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How to build an AI scorecard

Hey! They say practice makes perfect, and well-structured feedback makes perfect practice. 

In this guide, you will learn more about how to build an AI scorecard that pushes reps to improve faster. If you follow MEDDPICC, GAP, Challenger or another methodology, your AI scorecard is where you’ll upload that framework into PitchMonster.

First, let’s take a look at how it is structured.


Anatomy of PitchMonster AI scorecard

Here is the view from your Admin account: 




Structure

What it does

Example scorecard

Metric

The major block or milestone in the conversation.

Intro & Rapport

Point

The point you care about inside that metric.

Introduced self & company

Context 

A “how to” so AI knows what “good” looks like.

Sales rep should start with first name, then 8‑word problem statement: ‘We help SaaS RevOps slash ramp time by 40%.’

And here is how AI will use them to score your reps:



Each metric is scored 1–5, so the total is balanced whether it contains two points or six.

Add up all metric scores and get an overall score out of the best possible results.

AI checks off any point it detects in the conversation. If misses a point, AI adds a coaching comment so the rep knows what to fix.

1.  Metrics:
Add the four to six things a rep must nail before you’d call the conversation “productive”: 

  • Intro
  • Discovery
  • Pitch / Value Prop
  • Objection Handling
  • Next Steps

2.  Points: break the metric into steps/criteria
Each metric usually has 2‑6 points. For discover process, for example: 

  • Uncovered current solution

  • Surfaced two pain points

  • Quantified business impact

3.  Context: tell the AI how you do it
Context is optional—but crucial when phrasing or depth matter. If the point can’t be scored from a single phrase, add context.

Your inputs will be used to provide AI-generated comments to your reps if they miss the mark.

When you DON’T need to add context:
“Ask customer name” - this point is self-explanatory

When you NEED to add context:
“Ask customer about current setup” – asking “Hey, how do you do it now?” might not be enough, according to your sales process, and you want the sales rep to go deeper.  Example:

Rep must:

  • Ask, “Walk me through your current CS onboarding workflow.”
  • Follow‑up: “On average, how many reps onboard per month?”
  • Calculate total hours & cost (rep count × 10 hrs × manager hourly rate).

The AI will only score it when the rep walks through that flow and captures the numbers — just like your best rep would.

Here is what you can include to the context:

  • Example of questions/phrases to say

  • Pieces of script

  • Granular points or flow

  • Reference to the learning material

Also, you can add your level of expectations at the beginning of the context box:

  • Verbatim to [add questions/phrases]

  • Similar to [add questions/phrases]

  • Rep should mention [add granular points]

  • If the rep failed to address this point, recommend [learning material]

4. Roles
Define who’s speaking so AI has the right perspective. Example:

User - SDR
AI - Prospect

or

User - Account Manager
AI - Enterprise Customer

 

Common mistakes

1. Too many metrics (10+)
Reps drown in too many things to remember at once. Cap at 6 metrics or create separate role-plays

2. Tracking non-verbal cues
AI can understand what was said, but it’s currently not working great with detailed non-verbal cues. Example: “Assume the sale - speak with a confident and low voice”. AI can’t analyze user voice to generate fair feedback.

3. Time-based criteria
AI doesn’t have a sense of time. Avoid adding points like “Tell value prop in under 15 seconds” and replace it with “Tell value prop at the start of the call in a few sentences.”

4. Ambiguous wording
AI is quite specific to your instructions, always use explicit verbs (“asks”, “calculates”, “books”).

5. Missing context when needed
When you sell, say, ERP to an Enterprise-level customer, adding a point  “Uncover a few pain points” is not enough. It's better to invest time and add context to such points. It directly impacts how effective the AI role-plays for your organization.

Iteration loop

  1. Build v1.
  2. Run 5 recordings with users. Note false positives/negatives.
  3. Tweak scorecard wording.
  4. After 2‑3 cycles, you’ll have a great scorecard you can reuse on other role-plays

TL;DR

A killer AI scorecard = your playbook → broken into metrics → concrete points → extra context when needed. Build it, iterate, and watch reps level‑up without another hour of manager talk‑time.

Got questions or want a live teardown? Reach out to your PitchMonster Customer Success Manager for help.