First, give conclusions that can be used for decision-making
The traditional BI addresses the issue of what is known to be expected to be stable in a fixed manner, and AI business analysis is more suitable for the scene of “user raising ad hoc questions in natural languages, systematically interpreting calibrations and leading down drills.” AI can break down “the main reason for the decline in profits in East China this month” into time, region, profit indicators and lower drilling dimensions, but definition of profit, organizational scope and data privileges must come from the controlled semantic level.
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.
Collecting recent real business problems and the time available for processing.
Validation Key Dependence
Distinguishing between fixed statements, ad hoc queries and analytical interpretation tasks.
Development of assessable outcomes
Build small-scale smart questions from PoCs with existing indicators and competencies.
Make sure you decide the next step with the real results.
More correct, waiting time, adoption rate and maintenance costs.
How do you understand it in the actual business?
The project retains the original BI board as an authoritative source, adding natural language queries and drilling access for governance indicators, avoiding rebuilding the entire statement. The examples do not represent the performance of a particular client, and the actual conclusions need to be validated in conjunction with the enterprise’s own business volume, sample, system, and liability boundaries.
The easiest pit to step on.
Replace all stable BI reports for AI
Without a calibration, the model can understand the business words.
Just to see if the answers flow, not against the authoritative figures.
How should we end up receiving and confirming?
Enterprises should use fixed sets of questions to compare AI answers and authoritative statements, check calibre, authority, source, ambiguity and drilling results, and identify which questions continue to be handled by traditional BI or data personnel.
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.