Introduction
Business intelligence delivers trusted, repeatable numbers to decision-makers through dashboards, reports and scheduled summaries. Its value rests on consistency: everyone should mean the same thing by "revenue" or "active customer".
How AI applies
AI makes BI more conversational and less manual. People can ask questions of governed metrics, and models can draft the written commentary that accompanies a report. The foundation remains a well-defined data model; AI sits on top, not instead of it.
Applications and use cases
- Conversational BI: ask questions of approved metrics.
- Automated commentary: weekly summaries written from verified numbers.
- Anomaly alerts: notify owners when a metric moves unexpectedly.
- Metric documentation: generate plain-language definitions.
- Forecast overlays: projections shown on existing dashboards.
Technologies involved
BI platforms and semantic layers that define metrics once, data warehouses, scheduling tools, language models for narrative, and forecasting and anomaly libraries. Many BI products now include built-in AI features, so evaluate those before building your own.
Implementation guide
- Define key metrics in one governed place with owners.
- Have code calculate every figure.
- Use the model only to describe numbers it is given.
- Check that every number in the text matches the data.
- Publish with a visible note that commentary is AI-assisted.
Technical resources
Let code compute the figures; let the model narrate them; then check nothing was invented.
import re
import pandas as pd
df = pd.read_csv("weekly_sales.csv")
this, last = df["revenue"].iloc[-1], df["revenue"].iloc[-2]
facts = {"this_week": round(this), "last_week": round(last),
"change_pct": round((this - last) / last * 100, 1)}
text = llm(f"Write two sentences for managers using only these numbers: {facts}")
allowed = {str(v) for v in facts.values()}
used = set(re.findall(r"\d+(?:\.\d+)?", text.replace(",", "")))
assert used <= allowed, "Commentary contains numbers not in the data"Business benefits
Teams aim for faster reporting cycles, wider access to insight and less manual commentary writing. Trust in the numbers is the benefit that matters most, so protect it.
Challenges and risks
- Conflicting definitions: without a semantic layer, AI will answer inconsistently.
- Invented figures: verify any number the model writes.
- Over-reliance: executives may accept polished text without checking.
- Permissions: conversational tools must respect row- and column-level security.
- Cost: frequent automated summaries add usage fees.
Examples
Illustrative scenarios: a regional manager asks why returns rose and receives an answer tied to governed metrics; a finance team receives a Monday summary checked automatically against the data; a metric owner is alerted when conversion drops unexpectedly.