Why Enterprise AI Implementation Must Start With 'Decision-Making Point Reconstruction'—And Why It Depends on It

The fundamental change of process deconstruction paradigm

I have previously sorted out and shared the five-step process for enterprises to implement AI, the most important of which is the first step: pain points sorting out and process deconstruction. Let's unfold the fatal misunderstandings and correct thinking process that illustrates this critical step.

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Some companies may secretly be glad that they have written process specification documents for some business processes or transactions, which can greatly save time on implementation and internal conflicts of internal disputes. However, because of this, they often fall into a fatal misunderstanding: Transfer the existing workflow directly into AI instructions .

Suppose that the financial reimbursement process of a certain enterprise is as follows (I know this fictional example is not very good. After all, it may not be worth reconstructing with AI. To help everyone understand this process)

传统流程逻辑

• Node is named after the operation role (accounting audit/supervisor approval)
• Decisions are implicit in operations ("audit" includes compound behaviors such as authenticity judgment, rule matching, etc.)
• Rely on human experience to deal with fuzzy scenarios (such as "the invoice seal is not clear but the amount is reasonable")

This kind of porting based on operation steps is the root cause of the failure of most AI projects. To achieve the real implementation of AI, it is necessary to use Decision point Reconstruct the process for atomic units. Here are five unavoidable reasons:

1. The cognitive boundary of AI: machines only understand "judgment" but not "operation".

Features of human workflow :

• Compoundability : A single step in the eyes of humans actually implies multiple decisions (such as "review reimbursement form" implies authenticity verification, rule matching, and risk judgment)
• Function : A large number of so-called steps actually rely on the executor's experience to deal with the gray area (such as "the invoice seal is vague but the amount is reasonable, if it is let go")

AI的问题 ：

• Stability issues in performing open tasks : AI requires clear input/output pipelines, and it is best to give enough examples to fine-tune the model or build a knowledge base.
• Be responsible for the backtracking problem of fuzzy judgment : Each judgment must have a preset rule boundary. If there is a fuzzy area, it is not conducive to backtracking problem for optimization and iteration.

案例对比 ：

• ❌ Error demonstration: directly upload an invoice, and then ask the AI: help me review the invoice → AI cannot understand the specific actions of "audit".
• ✅ Decision point disassembly:
  Disassembly into Boolean judgment (yes/no) or numerical output (amount value), so that AI can execute reliably. It also facilitates decision points that can be traced backwards errors when there are problems in the future, and some code breakpoint debugging.

2. The premise of engineering: without atomic decisions, there is no measurable AI capability

Evaluation dilemma of traditional processes :

• Node success rate cannot be quantified (such as "contract review quality" depends on subjective evaluation)
• The root of the error is difficult to locate (Is the entire process failure? Or is it an error in a sub-judgment?)

• Convenient to build an automated engineering verification mechanism :
  You can output logs for each node for automated detection. You can judge whether an exception occurs in real time and build a breakpoint retry mechanism, which can not only help iterate AI capabilities, but also save some of the costs of reruns throughout the process caused by some exceptions.

• Featureability Assessment :

For example, when the accuracy of the "Judge Force Majeure clause" is less than 75%, you can consider using other methods to replace this node in the process to form a human-computer collaborative process, rather than blindly upgrading (replacement) the entire system. This leads to poor results and the overall project failure, which is a bit of an FDD model in agile development management.

3. The cornerstone of human-computer collaboration: a clearly defined "handover surface"

When AI cannot completely replace humans, the decision point becomes the dispatching unit for human-machine division of labor:

• AI Responsibility Closed Loop : 100% automation of high-deterministic tasks (such as blacklist verification)

Counter-example warning : A bank handed over the "loan approval" to AI as a whole. Due to the failure to solve the solution points, a large number of errors in fuzzy cases (freelancer income verification) were caused, and it was eventually forced to return to the manual full process.

IV. The lifeblood of cost control: avoid over-reliance on AI

Typical waste of solution points without decomposition :

• Deploy large models for simple tasks (such as simple date format verification can be implemented by code logic, and you should not "lazy" all expect AI to be formed at one time)
• Unest the development cost on complex tasks (such as letting the basic OCR resolve non-standard contract terms)