Enterprise AI Implementation Practice

Background: the reality of the situation

In 2025-2026, the capability of the Big Language Model (LLM) was dramatically changed. But one of the embarrassing realities is:The majority of the AI applications are still in the shallow stages of "ChatGPT Writing Weekly" and "Copilot Completing Codes"From "Personal Power" to "Enterprise Business Systems", there are three mountains in the middle:

• Data Wall• The generic model does not understand private data for enterprises — product banks, contract banks, passenger service orders, operating manuals, industry codes. Without an injection of private knowledge, AI answers in business scenes are either general or a serious piece of nonsense (fantasy).
• Process Wall: How can AI embed it into approval streams, passenger manifests, quotation processes? How does AI output trigger the next business move? This requires deep integration of AI capabilities with existing enterprise systems, rather than an independent chat window.
• Value WallBusiness managers are concerned not with "what model is used," but with "AI, how much people have saved me, how much conversion rate, how much errors have been reduced." Without quantifiable business indicators closed, AI projects are vulnerable to the end of "technology demo, business sector doesn't pay."

The methodology and capability scenario described hereZhiHua Tech (Shanghai e-Seok-shu Hsien-Shui Information Technology Ltd.)Deposition in practice in the context of intermination, which is used to demonstrate project scope and delivery methods and does not represent the disclosure of data by a particular client.

Third-stage approach to enterprise AI application

The three phases of the process, which are summarized in the present paper as follows:

Phase 1: Find - High Value AI scenes

Not all business processes are suitable for AI. The core task of the Find phase is to work with the business team.Site Assessment MatrixFilters the highest AI entry point for ROI:

• Site 4-Spects Range Assessment: The cross-axis is "AI-replaceable" (from supporting decision-making to full automatic implementation) and the vertical axis is "business value" (from marginal processes to core income/cost chain). Priority is given to high-replaceable, high-value first-hand quadrants, such as automatic classification and route of passenger manifests, review of contract terms compliance, maintenance knowledge case questions and answers, procurement request matching aids, etc.
• Data readiness assessment: Is the target scene sufficiently historical data (text, worksheet, dialogue records, operating manual) for model learning? Is the data structured? Is it marked? Data readiness determines the speed and effect limit of AI implementation.
• ROI Rapid estimateBased on the human input, error rate and processing time of the current process, the modified AI economy potential and efficiency enhancement space are estimated to form the basis for the project on 1-2 pages.

The output for the Wind phase is AI Opportunity Map— a comprehensive assessment document containing the ranking of the scene, expected benefits, data readiness and implementation difficulties.

Phase 2: Drill - Deep grinding AI capabilities

After the selection, the Drill phase focuses on the ability to get AI from 60 minutes to 90 minutes to deliver:

• RG (Retrievation Enhancement) knowledge base: The private files of the enterprise (PDF contract, Word Operations Manual, Excel product parameter table, Wiki knowledge base) are to be analysed through the document to the channel for the storage of the quantitative embedded text sections (Chunking) into the Embedding database, and the establishment of the enterprise-specific knowledge base. When a user asks a question, the system retrieves the most relevant document clips and then injects them as LLM tips in the context, significantly reducing the hallucination rate.
• Tip Project to Model fine-tuning: Develop a System Prompt template with extensive empirical verification for specific business scenarios (e.g., passenger talk, contract terms, technical inventory). For tasks required by high frequency, high accuracy (e.g., worksheet classification, intent identification), a LoRA fine-tune on the labeled data to open source models (e.g., Qwen, DeepSeek) to achieve near or even beyond closed source models at lower reasoning costs.
• Assessment and quality control: Create an AI output quality assessment system - Auto-assessment (rule-based and model-based scores) combined with manual check-ups. Each time a Prompt or model changes, the historical test set returns to verify that the B scenario does not deteriorate the output quality of the "optimise A scenario" because of "optimizing A scenario".
• Security fence• Security fences are placed on the model input output layer for the risk of leaking sensitive information, overstepping control, and improper output in the enterprise scene — sensitivity data desensitization, escape interception, export content compliance checks.

Phase 3: Embed - Embed business processes, create closed loops

AI's end point is not a model impact assessment report, but a model.Improved seamless integration and quantifiable operational indicators with existing operational systems：

• Enterprise Systems Integration: The AAI capabilities are embedded in existing platforms such as ERP, CRM, OA, passenger service, and worksheet systems through API / Webbook / Intermediate. Users can call AI in their daily working interface without switching to a stand-alone tool.
• Human-in-the-Loop human-power-coordinationThis model allows AI to have real efficiency gains, while manual clearance provides AI with continuous feedback data to mark and feed to the flying wheel.
• Closed circle of operational indicators: Defines quantifiable success indicators for each AI scenario - the "first solution increase X per cent" for the guest scene "the average processing time is reduced Y per cent" and the "review efficiency increase X times" for the contract review scene "the risk clause recognition accuracy rate Y per cent". The system automatically captures these indicators and output the AI value report in week/month.

Typical AI application

Smart customer service and worksheet processing

• Intent to identify and auto-routing: AIR Real-time Identification Intention (technical failure/refund/invoice query/complaint) when submitting a worksheet/online consultation, automatic route to corresponding processing queue, reducing the manual single time from an average of 3 minutes to a second.
• Question and answer: Based on the Enterprise Product Manual, Maintenance Guide, the RAG knowledge base case, which is built by FAQ, AI can answer 70-80% of the usual consultations, and complex questions automatically upgrade to manual seating and carrying summaries of the dialogue.
• Sitting assistant.: A manual seating facility, which provides real time advice on knowledge entries, solutions for historically similar worksheets and standardized response templates to help the junior seating facility to meet the response level of senior seats.

:: Smart contract review

• Key Article Identification(b) Automatically identify key elements of the payment terms in the contract, liability for breach of contract, confidentiality clauses, intellectual property attribution, competitive limitations, etc., and present them in structured tables.
• Early warning of risk clauses• Based on the rules defined by the corporate legal team and industry best practice, automatic labelling of risk clauses such as unreasonable default rates, too broad scope of authorization, missing termination conditions, and proposals for amendments.
• Differences• Compare the revised contract returned by the supplier with the standard corporate template, highlight the difference item and reduce the time for the word-for-word comparison.

:: Enterprise knowledge hub

• Cross-sectoral knowledge retrieval: Harmonized indexing of business knowledge to knowledge base, distributed on platforms such as Wiki, SharePoint, flybook documents, Notion, etc., where the most relevant document clips are accessible to staff in natural languages without the need to remember the document name and the storage path.
• Newcomers in the workforce AI Partner"The new employee, in any internal system problem, asks his assistant "How will the reimbursement process go?" "How will the company VPN fit?" "What approval is required for project development?" -- "AI provides immediate answers based on internal system files."
• Auto-generated proceedings: Conference transliteration results with access to flybooks/ nails/enterprise micro-intelligence, AI Auto-extracting Meeting Summary, To-dos and Decision Items, synchronized with project management tools.

Core components of the technical architecture

Deliverables

Intended value orientation

• Turn AI from "sample" to "productivity.": Instead of remaining at the level of personal dialogue assistants, AI is embedded in core business processes such as order processing, contract review, single route, which directly results in quantifiable efficiency gains.
• Reduce AI hallucinogenity to business acceptability: Increase the AI output accuracy of key business scenarios to over 90% through the RAG knowledge base + security fence + Human-in-the-Loop triple security.
• Create an AI capacity for continuous evolution: Each manual correction is providing feedback signals to the model, and AI capabilities evolve as usage increases, rather than gradually decline after a single delivery.
• Cost is visual and manageableLLM gateway provides a scenario-by-scenario, model-based Token consumption and cost statement, and managers can accurately assess the ROI of each AI scenario and make decisions that are scaled up or optimized.