Enterprise AI Model vs. Process First
Enterprise AI should start with business tasks and process results, not with the first procurement model. When processes are confused and data are not subject to liability, AI will only magnify inconsistencies. First, it will identify users, input, expected results, manual clearance and system actions, then select models, RAGs, rules or automated routes using real samples.
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Enterprise AI should start with business tasks and process results, not with the first procurement model. When processes are confused and data are not subject to liability, AI will only magnify inconsistencies. First, it will identify users, input, expected results, manual clearance and system actions, then select models, RAGs, rules or automated routes using real samples.
The video content of this issue is read
The following are structured textual interpretations of the video for the current period, which allow for quick reading, internal discussion and search; it is not verbatim subtitled. Around “businesses should use AI first, model or process first”, it is suggested that a distinction be made between symptoms, business causes and system improvements before deciding whether process adjustments, data governance, system integration, automation or customization development are required.
1. How the AI scenario should be defined
The process itself is confusing and data is not accountable. First, it identifies users, inputs, expected results, manual clearances and system actions, then selects models, RAGs, rules or automated routes using real samples. For this point of judgement, the actual tasks, documents, communication records or system logs should be drawn to check frequency, waiting time, back-to-work costs, responsible positions and exceptions.
2. Why processes and data preparation precede models
The process itself is confusing and data is not accountable. First, it identifies users, inputs, expected results, manual clearances and system actions, then selects models, RAGs, rules or automated routes using real samples. For this point of judgement, the actual tasks, documents, communication records or system logs should be drawn to check frequency, waiting time, back-to-work costs, responsible positions and exceptions.
3. How to control first-stage inputs with the PoC
The process itself is confusing and data is not accountable. First, it identifies users, inputs, expected results, manual clearances and system actions, then selects models, RAGs, rules or automated routes using real samples. For this point of judgement, the actual tasks, documents, communication records or system logs should be drawn to check frequency, waiting time, back-to-work costs, responsible positions and exceptions.
What should we do with this scene?
The first step in the project is to determine what information projects should do, in terms of size, construction route, product selection and AI entry points. The first step is to define real input, desired output, tool privileges, manual clearance, unusual handling and operational acceptance indicators, and then decide whether to use rules, scripts, API, Codex or other AIAgents, around whether an enterprise wants to use AI.
The verification of conditions, liability, data sources and exceptions is done using real samples, and the presentation is not used as a substitute for production evidence.
The verification of conditions, liability, data sources and exceptions is done using real samples, and the presentation is not used as a substitute for production evidence.
The verification of conditions, liability, data sources and exceptions is done using real samples, and the presentation is not used as a substitute for production evidence.
Suggested paths for improvement
- 1Recovery status process and cost baseline
Selecting recent and representative tasks and anomalies, identifying participants, input outputs, time and current costs.
- 2Prioritization by value, risk and conditions of implementation
Distinction between actions that are self-executing, that require manual confirmation and that prohibit automatic processing.
- 3First, try a quantitative closed ring.
Start with the draft, a copy or a limited scene, and keep the abnormal transferer and retreat.
- 4Extension or re-routing of routes through data decisions
Continuous observation of accuracy, adoption, processing cycle, error and real business results.
How to automate the receipt and inspection is really effective.
The acceptance cannot be based solely on whether a single demonstration runs. The following results should be observed continuously using independent samples and real anomalies, and pre-modification baselines of the same calibre should be maintained:
- Whether core processes are really shortened
- Whether key data form a uniform calibre
- Continued use of staff
- Reconciling input, operating cost and business value
The authorization, approval, audit and manual takeover must also be verified when it comes to the amount, customer commitment, privacy, compliance, production change or deletion operations.
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