We have a clear set of company-level metrics that leadership reviews regularly.

Each core metric is linked to a real decision, operating rhythm, or business owner.

We know which metrics matter most by function, such as sales, marketing, finance, product, customer success or operations.

Key metrics such as revenue, pipeline, qualified lead, active customer, churn, CAC, conversion rate or gross margin have written definitions.

If the board asked "what was revenue last month?", sales, finance and operations would give the same number.

If an investor, auditor or regulator asked us to evidence where a reported number came from, we could show the calculation and the source data.

CRM, finance, marketing, product and support systems capture the fields needed for reliable reporting.

Important fields are consistently completed and not left blank, duplicated or overwritten.

We could reconstruct what happened to any deal, customer or account from our systems — without relying on whoever was there at the time.

Data from key systems is brought together somewhere central, such as a warehouse, reporting database or governed BI model.

There is a clear source of truth for each major subject area, such as customers, revenue, pipeline, product usage or support.

Everyone knows which dashboard or dataset leadership runs on — and when two reports disagree, there is an agreed way to settle it.

Important reports have checks for completeness, duplicates, invalid values or unexpected changes.

We know how fresh the data behind key dashboards is — and we would notice quickly if a pipeline silently broke.

Someone is alerted when key data loads fail, metrics move unexpectedly or source-system data quality drops.

There is a clear owner for leadership reporting and the core metrics pack.

If the person who knows our data best left next month, reporting and metrics would keep working — the knowledge is not in one head.

Dashboards are maintained, retired or improved rather than multiplying without governance.

AI use cases are tied to specific business decisions or workflows rather than vague experimentation.

The data needed for AI use cases has known owners, definitions, permissions and quality checks.

When AI tools answer questions about our business, they can show where the answer came from, how fresh it is, and what the caveats are.

Sensitive personal or commercial data is classified and access controlled.

Reporting and AI tools only expose data to people who should be allowed to see it.

There is a clear privacy and security review before customer, employee or sensitive commercial data is used in AI workflows.