Reports should arrive, not be assembled
Manually built reports have three problems: they take time, they are only as current as the last export, and a single copy-paste error can send the wrong number to the leadership team.
Automated reporting pulls data directly from each source on a schedule, applies the same calculations every time, checks for gaps and anomalies, and delivers the result to the people who need it, as a live dashboard, a formatted spreadsheet or a short email summary.
Reports we commonly automate
Weekly operations summary
Orders, shipments, backlog and service levels.
Sales pipeline report
Pipeline by stage, rep and forecast category.
AR ageing and collections
Overdue balances with follow-up status.
Inventory and reorder report
Stock, turns and items below reorder point.
Month-end pack
Management accounts prepared from the ledger with commentary prompts.
Client reporting
Per-client reports generated and sent on schedule.
Signs your reporting should be automated
Manual reports are expensive in ways that are easy to miss.
- A day a week spent on one reportExporting, pasting and formatting the same numbers.
- Numbers that do not matchDifferent reports show different figures for the same thing.
- Reports that arrive too lateBy the time the report lands, the week it describes is over.
- Key-person riskOnly one person knows how the report is built.
How it is built
Define the metrics
Agree exactly how each number is calculated and from which system.
Connect the data
Scheduled extracts from ERP, CRM, accounting and spreadsheets.
Build the report
Dashboard, spreadsheet or email summary, with checks for missing data.
Schedule and hand over
Delivered automatically to each audience, documented for your team.
An example reporting finding
Reports often disagree because each one defines a metric differently.
- What we found
- Sales counts orders when booked; finance counts invoices when issued; credits are handled differently.
- Fix
- Agreed definitions for booked, invoiced and recognised revenue, each from one source, shown side by side.
- Effect
- One report everyone trusts, with differences explained rather than argued about.
What to automate first
Not every report deserves automating. Start with reports that are:
- Produced often. Weekly or daily reports pay back far faster than annual ones.
- Built from the same steps. If the process is identical each time, it can be automated.
- Used for decisions. Reports nobody acts on should be retired, not automated.
- Fragile. Reports that depend on one person or one complex spreadsheet.
What reporting automation typically costs
Typical fixed-price ranges. Your assessment gives an exact price before any build starts.
| Scope | Typical build price |
|---|---|
| One recurring report from two or three systems | $5,000–$12,000 |
| A management dashboard with agreed metrics | $10,000–$25,000 |
| A small data warehouse feeding several reports | $20,000–$40,000 |
BI tool licences, such as Power BI, are separate.
Common questions
Do we need a data warehouse?
Not usually for a handful of reports. We pull directly from source systems. A warehouse makes sense when many reports share the same data or historical analysis matters.
Can AI write the commentary?
It can draft a summary of what changed and why, based on the numbers, for a person to review. We do not send AI-written commentary to stakeholders without review.
Which tools do you build reports in?
Power BI, Looker Studio, Excel and Google Sheets, or scheduled email summaries. We use what your team already works in.
Find out what one process could save you
Book a paid automation assessment. We map the process with your team, measure the time it takes, and give you a fixed-price build plan with expected savings. If automation is not worth it, we will tell you.