Report Dashboard
A dashboard that sits there waiting for someone to look at it is not intelligence — it is a filing cabinet. This one connects every system, feeds live data automatically, and sends the warning before the problem becomes visible.
The Gap I Found
The dashboard existed.
It just waited for people to find the problems.
The business had a dashboard with the right metrics — AR balance, gross margin, utilisation, pipeline value, 90-day cash forecast. Leadership opened it when they needed to make a decision. And every time they did, the numbers were behind.
Sometimes by hours, sometimes by days. Because the dashboard updated when someone refreshed it — not when the underlying data changed. In a business where people are busy, that refresh step gets skipped constantly. The problem wasn't the layout or the metrics. The problem was that the dashboard was passive. It held the data. It didn't watch it.
A traditional dashboard sits and waits for a human to look at it, notice something is wrong, and decide to act. By the time that loop completes, the golden window to fix the issue has often already closed. The over-commitment has already been made. The cash has already left. The client has already pushed back on the invoice.
Why It Happens
No push events. No analysis layer.
No one watching the numbers.
Financial DB, Capacity View, CRM pipeline, and People DB each hold a piece of the picture — but each was built independently. No push event connected any of them to the dashboard, so data aged silently in the source until someone pulled it manually.
And even when the dashboard was current, the monitoring was entirely human. Someone had to open it, scan the numbers, recognise that something looked off, and escalate. That chain depends on attention and availability at exactly the right moment — which is precisely when it is least reliable.
What I Designed
Two layers: live data in,
AI analysis out.
The first layer connects all four source systems through defined push events. Financial DB fires on every write. Capacity View on refresh. CRM on stage change. People DB on payroll approval. The dashboard stops being a snapshot and becomes a live view — always current, no manual refresh required.
The second layer is where it separates from any traditional dashboard. An AI layer scans every incoming data point against defined thresholds. When a number drops below target — AR balance, gross margin, utilisation, pipeline value — the system doesn't wait for someone to notice. It routes an alert to Slack, email, or both, depending on the severity and the stakeholder. On a fixed schedule, it also sends a weekly digest summarising the state of the operation — whether or not anything is wrong.
The dashboard went from passive to active. It no longer needs a person to look at it to be useful. It tells you when you need to look.
Layer 1 — Live feed architecture
Layer 2 — AI watchdog + alert routing
Systems Involved
What Changed
The operation is visible.
And it speaks up when something is wrong.
Live
Dashboard, always
Every write event in any source system triggers an update. Leadership sees the current state of the operation — not the state it was in the last time someone opened a tab and hit refresh.
→
AI watching every number
The AI layer scans each incoming data point against defined thresholds. When a number drops below target — AR, margin, utilisation, pipeline — it does not wait for someone to notice. It sends the alert.
0
Manual reports required
The weekly digest goes out on schedule automatically. Stakeholders get the summary in their inbox whether or not they remember to look at the dashboard — and whether or not anything is wrong.
⚡
Golden time to act
The gap between a problem appearing and a stakeholder knowing about it used to be measured in days. Now it's measured in minutes. That gap is where the damage gets done — or gets prevented.
The human experience changed on two levels. First: the dashboard became trustworthy. Before, opening it meant a quiet mental check — when did someone last refresh this? That question no longer exists. Every metric reflects the current state of the operation.
Second, and more importantly: stakeholders stopped having to go looking for problems. The system now finds them first. A Slack message arrives before the over-commitment is locked in. An email lands before the cash shortfall becomes a crisis. The golden time to act — the window between when a problem appears and when it becomes irreversible — is now the window the team actually operates in.
How It Connects
The terminal point for every
workflow — and the earliest warning system.
Every other workflow in this system feeds the dashboard downstream. The quality of what it shows — and what the AI watchdog can detect — depends entirely on how accurately each upstream workflow writes to its source. A clean pipeline here means the alerts are trustworthy, not noisy. A broken upstream feed means the warning system has nothing to watch.