First, give conclusions that can be used for decision-making
The task of first validation is usually clearly input-output, weekly recurrence, current cost-recording, and error-identifiable by hand, such as document extraction, knowledge retrieval, worksheet classification or sales preparation. High-risk decision-making, lack of legal data, and unclear processes of responsibility are not appropriate for direct automation.
What conditions need to be identified before judgement is made?
The same question may have different answers under different business, data and project phases. It is suggested that the following conditions be checked and that the common findings on the web be incorporated into their own projects.
Suggested order of advance
First, we'll be clear about the target and the border.
Create a scene list that is sequenced according to value, feasibility, risk and data readiness.
Validation Key Dependence
Select a mission to create a fixed assessment set and record the pre-reform operational baseline.
Development of assessable outcomes
Comparison of model effects, manual intervention, running costs and consequences of errors with PoC.
Make sure you decide the next step with the real results.
(c) Enter a small range of test operations to complete privileges, logs, interfaces and continuous evaluation.
How do you understand it in the actual business?
The first stage is to select a sample of the last two months of de-sensitization, test field extraction, missing tips and manual confirmation, and not rush to automatically send a quotation. The risk is more manageable when CRM and quotation systems are connected once the quality, processing time and correction costs have been met.
The easiest pit to step on.
First, buy a big model or one-stop plane, then look for a business use scenario.
It's only a dozen beautiful samples. There's no fixed evaluation.
Ignore manual liability, error processing and continuous operations after going online
How should we end up receiving and confirming?
The first AI scenario should deliver business baselines, sample and assessment collections, version logs, impact and cost reporting, risk boundaries, manual processes, and next-stage routes. The mark of the AI transition success is not “access to models”, but rather a methodology that businesses have in place to assess and expand applications on a continuous basis.
When preparing to communicate with suppliers or internal teams, it is recommended that current processes, representative samples, existing systems, planning time and budget levels be brought. First, the unknown items are clearly marked, and then the decision is made to use diagnostics, PoC, fixed-range projects or ongoing research and development, which is usually more reliable than a direct demand for a price and duration without borders.