AI
Playbook 5 of 5

How to Analyze Deals and Forecast with AI

Most sales forecasts miss because they rely on how reps feel about deals rather than what the data shows. This playbook teaches you how to use AI to score deal health, spot stalling opportunities before they slip, and build pipeline reviews grounded in evidence instead of optimism. You will learn to compare AI projections against your own judgment, investigate the gaps, and present recommendations your manager can act on immediately.

This playbook covers the how. For the why and what, see the skill definition .

Developing

Start here. Build the foundation.
  1. 1

    Run a weekly pipeline health check every Monday morning. Export your open opportunities from your CRM into a spreadsheet and run the prompt below. Review the output, verify it against your CRM, and address the top 3 at-risk deals before your weekly pipeline review.

    Try this prompt

    For each deal below, flag any that show warning signs:

    1. no activity in the last 14 days
    2. no next step scheduled
    3. single-threaded (only one contact engaged)
    4. missing key qualification data (budget, timeline, decision process, or competition)

    List each flagged deal with the specific warning sign.

  2. 2

    Build a deal scoring checklist with 8-10 criteria that predict whether a deal will close. Include:

    1. confirmed budget
    2. identified decision-maker
    3. established timeline
    4. defined decision process
    5. multi-threaded engagement (3+ contacts)
    6. recent activity (within 14 days)
    7. clear next step scheduled
    8. competitive position understood

    Score each open opportunity 0 or 1 on each criterion and calculate a percentage. Any deal below 50% needs an action plan before you include it in your forecast. Update scores weekly and track whether they correlate with actual outcomes over a quarter.

  3. 3

    Before every pipeline review with your manager, prepare a one-page summary for each deal using the prompt below. Print or share this summary before the meeting so review time is spent on coaching and strategy instead of status updates.

    Try this prompt

    For this opportunity [paste CRM data and recent meeting notes], provide:

    1. current deal status in one sentence
    2. the single biggest risk to closing
    3. what has changed since last review
    4. the specific next action I plan to take with a deadline

Proficient

Build consistency and rhythm.
  1. 4

    Compare your forecast against AI-generated projections every two weeks by running your pipeline through the prompt below. Compare the AI estimates with your own commit/upside/pipeline designations. For every deal where you disagree with the AI by more than 20 percentage points, write a one-sentence explanation of why you believe differently. Bring these disagreements to your manager. They are the most productive coaching conversations you can have.

    Try this prompt

    Based on these deal attributes [paste deal data including stage, age, activity recency, number of contacts, and deal size], estimate the probability of each deal closing this quarter. Explain your reasoning for any deal you rate below 50%.

  2. 5

    Create a deal velocity tracker to catch stalling patterns early. In a spreadsheet, track each deal's stage entry date and calculate days-in-stage for every opportunity, then run the monthly prompt below. Review the output, pick the most likely diagnosis for each deal, and execute the suggested action within the week.

    Try this prompt

    Here are my open deals with their current stage and days-in-stage. Our average sales cycle is [X] days. Flag any deal that has spent more than 150% of the average time in its current stage. For each flagged deal, suggest three possible reasons it may be stuck and one action to unstick it.

  3. 6

    Build a win/loss pattern analysis at the end of each quarter. Export your closed-won and closed-lost deals with full attribute data and run the prompt below. Present the findings to your team with specific examples from real deals. Use the patterns to update your deal scoring criteria for the next quarter.

    Try this prompt

    Compare these won and lost deals across these dimensions: deal size, sales cycle length, number of stakeholders engaged, number of meetings held, competitive situation, and industry. Identify the 3 strongest predictors of winning and the 3 strongest predictors of losing.

Mastered

Operate at the highest level.
  1. 7

    Build a forecast accuracy tracking system. Each week, record your committed forecast number and the AI-generated projection side by side. At the end of the month, compare both against actual bookings. Create a simple chart showing your forecast accuracy and the AI's forecast accuracy over 6 months. Identify where each source tends to be wrong. If you consistently overestimate certain deal types or stages, build that correction factor into your future forecasts. Share this analysis with your manager to demonstrate data-driven forecast discipline.

  2. 8

    Create strategic pipeline recommendations for your team's quarterly business review by running your full team pipeline through the prompt below. Present these recommendations with the supporting evidence in a structured slide or document that your leadership team can act on in the review.

    Try this prompt

    Given this pipeline [paste aggregated data], recommend

    1. which deals to accelerate with executive sponsorship or additional resources
    2. which deals to de-risk by widening stakeholder engagement or addressing specific objections
    3. which deals to deprioritize because the probability-weighted return does not justify continued investment

    Support each recommendation with specific data from the deal attributes.

  3. 9

    Develop a rolling 90-day pipeline health dashboard that your team reviews monthly. Track five metrics:

    1. pipeline coverage ratio (pipeline value divided by quota)
    2. average deal health score
    3. average days-in-stage by stage
    4. forecast accuracy trend
    5. new pipeline generated versus pipeline closed

    Run the monthly prompt below. Use this dashboard to shift pipeline reviews from deal-by-deal status updates to strategic discussions about where to invest selling effort.

    Try this prompt

    Based on these pipeline health trends over the last 3 months, identify the 2 most concerning trends and recommend specific actions to address them.

Common Pitfalls

Avoid the common failure modes.
  • Treating AI deal scores as gospel rather than a diagnostic tool. AI scores are based on the data you feed them. If your CRM data is incomplete or stale, the scores will be misleading. Always check whether the underlying data is current before acting on an AI recommendation. A deal scored as healthy with two-week-old meeting notes may actually be stalled.
  • Only analyzing your pipeline when your manager asks you to. The value of AI pipeline analysis comes from doing it consistently every week, not cramming before a review. Reps who review weekly catch problems when they are still fixable. Reps who review monthly discover problems after the quarter is already lost.
  • Confusing activity with progress in deal analysis. A deal with 12 meetings and 50 emails is not necessarily healthy. It may be circling without advancing. Train yourself to ask: 'What has changed since the last interaction?' If the answer is nothing, more activity is not the solution. Reassess your champion, your value proposition, or whether this deal is worth pursuing.

Continue Learning

All AI-Powered Selling playbooks →
Matching skill Analyze Deals and Forecast with AI

Related Playbooks

Unlock Skill Progression

Coaching Personalized to your current level
Progress Tracking Across every skill area
Mastery Validation Evidence-based, not guesswork
Speak to an Expert