I lead [SALES TEAM TYPE] selling [OFFERING] to [TARGET CUSTOMERS]. Create a practical set of six copy-paste AI prompts for my day-to-day sales leadership work. Use only facts I supply in each prompt; do not invent customer needs, pipeline movement, forecast confidence, competitive claims, or rep performance issues. Operating context - Sales motion, cycle length, and average deal size: [SALES MOTION] - Current targets and metrics: [TARGETS AND METRICS] - CRM fields and data quality limits: [CRM CONTEXT] - Current business priorities: [BUSINESS PRIORITIES] Create one prompt each for: 1. Pipeline inspection: identify deals needing inspection based on stage age, next step, close-date movement, stakeholder coverage, and risk—not just deal size. 2. Forecast review: produce commit, best case, and pipeline views, explain assumptions, and identify evidence required to change a category. 3. Rep coaching: turn one call, deal review, or metric pattern into a fair coaching plan with observable actions. 4. Account strategy: map buying roles, business problem, mutual action plan, competition, and proof needed for a specific account. 5. Weekly leadership update: summarize outcomes, leading indicators, risks, decisions needed, and owner/date for each action. 6. Deal-loss review: classify controllable versus uncontrollable factors and propose one process experiment. Make every prompt 120 to 180 words, with 3 to 5 bracketed input fields and an explicit output format. Include a data-quality check in every prompt. Before finalizing, verify that forecast prompts do not treat a rep’s confidence as evidence and that coaching prompts do not make claims about intent. Ask up to three questions only if a required input is missing.
Fill in
| Placeholder | What to enter | Example |
|---|---|---|
| [SALES TEAM TYPE] | Enter the team scope, such as a regional enterprise sales team or SMB account executives. | Eight enterprise account executives covering the Northeast. |
| [OFFERING] | Enter the product or service the team sells. | Cloud spend-management software with annual subscriptions. |
| [TARGET CUSTOMERS] | Enter the customer segment, buyer type, and market. | Finance and IT leaders at U.S. companies with 1,000 to 10,000 employees. |
| [SALES MOTION] | Enter the sales process, average cycle length, contract size, and channel details. | Direct enterprise sale, 90- to 150-day cycle, typical first-year contract value of $85,000. |
| [TARGETS AND METRICS] | Enter the quota, coverage, conversion, retention, or activity measures used by the team. | Quarterly team quota of $1.8M, 3x qualified-pipeline coverage, 25% stage-two-to-close conversion. |
| [CRM CONTEXT] | Enter the available CRM fields and known limitations in data quality. | Salesforce includes stage, amount, close date, next step, activity date, contacts, and forecast category; contact roles are incomplete. |
| [BUSINESS PRIORITIES] | Enter current goals such as new logo growth, renewal protection, or a vertical launch. | Protect Q4 forecast quality and build pipeline in healthcare and manufacturing. |
How to use
- Paste the core prompt with your sales motion and the data your leaders actually use.
- Choose one generated prompt and add a clean CRM export, call notes, or account notes as its inputs.
- Check that findings distinguish missing data from a true deal risk before acting on them.
- Follow up with: “Adapt the pipeline-inspection prompt for [SEGMENT] and include our [STAGE] exit criteria.”
Variations
Forecast call
Use this before a weekly forecast meeting.
I am preparing a forecast call for [PERIOD]. Analyze the deal data in [PIPELINE DATA] against our definitions for commit, best case, and pipeline: [FORECAST RULES]. Return a table with deal, category, evidence supporting it, missing evidence, downside risk, next validation step, owner, and date. Then give a roll-up by category. Treat CRM fields as claims to test, not facts; flag stale close dates and missing next steps. Do not change forecast categories without stated evidence.
Call coaching
Use this after reviewing a rep’s discovery or deal call.
Coach a [REP ROLE] from this sales-call transcript or notes: [CALL NOTES]. The opportunity context is [DEAL CONTEXT], and our discovery standard is [DISCOVERY STANDARD]. Identify two effective behaviors and no more than two coaching priorities. Quote or reference the relevant moment, explain its effect on the buyer conversation, and give a practice exercise for the next call. Do not infer intent, score personality, or claim a buyer need not supported by the notes.
Account plan
Use this to prepare a strategic account review.
Build an account-plan brief for [ACCOUNT] using [ACCOUNT FACTS], [OPPORTUNITY DETAILS], and [STAKEHOLDER INFORMATION]. Output a one-page plan with business hypothesis, buying committee map, current evidence, unknowns, competitive position, mutual action-plan milestones, and internal help needed. Mark every unknown explicitly and separate confirmed customer facts from seller assumptions. Include the next two customer-facing actions with owners and dates; do not invent stakeholders or business pain.
Tips
- Define forecast categories with observable proof, such as a verified decision process and dated customer action, rather than a rep’s verbal confidence.
- Inspect stage age and next-step quality together; an old opportunity with a customer-owned next step is different from an old opportunity with no plan.
- Use call coaching to focus on one skill at a time, such as quantifying impact or confirming the buying process.
- A loss review is useful only when it changes a qualification rule, enablement asset, or sales-process experiment.
FAQ
Can AI forecast revenue from my CRM export?
It can organize evidence and expose weak assumptions, but it cannot make incomplete CRM data reliable. Keep human ownership of the forecast call and category decisions.
What data should I include for pipeline analysis?
Include stage, amount, close date and its history, next step, last activity, stakeholders, forecast category, and relevant notes. Explain which fields are often stale or incomplete.
Can I use AI to rank reps?
Use it to find coaching patterns, not as the sole basis for performance ranking. Context such as territory, account mix, tenure, and data quality matters.