Turn spreadsheets into decision notes

What this solves

Spreadsheets contain useful signals, but leaders still spend time explaining what changed, why it matters, and what decision is needed.

• Reports are shared as raw tables with little interpretation or recommended action.
• Only one person knows which rows, trends, or exceptions actually matter.
• Decisions are delayed while commentary is assembled from formulas, filters, and side notes.

What AI is good at here

• Summarizing visible trends, outliers, and changed assumptions from structured data.
• Drafting decision notes with facts, interpretation, risk, options, owner, and next action separated.
• Turning recurring spreadsheet packs into a consistent operating narrative.

What AI should not own

• Humans must verify formulas, source data, missing rows, and financial or operational interpretation.
• AI should not make unsupported claims from incomplete spreadsheets or silently treat bad headings as reliable data.

Best first implementation

• Choose one recurring spreadsheet with stable columns and a real decision attached, such as cash, sales pipeline, bookings, stock, campaign performance, rosters, or delivery status.
• Define the questions the note must answer: what changed, what breached threshold, why it matters, what decision is needed, and who owns the action.
• Have AI draft observations only after the numbers are locked, then require humans to approve interpretation and actions.

What good looks like

• Leaders get a clearer story from existing data without waiting for manual commentary.
• Recurring reporting packs become faster because the structure is reusable.
• Decisions are easier because facts, judgment, uncertainty, and recommended next steps are separated.

Common mistakes

• Using AI before cleaning headings, filters, formula issues, and stale rows.
• Letting AI recommend actions without separating facts, assumptions, and human judgment.