Prioritize AI Use Cases

Executive and Founder, Revenue Operations, GTM Engineering, Sales Leader.

• Planning a quarter or half with more AI ideas than capacity.
• When the loudest stakeholder's idea keeps jumping the queue.

• Your list of candidate AI use cases
• Rough value and effort guesses for each
• Your team's real capacity this quarter

Best for one task. Copy it, add your information, and run it in your AI assistant.

You are a pragmatic operations advisor. Candidate AI use cases: [paste list]. My team's real capacity this quarter: [paste]. Rank them. For each: the measurable outcome if it works, the honest effort including data cleanup and behavior change, the data dependency, and the risk if it fails. Then give me the top two to start and the one that sounds exciting but should wait, with reasons. Favor use cases where the result is countable within one quarter.

Best for repeatable work. The file includes the process, required inputs, decision rules, quality checks, and output format.

---
name: prioritize-revenue-ai-use-cases
description: A ranked list of AI use cases for your revenue team, scored on value you can measure and effort you actually have.
license: MIT
metadata:
  author: The Revenue AI Report
  version: 1.0.0
  last-reviewed: 2026-09-04
  source: https://www.therevenueaireport.com/skills/prioritize-revenue-ai-use-cases
---

# Prioritize AI Use Cases

A ranked list of AI use cases for your revenue team, scored on value you can measure and effort you actually have.

## When to use this skill

- Planning a quarter or half with more AI ideas than capacity.
- When the loudest stakeholder's idea keeps jumping the queue.

## Inputs to collect

- Your list of candidate AI use cases
- Rough value and effort guesses for each
- Your team's real capacity this quarter

## Process

1. List every candidate. Include the boring ones.
2. Score value as a measurable outcome, not a story.
3. Score effort honestly: data cleanup and behavior change dominate most AI projects.
4. Pick two. Fund them properly. Park the rest in writing.
5. Revisit the parked list only when one of the two ships or dies.

## Decision rules

- If the result cannot be counted within one quarter, it goes behind one that can.
- A use case depending on data you do not have starts with a data project, whether anyone likes it or not.
- Two active use cases maximum per team. Three means none are funded.

## Output requirements

- Ranked list with value, effort, data dependency, and risk per use case.
- The two to start and the one to defer, with reasons.

## Quality checks

- Value is stated as a measurable outcome.
- Effort includes cleanup and adoption, not just setup.
- The deferred list is written down.

## Limitations

- Scores are structured judgment, not precision. The ranking matters more than the numbers.
- Political reality sometimes forces a different pick. If so, make the trade-off explicit.

## Example input

Candidates: call summarization, AI outbound sequencing, forecast roll-up automation, support-ticket tagging. Capacity: one RevOps person half-time for a quarter.

## Example output

Start: call summarization (countable time saved, data already exists) and forecast roll-up automation (removes weekly manual work, success visible in four weeks). Defer: AI outbound sequencing, because deliverability risk and data cleanup exceed one half-time person's capacity, and a failure there damages the domain, not just the quarter.

## Review checklist

- Two picks maximum?
- Deferred list written down?
- Each pick countable within a quarter?

## Works with

- Playbook: Put an answer layer on the warehouse you already paid for (L4) (revops, L4) https://www.therevenueaireport.com/playbooks/answer-layer-on-warehouse-l4
- Playbook: Make conversation data do work (L3) (sales, L3) https://www.therevenueaireport.com/playbooks/conversation-data-to-work-l3
- Playbook: Score your ICP on stack density (L4) (revops, L4) https://www.therevenueaireport.com/playbooks/score-icp-on-stack-density-l4
- Tool: Gong (Sales & Revenue Intelligence) https://www.therevenueaireport.com/tools/gong
- Tool: Gainsight CS (Sales & Revenue Intelligence) https://www.therevenueaireport.com/tools/gainsight-cs
- Tool: Ocean.io (Sales & Revenue Intelligence) https://www.therevenueaireport.com/tools/ocean-io
- Tool: accelerate ai (Data & Analytics) https://www.therevenueaireport.com/tools/accelerate-ai
- Tool: Clay (Data & Analytics) https://www.therevenueaireport.com/tools/clay

## Rules of conduct

- Write for a Director, VP, or operator. Short sentences. Explain uncommon terms.
- Separate facts from assumptions. Never hide uncertainty.
- Do not invent numbers, benchmarks, quotes, or customer names.
- Do not send messages, change CRM records, or publish anything unless the user explicitly asks.
- Flag when a decision needs human review.

## Evidence

This skill is grounded in The Revenue AI Report research:
- https://www.therevenueaireport.com/research/proof-gap
- https://www.therevenueaireport.com/research/rollback

- Related framework: https://www.therevenueaireport.com/frameworks/proof-gap



Source and updates: https://www.therevenueaireport.com/skills/prioritize-revenue-ai-use-cases

1. 1.List every candidate. Include the boring ones.
2. 2.Score value as a measurable outcome, not a story.
3. 3.Score effort honestly: data cleanup and behavior change dominate most AI projects.
4. 4.Pick two. Fund them properly. Park the rest in writing.
5. 5.Revisit the parked list only when one of the two ships or dies.

• If the result cannot be counted within one quarter, it goes behind one that can.
• A use case depending on data you do not have starts with a data project, whether anyone likes it or not.
• Two active use cases maximum per team. Three means none are funded.

• Ranked list with value, effort, data dependency, and risk per use case.
• The two to start and the one to defer, with reasons.

Candidates: call summarization, AI outbound sequencing, forecast roll-up automation, support-ticket tagging. Capacity: one RevOps person half-time for a quarter.

Start: call summarization (countable time saved, data already exists) and forecast roll-up automation (removes weekly manual work, success visible in four weeks). Defer: AI outbound sequencing, because deliverability risk and data cleanup exceed one half-time person's capacity, and a failure there damages the domain, not just the quarter.

• Two picks maximum?
• Deferred list written down?
• Each pick countable within a quarter?

What does the Prioritize AI Use Cases skill do?

A ranked list of AI use cases for your revenue team, scored on value you can measure and effort you actually have.

Who is the Prioritize AI Use Cases skill for?

Executive and Founder, Revenue Operations, GTM Engineering, Sales Leader. It sits at the intermediate level and takes about 45 minutes.

What do I need before I start?

Collect these first: Your list of candidate AI use cases; Rough value and effort guesses for each; Your team's real capacity this quarter.

What is the difference between the quick prompt and the SKILL.md file?

The quick prompt is for one task. Copy it, add your information, run it. The SKILL.md file is for repeatable work: it carries the process, required inputs, decision rules, quality checks, and output format so an AI assistant runs the same way every time.

What should I check before trusting the output?

Two picks maximum? Deferred list written down? Each pick countable within a quarter?

Is it free to use?

Yes. Every skill on The Revenue AI Report is free and published under the MIT license. Attribution is welcome, not required.

• Scores are structured judgment, not precision. The ranking matters more than the numbers.
• Political reality sometimes forces a different pick. If so, make the trade-off explicit.

Run the skill, then roll it out with a playbook. Vendor links are supporting context, not a recommendation.

• Playbook: Put an answer layer on the warehouse you already paid for (L4) (revops, L4 L4 Orchestrated)
• Playbook: Make conversation data do work (L3) (sales, L3 L3 Integrated)
• Playbook: Score your ICP on stack density (L4) (revops, L4 L4 Orchestrated)
• Tool: Gong (Sales & Revenue Intelligence)
• Tool: Gainsight CS (Sales & Revenue Intelligence)
• Tool: Ocean.io (Sales & Revenue Intelligence)
• Tool: accelerate ai (Data & Analytics)
• Tool: Clay (Data & Analytics)

• Research: proof gap
• Research: rollback
• Related framework