizational capacity-building

A Evidence level

Datawhale FDE Case 100 | No. 14: From technical delivery to business-owned AI workflows in a state-owned enterprise

Publisher: Datawhale FDE100 · Official case collection PDF · Direct sources at the case level

Claim origin: Disclosure by the author of the case · Independent verification: No · Accessed: 2026-09-19

Evidence level measures whether a source can be located and reviewed; it does not mean vendor-reported claims were independently audited.

Deep case | Key delivery chain is substantially documented 12 / 12

Business context2 / 2

Transformation workflow2 / 2

Technical workflow2 / 2

Human roles & governance2 / 2

Measured outcomes2 / 2

Source traceability2 / 2

All six dimensions meet the current completeness threshold.

The most interesting example of this case is that the project did not stop at the Unified AI platform, but placed job extraction, scenes of Agent and organizational training in the same link to the ground.

Business problem

Several lines of operation for electric power suppliers have continued to grow, but professional posts such as legal, financial and internal control have been limited by staffing constraints; a great deal of audit experience is in the minds of employees, and the base of AI has not actually entered the business stream.

Solution

The knowledge extraction teacher first combs the hidden knowledge and processes of the job, then builds a unified AI platform comprising the knowledge base, multimodel dispatch, Agent programming and MCP interfaces, gradually lands Agent on the basis of the legal, financial, administrative and other scenarios, and builds the self-alternation capacity of the business team through training.

Technical architecture & production workflow

Step 01

OA / ERP / Data Center and Job Knowledge

→

Step 02

Knowledge extraction and implicit process visibility

→

Step 03

Harmonization of AI platforms: knowledge base, multimodels, Agent programming, MCP

→

Step 04

Legal/financial/administrative sceneAgent

→

Step 05

AI initial screening and execution. Final confirmation.

→

Step 06

Training, use of feedback and continuous deposition of new knowledge

Key technology & infrastructure components

Enterprise knowledge baseMultimodel schedulingAgent presentation.MCP/business system interfaceOA/ERPHuman-in-the-loop

Human roles & accountability

AI is responsible for duplication of tasks such as initial screening of contracts, reconciliation of notes, etc.; legal, financial and operational personnel retain final recognition and responsibility.

FDE delivery actions

Entering legal, financial, operational, etc. to sort the hidden process
Conversion of expert experience into rules, exceptions and conditions for manual upgrading
Reuse existing OA, ERP and data hubs without re-engineering of business systems
Construct Agent by scene and clarify the boundaries of human responsibilities
Promotion of personnel and operations participation in training and operation

Reusable delivery patterns

The A.I. base line is not the same as business use.
Knowledge extraction and process comb should have been developed earlier than Agent.
Business AI transformation requires joint business, personnel and IT responsibility
Validate values with narrow scenarios and expand the Agent matrix

Business outcomes & delivery results

Datawhale reviewed the case and stated that the legal scene would save one legal production, covering the costs of approximately 10 person/years of labour in legal matters, finance, personnel, operations, technology, etc.

Evidence boundaries & verification notes

Page marked as VERIFED CASE/ approval.
The effect figures are disclosed by the issuer of the case and do not represent an independent audit.

Primary source: Datawhale FDE Case 100 | No. 14: From technical delivery to business-owned AI workflows in a state-owned enterprise ↗ Additional sources: Datawhale FDE100 case webpage | No. 14 ↗