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VP of Revenue Operations

Board and investor reporting

Enhances✓ Available Now

What You Do Today

Prepare board-level revenue metrics — ARR, NDR, CAC payback, magic number, LTV/CAC. Own the data integrity behind investor-facing numbers and ensure consistency across internal and external reporting.

AI That Applies

AI automates metric calculation and trend analysis, flagging inconsistencies between data sources and generating board-ready visualizations.

Technologies

How It Works

The system aggregates data from multiple operational systems into a unified analytical layer. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output is a structured view that highlights exceptions, trends, and items requiring attention — available in the existing tools without switching systems.

What Changes

Board deck preparation shifts from manual spreadsheet gymnastics to automated metric generation with human-crafted narrative.

What Stays

Telling the revenue story to the board, contextualizing metrics with market conditions, and the judgment about how to frame challenges and opportunities.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for board and investor reporting, understand your current state.

Map your current process: Document how board and investor reporting works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Telling the revenue story to the board, contextualizing metrics with market conditions, and the judgment about how to frame challenges and opportunities. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Looker tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long board and investor reporting takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your board chair or lead independent director

Which of our current reports are manually assembled, and how much time does that take each cycle?

They shape expectations for how AI appears in governance

your CTO or CIO

What questions do stakeholders actually ask that our current reporting doesn't answer?

They own the technology infrastructure that enables AI adoption

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.