Automation / AI Reporting Dashboards
A number you can trace all the way to the record.
Connect reporting sources, agree on metric definitions, and let staff drill into the records behind a total. AI summaries should explain the same figures the team can inspect.
Explore a workflow
Illustrative exampleSales and finance compare the same closed reporting month.
Metric definition
| Check | Evidence |
|---|---|
| Measure | Invoiced revenue |
| Date field | Invoice issued date |
| Grain | One row per invoice |
Ready for the owner’s review
What happens
Use the agreed invoice grain, date field, and refund treatment.
Both teams can open the records behind the reported total.
One decision that matters
Booked, invoiced, and collected are different events.
A sale can be booked in March, invoiced in April, and refunded later. A dashboard needs an explicit date field, reporting window, and adjustment policy. We reconcile a sample period before presenting a trend.
Technical reading
Microsoft Learn · Model grain and relationships ↗Background for this design discussion. Implementation and outcomes depend on your systems.
Understand the moving parts
Three concepts behind this service.
What the term means and where your team will encounter it.
- Grain
What one row represents before it is aggregated.
An invoice total should not repeat for every line item.
- Reporting window
The dates and time zone used to include records.
Compare the same period and event date across teams.
- Reconciliation
Checking a measure against verified source records.
Trace a total to its IDs before explaining a trend.
The build plan
What goes into
the working version.
We connect the important data sources and build dashboards with plain-language summaries. The goal is to help you quickly see sales movement, lead activity, marketing results, support issues, operations volume, and exceptions.
- 01
Data source mapping
We identify where the numbers live and which metrics actually matter to the business.
- 02
Dashboard design
We build simple views for owners, managers, or staff to scan quickly.
- 03
AI summaries
We add plain-language summaries that explain movement, anomalies, risks, and next steps.
- 04
Refresh and alerts
We set refresh schedules, alerts, and checks for missing or broken data.
A result you can check
Agree on the evidence
before the demo.
These are recommended checks for this service. We choose a baseline and acceptance criteria with the person responsible for the work.
Reconciled totals
Compare measures against a verified source period.
Reporting latency
Track when the data is ready for the decision it supports.
Manual rework
Count corrections needed before the report can be used.
Included at handoff
Built, explained, and ready for your team.
- Live dashboard for the metrics that matter
- AI summaries explaining what changed
- Metric definitions and data source notes
- Alerts for important movement or broken data
In practice
Explain a number before acting on it
If booked revenue drops, the report should show the reporting period, source refresh time, and transactions behind the total. An AI summary can then describe the change with context.
Bring a report your team has to double-check by hand.
Review a recurring report →Questions before you start
How do you handle conflicting metrics?
Agree on a definition, date field, and source owner for each metric. Reconcile a sample period against the existing report.
Can we see when the data is stale?
Include last-refresh times and flag delayed sources. A missing feed should be distinguishable from a true zero.
Can we combine spreadsheet and CRM data?
Yes, if the records can be matched reliably. Check identifiers, duplicate rows, and date ranges before adding totals from different systems.
How often will a dashboard refresh?
Set refresh frequency from the decision it supports and the limits of each source. A morning planning report and a live dispatch view have different needs.
Can different teams see different figures?
Yes. Define access by role and test both summary views and drill-down records so restricted details stay restricted.
How do we validate an AI-written summary?
Compare its statements with the underlying measures. Flag missing periods and require evidence before presenting a possible cause as an explanation.
Keep exploring