monte-carlo-simulator/examples/simulations/ai-tool-adoption/conservative.yaml

Business decision framework with Monte Carlo risk analysis - instant via npx

name: AI Tool Adoption Analysis (Conservative)
category: Technology
description: Conservative scenario with higher costs, lower productivity gains, and slower adoption
version: 1.0.0
tags: [ai, tools, adoption, conservative, scenario]

parameters:
  - key: teamSize
    label: Team Size
    type: number
    default: 20
    min: 5
    max: 200
    step: 1
    description: Number of developers who will use AI tools

  - key: monthlyToolCost
    label: Monthly Tool Cost per Developer ($)
    type: number
    default: 45  # Higher cost assumption
    min: 10
    max: 100
    step: 5
    description: Monthly subscription cost per developer

  - key: productivityGain
    label: Expected Productivity Gain (%)
    type: number
    default: 8   # Lower productivity gain
    min: 5
    max: 40
    step: 1
    description: Expected productivity improvement percentage

  - key: adoptionRate
    label: Team Adoption Rate (%)
    type: number
    default: 55  # Slower adoption
    min: 30
    max: 95
    step: 5
    description: Percentage of team expected to actively use tools

outputs:
  - key: annualCost
    label: Annual Tool Cost ($)
    description: Total annual cost for AI tools

  - key: annualSavings
    label: Annual Productivity Savings ($)
    description: Cost savings from productivity improvements

  - key: netBenefit
    label: Net Annual Benefit ($)
    description: Annual savings minus costs

  - key: roi
    label: ROI Percentage
    description: Return on investment percentage

simulation:
  logic: |
    // Calculate costs (same logic as base simulation)
    const annualCost = teamSize * monthlyToolCost * 12
    
    // Conservative modeling - more variance on the downside
    const actualAdoption = (adoptionRate / 100) * (0.6 + random() * 0.4)
    const actualProductivity = (productivityGain / 100) * (0.6 + random() * 0.5)
    
    // Calculate savings with conservative estimates
    const avgDeveloperCost = 120000
    const annualSavings = teamSize * avgDeveloperCost * actualAdoption * actualProductivity
    
    // Calculate metrics
    const netBenefit = annualSavings - annualCost
    const roi = annualCost > 0 ? (netBenefit / annualCost) * 100 : 0
    
    return {
      annualCost: Math.round(annualCost),
      annualSavings: Math.round(annualSavings),
      netBenefit: Math.round(netBenefit),
      roi: Math.round(roi * 10) / 10
    }