In the past, enterprise digitalization and operational data analysis often relied on structured data: tables, databases, logs.
However, most information in the real world actually exists in the form of natural language: business information flows, communication records, user reviews, product feedback, news...
The first problem enterprises face when promoting digital transformation is how to structure historical unstructured data, but most companies lack this capability. When confronted with vast, diverse, and cross-system information records, the digitalization process often struggles to make progress.
What's more complex is that the Chinese market is not a market defined by SaaS. Each enterprise has its own unique management methods, leading to diverse and difficult-to-unify data types.
This creates an awkward reality: SaaS products are misaligned with enterprise needs.
SaaS vendors typically hope to use a standardized set of operational methodologies and software tools to help enterprises complete AI-driven upgrades in production and operations. But they face various non-standardized and difficult-to-change enterprise management practices.
The ultimate result is that SaaS vendors can only provide a basic set of software tools and conduct deep customization for different enterprises. This business model represents a huge investment and meager profits for software companies.
How AI Changes This Situation
The true value of AI lies in its processing of natural language. It does not require enterprises to first standardize processes; instead, it can directly process unstructured natural language data and transform it into computable, analyzable results.
Simultaneously, it can align the same type of data expressed in different ways.
For example, using multimodal capabilities, it can merge transaction records from Alipay, WeChat Pay, and supermarket receipts into a unified, digitally standardized transaction ledger. This not only converts physical world information into digital information but also unifies data of different formats and languages into a single standard format for centralized management.
In terms of data processing, AI's capabilities can be summarized into four levels:
Cross-Language Understanding Capability
AI can recognize not only numerical language but also natural language simultaneously. For instance, skills, events, time, objects, and by combining context, it understands their true meaning, extracts key information, and converts it into structured data.
- Example: "Customers frequently complain about delivery delays" is recognized as 【Link=Logistics, Problem=Delay, Sentiment=Negative】;
- "Sales have failed to meet targets for three consecutive months" is recognized as 【Metric=Sales, Trend=Decline, Period=3 months】.
Structuring Capability
AI can decompose vague text into clear data units, converting it into tables or JSON format for easy use by enterprise management systems.
- Original input: "Responsible for team goal setting and performance tracking";
- Conversion result: {Responsibility: Team Management, Action: Goal Setting/Performance Tracking, Object: Team Members}.
Data Alignment Capability
AI can align similar information expressed in different ways, avoiding the loss of value due to "different ways of saying things."
- "Customer feedback," "after-sales issues," "user opinions" → aligned to 【Topic: Customer Satisfaction】;
- "Insufficient inventory," "out of stock," "supply chain disruption" → aligned to 【Problem: Supply Chain Risk】.
Data Analysis Capability
After completing structuring and alignment, AI can further calculate and reason with the data to generate valuable conclusions.
- Trend Analysis: The frequency of a certain type of problem gradually increases;
- Matching Analysis: A certain result deviates from business requirements;
- Risk Warning: A certain type of negative feedback concentrates in a specific area.
Case Study: Person-Job Matching (From the Job Seeker's Perspective)
Taking person-job matching within enterprises as an example, in recruitment and job-seeking scenarios, matching positions with resumes is a typical natural language data matching problem. When job seekers browse job postings, traditional methods rely on the job seeker's subjective judgment, which is both time-consuming and prone to being affected by experience and information asymmetry.
Here, a design approach for a person-job matching application is presented.
Demand Analysis: By summarizing the problems job seekers encounter during the job search process, five basic job-seeking needs are abstracted.
- A large number of fake job postings are rampant, wasting time and energy;
- Job Descriptions (JDs) are overly broad and comprehensive, making it difficult to assess fit;
- Job seekers need to pass HR first before they can speak with the actual hiring department;
- The scope of employment is too narrow, unable to break through the "experience wall";
- Labor dispatch companies disguise themselves as direct-hire recruiters.
JD Effectiveness:
| Dimension | Dimension Weight | Metric | Evaluation Method | Score (0-10) | Necessity Weighting (Non-essential 0.8, General 1.0, Important 1.2, Critical 1.5) | Label (Based on raw score, before weighting) | Data Source |
|---|---|---|---|---|---|---|---|
| JD Level | 0.6 | Requirement Clarity | Does the JD specify concrete business goals/pain points (e.g., "increase conversion rate" vs. "improve user experience")? | 0=Vague, 5=Directionally unclear, 10=Clear goals | Important=1.2 | 0=Vague, 5=Directionally unclear, 10=Clear goals | JD Text |
| Metric Verifiability | Are KPIs/quantitative targets/deliverables mentioned? | 0=No metrics, 5=Partially mentioned, 10=Clearly quantified | Important=1.2 | 0=None, 5=Partially mentioned, 10=Clearly quantified | JD Text | ||
| Responsibility Focus | Are responsibilities centered around 1–2 core areas, avoiding being overly broad? Also consider the number of items (3–5 is ideal). | 0=List-like, 5=Partially relevant, 10=Focused and clear | Important=1.2 | 0=List-like, 5=Partially relevant, 10=Focused and clear | JD Text | ||
| Self-Consistency | Do the job level, experience requirements, and salary range in the JD match? | 0=Contradictory, 5=Partially unreasonable, 10=Completely reasonable | Important=1.2 | 0=Contradictory, 5=Partially unreasonable, 10=Completely reasonable | JD Text | ||
| Tool & Methodology Details | Are real tools/processes/tech stacks mentioned (e.g., SQL, tracking, A/B testing, JIRA, etc.)? | 0=Not mentioned, 5=Generic, 10=Specific | Important=1.2 | 0=None, 5=Generic, 10=Specific | JD Text | ||
| Reporting Line & Team Info | Are reporting manager/team size/collaborating parties stated, and do they match the job level? | 0=Not mentioned, 5=Vague, 10=Clear and reasonable | General=1.0 | 0=None, 5=Vague, 10=Clear and reasonable | JD Text | ||
| Recruitment Channel & Update Frequency | Has the JD been updated recently? | 0=Over 1 month, 5=Within 1 month, 10=Updated within 7 days | Non-essential=0.8 | 0=Overdue, 5=Channel vague or expired, 10=Official/authoritative platform recent update | JD Text | ||
| Company Level | 0.4 | Business Dynamic Alignment | Does the JD position align with the company's latest strategy/products/expansion? | 0=Unrelated, 5=Weakly related, 10=Highly aligned | Important=1.2 | 0=Unrelated, 5=Weakly related, 10=Highly aligned | JD Text/Public News |
| Recruitment Entity Consistency | Does the JD publishing company match the actual hiring company? Does the work address match the company's registered address? Is there a risk of proxy recruitment/outsourcing? | 0=Inconsistent, 5=Vague, 10=Consistent | Critical=1.5 | 0=Inconsistent, 5=Vague, 10=Consistent | JD Text | ||
| Employment Transparency | Does the JD/website clearly state "direct hire/formal employee"? | 0=None, 5=Vague, 10=Clear | Non-essential=0.8 | 0=None, 5=Vague, 10=Clear | JD Text/Website | ||
| Employer Reputation | Evaluations from multiple channels like Maimai, Zhihu, Boss, requiring cross-validation. | 0=Many negative reviews, 5=Mixed, 10=Good reputation | General=0.8 | 0=Many negative reviews, 5=Mixed, 10=Good reputation | Search Engine/API/MCP | ||
| Recruitment Scale & Company Size Alignment | Does the number of open positions match the company's size and stage? | 0=Unreasonable, 5=Partially reasonable, 10=Reasonable | Non-essential=0.8 | 0=Unreasonable, 5=Partially reasonable, 10=Reasonable | Search Engine/API/MCP | ||
| Recruitment Timeliness | Is the JD posted for a long time without being taken down, or does the position frequently reappear? | 0=Constantly posted, 5=Occasionally updated, 10=Recently updated | General=1.0 | 0=Constantly posted, 5=Occasionally updated, 10=Recently updated | JD Text/Local Storage Monitoring | ||
| Business Info Risk Check | Are there serious legal violations, dishonest被执行人 records? | 0=High risk, 5=Minor anomalies, 10=No risk | Critical=1.5 | 0=High risk, 5=Minor anomalies, 10=No risk | Search Engine/API/MCP/Crawler |
$$ Comprehensive Score = \frac{\sum_{Dimension} \sum_{Metric} (Actual Score \times Metric Weight \times Dimension Weight)}{\sum_{Dimension} \sum_{Metric} (Full Score 10 \times Metric Weight \times Dimension Weight)} \times 100% $$
Person-Job Matching Evaluation Table (Including Scoring Standards)
Usage: Use this table in conjunction with a personal resume for scoring, provided the position's credibility assessment is qualified.
Each metric is scored 0–10, combined with necessity weighting, to finally calculate the person-job match percentage.
| Dimension | Metric | Evaluation Method | Scoring Standard (0–10) | Score (0-10) | Necessity Weighting | Label (Based on raw score, before weighting) | Data Source |
|---|---|---|---|---|---|---|---|
| Hard Skill Match | JD Tech/Tools vs. CV Skills | Keyword-by-keyword comparison (SQL, A/B testing, ASR, Electron, etc.) | 0 = No relevant skills, 5 = Partial skill match/has alternatives, 10 = Fully covers | Critical = 1.5 | 0 = No relevant skills, 5 = Partial skill match/has alternatives, 10 = Fully covers | ||
| Experience Fit | JD Industry/Years vs. CV Experience | Compare industry and years of experience | 0 = Completely unrelated, 5 = Transferable experience, 10 = Industry/years fully match | Important = 1.2 | 0 = Completely unrelated, 5 = Transferable experience, 10 = Industry/years fully match | ||
| Project Achievement Alignment | JD Business Goals vs. CV Achievements | Compare KPIs or achievements | 0 = No relevant achievements, 5 = Has similar cases, 10 = Achievements directly align with JD goals | Important = 1.2 | 0 = No relevant achievements, 5 = Has similar cases, 10 = Achievements directly align with JD goals | ||
| Responsibility Focus Match | JD Core Responsibilities vs. CV Core Experience | Compare responsibility areas | 0 = Completely mismatched, 5 = Partially relevant, 10 = Highly aligned | Important = 1.2 | 0 = Completely mismatched, 5 = Partially relevant, 10 = Highly aligned | ||
| Soft Skill Fit | JD-required Communication/Collaboration/Learning Ability vs. CV Reflection | Review resume/experience descriptions | 0 = Not reflected, 5 = Generally reflected, 10 = Clearly and prominently reflected | General = 1.0 | 0 = Not reflected, 5 = Generally reflected, 10 = Clearly and prominently reflected | ||
| Career Development Fit | Position Career Path vs. CV Long-term Goals | Compare development direction | 0 = Completely mismatched, 5 = Partially aligned, 10 = Long-term highly aligned | General = 1.0 | 0 = Completely mismatched, 5 = Partially aligned, 10 = Long-term highly aligned | ||
| Transfer Potential (Encouragement Dimension) | Has cross-industry experience/quick learning cases? | Find adaptation/transition cases from CV | 0 = No relevant experience, 5 = Has some cases, 10 = Has clear cross-industry success cases | Important = 1.2 | 0 = No relevant experience, 5 = Has some cases, 10 = Has clear cross-industry success cases |
Scoring Calculation Method
- Individual Item Score = Score × Necessity Weighting
- Total Score = ∑ All Individual Weighted Scores
- Full Score = ∑(10 × Necessity Weighting)
- Person-Job Match Percentage (%) = (Total Score ÷ Full Score) × 100%
Output Results
- Above 80% → High match, worth focusing on applying
- 60–79% → Moderate match, can apply, highlight strengths in interview
- 40–59% → Low match, suitable for trying or accumulating interview experience
- Below 40% → Match too low, not recommended to invest excessive effort
Position Credibility Assessment: Use AI to list key evaluation metrics, score recruitment information for credibility, filtering out fake postings or unclear position requirements.
- Requirement clarity and verifiability of recruitment information (whether it includes clear business goals, KPIs);
- Responsibility focus (is it overly broad or concentrated on core tasks?);
- Details of tools and methodologies (are real tools, processes, or tech stacks mentioned?);
- Company information transparency (direct hire vs. outsourcing, business info risks, employer reputation, etc.).
Resume and Position Match Calculation: Compare position requirements with the resume item by item, calculating the match percentage.
- Hard Skill Match: Required tools/skills vs. resume skills (e.g., SQL, A/B testing, Electron, etc.);
- Experience Fit: Required industry and years vs. resume experience;
- Project Achievement Alignment: Business goals vs. achievements on resume (whether there are KPI or case alignments);
- Soft Skill Fit: Whether communication, collaboration, learning abilities are reflected in the resume;
- Transfer Potential: Whether cross-industry experience or quick learning cases exist.
Calculation Method:
- Individual Item Score = Score × Necessity Weighting
- Match Percentage (%) = (Total Score ÷ Full Score) × 100%
Analysis Results:
- Above 80%: High match, worth focusing on applying;
- 60–79%: Moderate match, can apply and highlight strengths in interview;
- 40–59%: Low match, can try or accumulate experience;
- Below 40%: Not recommended to invest excessive effort.
Through this method, AI can not only decompose vague natural language into calculable metrics but also align different expressions and conduct quantitative analysis on this basis. This approach allows job seekers to objectively assess the true value of a position and enables enterprises to screen talent more efficiently, achieving bidirectional data-driven decision-making.
More Application Scenarios: From Recruitment to Enterprise Management
Person-job matching is just one entry point for AI's natural language processing in enterprises.
In fact, the vast majority of information in enterprise management exists as natural language: job descriptions, employee weekly reports, customer complaints, supply chain communications, contract terms... If this data remains merely at the "text" level, it has almost no computational value or is difficult to utilize. Once parsed, aligned, and structured by AI, it can be transformed into measurable metrics, providing real-time insights and decision support for management.
Recruitment and Human Resource Management
In the recruitment phase, AI can automatically generate position requirements based on project needs within the enterprise. When receiving candidate resumes, it can automatically parse the job description and candidate resume, convert both into structured data, and calculate the person-job match, thereby helping HR screen suitable candidates faster.
But recruitment is just one part of HR. AI's capabilities can extend to performance and training management. For example, employee OKRs, weekly reports, and evaluation comments are often described in natural language. AI can identify keywords and behavioral patterns within them. For instance, "led team to complete cross-departmental collaboration project" can be transformed into "Capability=Cross-departmental Collaboration, Result=Project Delivery." This not only helps in more fair performance evaluation but also helps enterprises identify skill and knowledge gaps in employees, enabling more precise training.
Customer Relationship and Market Feedback
In customer relationship management, enterprises are often immersed in vast amounts of scattered textual data: customer service tickets, social media comments, user feedback forms, etc. The value of this information is often overlooked, or relies on manual summarization, which is inefficient, incomplete, or misses key details.
AI can automatically perform semantic clustering and sentiment analysis on this textual information, unifying "delivery delay," "slow shipping," "logistics too slow" into the category "Logistics Problem," while labeling it as negative sentiment. When the frequency of a certain type of problem suddenly increases, AI can provide an early warning, prompting the enterprise to accelerate improvements in the supply chain.
This capability not only improves customer satisfaction but also provides first-hand market intelligence for product iteration.
Supply Chain and Operations Management
The stability of the supply chain is often reflected in countless communication records and contract texts.
AI can automatically scan procurement contracts, extracting key clauses such as delivery dates and liability for breach, thereby quickly identifying potential risks.
Simultaneously, it can parse informal texts like emails and order conversations. For example, "supplier's recent production capacity is insufficient" can be labeled as "Risk=Unstable Supply."
When this structured data is further input into operational systems, it can form trend analysis and prediction models, helping management proactively address issues like inventory shortages and transportation delays. For enterprises, this means a significant improvement in risk prevention and control capabilities.
Strategic Decision-Making and Corporate Governance
Management deals with a large number of documents and meeting minutes daily, with severe information redundancy. AI's value lies in automated extraction and aggregation.
It can extract core topics from dozens of pages of strategic reports, such as "Focus: International Market Expansion," "Risk: Compliance Review." It can also identify communication bottlenecks from cross-departmental email exchanges, discovering overlapping responsibilities or information delays.
This "information compression" capability allows senior executives to quickly grasp the overall situation and focus on strategic direction. Meanwhile, AI can also monitor external natural language data, such as regulatory