Board packs are a good test of a reporting process. They need accurate numbers, consistent formatting and a firm deadline, and they are often built by hand in the last few days of the month. This guide explains how Power Automate, Fabric pipelines and AI agents can take on the repetitive parts, and why solid data engineering for automated reporting is the foundation.
It is written for mid-market organisations with revenue between $30M and $500M.
Map the pack before you automate it
Start by listing every table, chart and commentary block in the current pack. For each, note the source system, who prepares it, how long it takes and how often it changes. This inventory usually reveals that a small number of items consume most of the effort.
Automating without this map tends to reproduce the existing mess faster.
Fabric pipelines for reliable data
Pipelines move data from source systems into a central store on a schedule, apply the cleaning rules and load a model that reports read from. Because the same steps run every time, the numbers do not depend on who prepared them.
Build in checks. Row counts, totals that must match the ledger and alerts for failed loads let the team find problems before the board does.
Power Automate for the handoffs
Power Automate handles the steps around the data: sending a reminder to a budget holder, collecting commentary, requesting sign-off and distributing the final pack. These tasks are simple but easy to miss when people are busy.
A clear approval flow also leaves a record of who confirmed what and when, which helps during audits.
Where AI agents help, and where they should not
An AI agent can draft first-pass commentary on movements, flag unusual items and answer routine questions about the figures. Analysts then review and edit the output. This saves time on the first draft without removing human judgement from the final message.
Keep agents away from anything that has not been reconciled. Their answers should draw on the governed model, and a named person should approve what goes to the board.
A sensible build sequence
Begin with the data refresh, then the report layout, then the workflow steps, and add AI assistance last. Each stage can be tested on its own, and the team gains confidence as manual work shrinks.
Run the automated pack alongside the manual one for a cycle or two so differences can be explained before the old process is retired.
What an engagement looks like
Projects typically include discovery of the current pack, pipeline and model build, report design and handover.
Power BI dashboard projects for mid-market organisations typically range from $5,000 to $30,000 AUD, and ongoing support starts from $2,000/month.
Timeline: 1 to 6 weeks, depending on how much data preparation is required.
Questions teams often ask
Can the whole pack be automated?
Most numeric content can. Narrative and judgement sections still need people.
What if a source system changes?
Pipelines include checks that flag changes early, and ownership of fixes should be agreed up front.
Is Power BI required?
It is a common choice for the report layer, though the pipeline work stands on its own.
How do we keep sign-off clear?
Use an approval step in the workflow so every pack has a recorded owner.
Next steps
If preparing the monthly pack is a recurring scramble, it may be time to automate the repetitive parts. You can get in touch with GrowthBI to talk through what a first phase might include.
