AI for Data and Business Intelligence
Processes, use cases and architecture
Augmented BI, anomaly detection, forecasting and natural-language queries. A semantic layer maps questions to approved metrics; queries are constrained and read-only. Forecasts are compared with a simple baseline on periods excluded from training.
Required data and applications
Data warehouse, ETL pipelines, business glossary, calculation rules and sufficiently stable history. Check grain, freshness, duplicates and series breaks before modelling.
Human accountability and limitations
Query accuracy, analysis lead time, forecast error and drift. Data owners approve metrics; analysts verify interpretations. Persuasive wording does not guarantee correct calculations.
Measuring benefits
The pilot compares a baseline with the target process on representative cases. Business owners define quality thresholds and the right to revert to manual processing before launch.
Bringing the right expertise together
FAQ
How should an AI programme be measured?
Compare outcomes with a baseline: quality, end-to-end time, total cost, adoption and incidents. Measure review effort and errors, not just the number of generated answers.