AI Business Intelligence Depends on Data Context, Not Better Models
At the Superweek 2026 analytics summit, consultant Siavash Kanani described a broken product bundle that was quietly costing a company $17,000 a week. Top-line revenue stayed high enough to mask it, so nothing on the dashboard turned red. The company had complete visibility into its own data and still could not see the problem.
That gap, between seeing data and understanding it, is what generative AI is now being asked to close. It is also where most AI analytics projects break.
Why Dashboards Stopped Being Enough
For two decades, the business intelligence budget went toward visibility. Data warehouses, ETL pipelines, cloud analytics platforms and dashboards built to surface every metric a team might want. It worked. Marketing watched campaign performance in real time, finance tracked cash flow through interactive reports, and sales leaders followed pipeline movement without waiting on an analyst.
What visibility never solved was interpretation. A dashboard answers...
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