Management wants commentary
Leadership needs to know why the numbers moved, not just see a chart.
Use case 01
AI over BI and reporting
A report shows a change. The workflow helps explain why, pinpoint the deviation and prepare a comment or a recommendation for the next step.
But if reporting rests on inconsistent inputs, manual exports or differing metric logic, we start at the data foundation. We map the sources, verify data quality and propose how to prepare it for a reliable workflow.
Where the problem arises
What slows the process down today
Five typical points that together steal time, quality and trust in the outputs. If you recognise any of these, the workflow has somewhere to start.
Deviations are explained by hand
Comments are written in Excel and emails, hours each week outside the dashboard.
Data in separate systems
BI, ERP, CRM, the warehouse - the context has to be tracked down across tools.
Every team interprets it differently
The same number has a different label and a different reason in each department.
Reporting steals decision time
People who should be working on the business are putting together slides and tables.
Step by step in one flow
The workflow doesn't just read documents or only show numbers. It connects data, rules and decision points into a single flow with a clear output.
Loads data from BI or source systems
From reporting, an export or a database - without manual ETL.
Compares the trend against rules, expectations or history
Previous periods, the plan, thresholds, comparable segments.
Flags deviations and possible causes
Anomalies, suspicious combinations, correlations worth noticing - and hypotheses about where they might come from.
Adds context from documents, comments and rules
Pulls data from other systems, internal documents and comments and explains the background.
Prepares an explanation and a recommended next step
A summary, a recommendation or a task for the responsible role - not just text.
Keeps an audit trail of the output
What was in the source, which rule was applied and on what basis the comment was created - traceable.
What changes in operations
The concrete impact on work
The goal isn't to deploy AI. The goal is to give people back time to decide and free the process from depending on a handful of experts.
Faster report preparation
From hours of compiling to minutes of validation.
Consistent commentary
The same topic, the same rules, the same format across teams.
Less manual explaining
Managers get a summary straight away, not an email full of questions.
Early deviation detection
The workflow runs daily - it doesn't wait for the monthly reporting.
When it makes sense to start
Three typical situations
A workflow doesn't bring the same value everywhere. Here are three scenarios where it pays to start.
The report shows numbers, not the cause
Reporting shows the change, but doesn't explain why it happened or what to do next.
Teams have different versions of metrics
The same value has a different source, definition or calculation logic in each department.
Data is exported and checked by hand
Putting a report together means combining exports, spreadsheets and comments across systems.
Commentary is created manually
Explanations of deviations are written in Excel or emails outside any structured system.
Deviations are addressed too late
By the time someone notices the problem, it has already affected the business, the plan or trust in the numbers.
Leadership needs faster interpretation
Management wants a trustworthy answer sooner than in a week or with the monthly reporting.
What to watch out for
Limits we state up front
Three risks we recommend addressing already at the design stage - not later in operations, where they cost time and trust.
Data quality at the input
If the sources contain noise or aren't kept up to date, the workflow can't overcome the error.
A comment ≠ a decision
AI prepares the groundwork - responsibility for the business decision stays with a person.
Metric governance
If a KPI means something different in each team, the definitions need to be unified first.