Summary of Key Points
This article focuses on the AI upgrades of existing B-side systems (such as CRM and reporting systems used by enterprises). It first identifies three major shortcomings of traditional reporting and warning functions: the output does not address decision-making issues, data validation lacks business logic, and rigid rules fail to keep up with changes. The core approach to AI upgrades is to "compatibility with existing systems + precise supplementation," rather than complete reconstruction. The article also shares practical pitfalls encountered during implementation (such as attempting to include all functions, neglecting data security, and using technical indicators that are disconnected from business needs). Finally, it emphasizes that AI upgrades should revolve around user value, leading to three key transformations: from simply moving data to providing analytical insights that empower decision-making.
The Painpoints of Traditional Reporting and Warning Systems: Three “Mismatches” That Exhaust Business Users
Traditional reporting and warning systems seem functional, but in reality, they are like semi-finished products, with three main issues:
1. Numbers are provided, but no answers: Reports may show that the contract amount is 10 million, but when you ask if this figure is good or what to do next, they cannot provide useful information. Business users still have to organize the data and hold meetings for discussion, meaning the system has only done the work of moving data without providing analysis. Even more frustrating is that changing the perspective (e.g., viewing contract amounts by region and customer type) requires contacting developers to modify templates, which can slow down decision-making significantly.
2. Format is correct, but logic is wrong: The system checks things like whether the number of phone digits is sufficient or whether required fields have been filled out, but it ignores business logic. For example, if you enter a package price that is half lower than the average for similar users, the system will not warn you; you need to manually verify the data. As a result, 15% of the report data may be incorrect, and 30% of the warnings might be false, leading to delays and potential business impacts.
3. Rules are fixed, but business is changing: Warning thresholds are set statically (e.g., an alarm when the prediction accuracy for electricity usage is below 90%). However, during holidays or seasonal fluctuations, or for different types of customers, these static rules do not adapt effectively to dynamic business conditions.
AI Upgrades Are Not About “Overhauling Everything,” but Adding Enhancements to Existing Systems
Many people think they need to replace the entire old system with an AI upgrade, but this is unnecessary. The old system has already established processes, and users are accustomed to it. The better approach is to add new AI functions as enhancements without changing the existing operations, allowing users to gradually adopt them.
- Reports Can Speak: Add an AI interpretation feature. Without modifying the reports, users can click on “AI Analysis” to get straightforward summaries (e.g., “New customer contracts in the eastern region have increased by 10%; consider expanding channels”) and see highlighted anomalies (e.g., “A customer’s prediction accuracy is only 20%—perhaps the production plan is incorrect”). AI can even generate charts based on user queries, eliminating the need to contact developers.
- Data Accuracy Is Guaranteed: Incorporate intelligent validation mechanisms. When data is entered, the system checks not only the format but also the business logic. For example, if the order amount exceeds three times the customer’s historical maximum, it will prompt confirmation. Missing delivery dates will be noted, and errors can be automatically corrected (e.g., changing “inventory 10” to “inventory 100” based on historical data). Start by addressing 80% of common errors.
- Rules Can Adapt: Upgrade the rule engine to include dynamic rules. For example, when setting up inventory warnings, the system can recommend rules based on sales forecasts. Business users can simply specify conditions (e.g., “Reorder when sales increase by 50% in the past 7 days and inventory lasts for less than 30 days”), and AI will automatically update the system rules. The system can also optimize these rules based on user feedback.
Practical Pitfalls to Avoid
While the technology behind AI upgrades is not difficult, the challenge lies in balancing various aspects during implementation. Here are three important lessons:
1. Don’t Try to Include All Functions: Let users get used to the system first. In our initial upgrade, we included all features (interpretation, validation, and prediction), but users found it too complex. We adjusted the approach by starting with basic interpretation and validation; gradually adding more functions later on. AI upgrades should be gradual and user-friendly, not a dramatic revolution.
2. Data Security Is Crucial: Financial and customer data in B-side systems is sensitive, especially in government, state-owned, and financial industries. Do not transfer data directly to third-party models; use local, lightweight models with data anonymization (e.g., replacing customer names with “Customer A”) to ensure both AI effectiveness and compliance.
3. Don’t Overpromise with Technical Metrics: Users want time-saving solutions and reduced losses. While high AI accuracy and low false-positive rates are appealing, they are more concerned with practical benefits like saving hours or reducing costs. Technical metrics must be aligned with business goals.
The Core of AI Upgrades: Returning to User Value and Achieving Three Transformations
AI upgrades for B-side systems are not about showing off technology; they aim to solve real problems. The ultimate goal is to achieve three transformations:
1. From Data Presentation to Analytical Insights: Reports should go from being mere numbers to providing actionable advice.
2. From Passive Validation to Proactive Error Prevention: Prevent errors at the point of data entry, avoiding the need for costly rework after reports are generated.
3. From Static Rules to Dynamic Adaptation: Rules should adapt to business changes, eliminating the need for manual adjustment of thresholds daily.
Product managers do not need to be AI experts, but they must understand the business challenges. AI is a tool; its purpose is to help solve problems and improve efficiency for enterprises. Future AI upgrades will become more user-friendly (e.g., through plugins and API calls), but the focus on business-driven approaches will remain unchanged.
With this analysis, even non-financial professionals can easily understand the logic and value of AI upgrades for B-side systems.
(The full article is approximately 1800 words.)