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Documentation Index

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Every deal is different. The same use case might resonate as “Accelerate Sales Cycles” for one buyer and “Increase Revenue per Rep” for another—even though they’re calculating the same value. Automations use AI to rewrite your business case content to match your specific buyer’s context, language, and priorities. Instead of manually editing use case names and descriptions for every deal, let AI tailor them automatically.

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Why Automations Matter

Your Value Framework provides standardized use cases that work across many deals. But the most effective business cases speak directly to each buyer’s unique situation. Without Automations:
  • Generic use case names like “Reduce Manual Data Entry”
  • Standard descriptions that don’t reference buyer-specific context
  • Time spent manually rewriting content for each deal
With Automations:
  • Context-aware names like “Eliminate Manual Reporting for Finance Team”
  • Descriptions that reference your buyer’s specific challenges
  • Tailored content in seconds, not hours
Automations analyze your deal context—discovery notes, call transcripts, CRM data—to generate suggestions that sound like you wrote them specifically for this buyer.

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Customize Use Cases

You can tailor any use case name or description to match your specific buyer’s context using the AI sparkle button.

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How It Works

  1. Click the sparkle icon – Next to any use case name or description, click the Minoa AI sparkle icon
  2. AI analyzes your deal context – Discovery notes, call transcripts, stakeholder information, industry, and company details are used to generate a tailored suggestion
  3. Review the suggestion – See the AI-generated alternative alongside the original text
  4. Accept or reject – Accept the suggestion to update your business case, or reject it to keep the original
The more deal context you provide (discovery notes, call transcripts), the better your suggestions will be. Quality inputs lead to quality outputs.

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Understanding the Suggestions

What Makes a Good Suggestion
AI-generated suggestions improve your business case when they:
  • Reference specific buyer context – “Reduce time spent on quarterly compliance reports” instead of “Reduce manual reporting time”
  • Match buyer language – Use terms your buyer used in discovery calls
  • Align with stakeholder priorities – Emphasize aspects that matter to the buying team
  • Stay accurate – Maintain the underlying value calculation while improving clarity
What to Watch For
Not every suggestion will be perfect. Review carefully for:
  • Accuracy – Does the suggestion correctly describe what this use case calculates?
  • Relevance – Does it align with what you learned in discovery?
  • Tone – Does it match how your buyer talks about their challenges?
  • Specificity – Is it appropriately specific without overpromising?
If a suggestion misses the mark, add more deal context to your business case (discovery notes, stakeholder details) and try again. Better context leads to better suggestions.

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Examples of Customization

Original Use Case Name:
“Improve Sales Team Efficiency” AI-Customized Name (for a healthcare deal):
“Reduce Time Spent on Patient Data Entry for Clinical Staff”
Original Description:
“Our solution automates repetitive tasks, allowing your team to focus on high-value activities.” AI-Customized Description:
“By automating patient data entry across your 15 clinics, your clinical staff can redirect 8 hours per week toward direct patient care, improving both efficiency and patient satisfaction.”
Notice how the customized version references specific buyer details (15 clinics, clinical staff, patient care) that make the value story more concrete and compelling.

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Best Practices

AI uses your discovery notes, call transcripts, and deal details to generate suggestions. The more context you provide, the better your suggestions will be.
AI suggestions are good starting points, but you know your buyer best. Don’t blindly accept everything—verify that suggestions align with your understanding of the deal.
Wait until you have substantive deal context from discovery calls. Customizing without context produces generic suggestions that won’t improve your business case.
AI handles the heavy lifting, but you can still manually refine use case content afterward. Use both for the best results.

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Suggest Input Values

Input Scout uses AI-powered research to automatically find publicly available data for your use case inputs. Instead of manually researching metrics like company revenue, employee counts, or industry salary benchmarks, let AI search the internet and populate your business case.
Input Scout only works with metrics that are publicly available online—company financials, headcount data, salary benchmarks, and profitability margins.

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How It Works

  1. Inputs are automatically categorized – When you save a calculation in the Value Framework, AI analyzes each input to determine if it represents publicly researchable data
  2. Input Scout runs automatically – When use cases are added to a business case, Input Scout searches for values for eligible inputs
  3. Results are scored by confidence – Each result receives a confidence score (1-3) based on source reliability
  4. High-confidence values auto-apply – Results with confidence ≥2 are automatically recorded in your business case, with a note added to the input explaining where the data came from

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What Inputs Can Be Researched?

Input Scout classifies inputs based on whether the data is publicly available online. The following categories are eligible:
CategoryWhat It IncludesExamples
HeadcountEmployee counts, team sizes, workforce data”# of employees”, “Sales team size”, “Engineering headcount”
RevenueAnnual/quarterly revenue, total sales, revenue by segment”Annual revenue”, “Q4 revenue”, “Revenue by region”
Salary & CompensationWages, FTE costs, fully-loaded costs, labor costs”Hourly wage”, “Cost per FTE”, “Software engineer salary”
MarginsProfitability metrics like gross, operating, or net margin”Gross margin”, “Operating margin”, “EBITDA margin”
Not everything with “revenue” or “employee” in the name is researchable.Inputs that require internal company knowledge—like “Potential revenue uplift” or ”% of employees using AI tools”—will not be enabled, because that data isn’t publicly available.

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Automatic Input Classification

Inputs are automatically classified when you save a calculation in the Value Framework. AI analyzes each input’s name, description, and unit to determine if it falls into a researchable category. ✅ Enabled (can be researched):
  • Factual company metrics disclosed in SEC filings, press releases, or LinkedIn
  • Industry salary benchmarks available on Glassdoor, Levels.fyi, or Bureau of Labor Statistics
  • Standard financial ratios reported in annual reports
❌ Not enabled (requires internal knowledge):
  • Estimates specific to the buyer’s situation (“potential cost savings”)
  • Behavioral or adoption metrics (”% of employees using X tool”)
  • Future projections or hypothetical scenarios

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Understanding Results

Inputs Added (High Confidence) Values with confidence ≥2 are automatically recorded, and a note is added to the input explaining the source. Sources include:
  • SEC filings and annual reports
  • LinkedIn company profiles
  • Levels.fyi, Glassdoor, LinkedIn Salaries
  • Company press releases and investor presentations
You can view the note by hovering on the input in your business case. Low Confidence Results Values with confidence of 1 are shown but not applied. These typically represent industry averages or estimates when company-specific data isn’t available.

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Resetting Values

Click “Reset” on any high-confidence result to restore the previous value. Low-confidence results were not applied, so there’s nothing to reset.

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Troubleshooting

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Customize Use Cases

This usually means AI doesn’t have enough deal context. Add more discovery notes, stakeholder information, or call transcript summaries to your business case, then try again.
You can manually edit the use case name or description to revert it or make adjustments. Click into the use case and update the text as needed.

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Suggest Input Values

Input Scout only supports publicly researchable metrics (headcount, revenue, salary, margins). Check that your inputs fall into these categories. Internal estimates like “potential revenue uplift” cannot be researched.
The company might be private or have limited public disclosure. The explanation will note when company-specific data wasn’t available. Consider this a starting point for your own research.
Check the source in the explanation. If the data is outdated or from an unreliable source, click Reset to restore the previous value and enter it manually.
With AI-powered features, you can deliver buyer-specific business cases without spending hours customizing content manually. Let AI handle the tailoring so you can focus on conversations that matter.