I’ve noticed this pattern across teams working on deep learning systems: models look solid during training and validation, metrics are strong, loss curves are clean—and confidence is high. But once the model hits real users, things start to feel off. Predictions become less stable, edge cases show up more often, and performance degrades in ways that aren’t immediately obvious. Nothing is “broken” enough to trigger alarms, yet the model no longer behaves like the one we evaluated offline.

seen this across teams again and again. We build dashboards, polish metrics, align KPIs… and yet, in meetings, decisions still come down to gut feel or last week’s Excel sheet.

On paper, BI is “live” and “data-driven.” In reality, half the dashboards are opened only during reviews, some metrics are tracked but never acted on, and everyone has a slightly different interpretation of the same number.

I’m curious how this plays out in your teams. Was there a moment where you knew BI was genuinely helping decisions?

I struggle with finding the balance between being too high-level and too detailed. If I keep things concise, leaders ask for more breakdowns. If I add breakdowns, they say it’s too much information.
How do you define the ‘minimum viable insight’ for executive reporting so the report stays useful without becoming a 20-page dump?

For nearly two decades, dashboards have been the backbone of business intelligence

static, structured, and pre-modeled. But today, the core experience of consuming insights is shifting. With LLMs layered on top of data warehouses,

business users no longer wait for a dashboard refresh or ask analysts to build a report. They simply ask a question in plain language:

“Why did churn increase in Q3?” or “Which customer segment saw the biggest drop in repeat purchases?”

The system not only returns aggregated results but contextual explanations, correlations, and even recommended actions.