AI is changing how people interact with data, but it does not replace the need for clean, governed and well-modeled data. Most AI-in-BI initiatives that disappoint do so because the foundation was not ready.
Where AI adds real value today
Demand forecasting
Forecasting demand from historical sales and inventory data supports purchasing and production planning. Start simple with a seasonal baseline, measure accuracy, and add complexity only when it improves results.
Experiment tracking with MLflow
MLflow records parameters, metrics and model versions, so forecasting experiments are reproducible and comparable. Running it in Docker keeps the setup portable.
Natural-language assistance with LLMs
Large language models can help explain metrics, draft SQL, summarize dashboards and document data models. They work best on top of a well-described semantic layer, and their output should be reviewed before anyone acts on it.
Prerequisites
- Consistent, governed gold-layer data
- Clear metric definitions
- Access controls that also apply to AI-driven queries
- A way to measure whether the AI output is accurate
A sensible path
Build the warehouse, establish trusted dashboards, then introduce forecasting and AI assistance where they answer specific business questions. AI amplifies the quality of the data underneath it, in both directions.
Fiaz Anjum
Senior Data Engineering & Business Intelligence Professional | Digital Transformation Specialist
- AI
- Forecasting
- MLflow
- LLMs