ROI Calculator Methodology

Quantifying Agentic AI Business Value

Document ID: D1-ROI-METHODOLOGY | Version: 1.0 | Category: P4 - Business/Strategy

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Executive Summary

This document provides a methodology for calculating return on investment for agentic AI implementations. The framework uses productivity multipliers rather than simple automation percentages, capturing the full value of AI-augmented work.

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ROI Framework

Value Creation Model

Total Value = (Productivity Gain × Labor Cost) + (Quality Improvement × Error Cost) 
            + (Speed Improvement × Opportunity Cost) - Implementation Cost

Productivity Multiplier Approach

Work Type	Without AI	With AI	Multiplier
Research	8 hours	2 hours	4x
Analysis	6 hours	2 hours	3x
Documentation	4 hours	1 hour	4x
Coding	8 hours	3 hours	2.7x
Review	3 hours	1 hour	3x

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Cost Categories

Implementation Costs

Category	Components	Typical Range
Platform	Licenses, infrastructure	$50K-$500K
Integration	Development, testing	$100K-$300K
Training	User enablement	$20K-$100K
Operations	Support, maintenance	20% of platform/year

Ongoing Costs

Category	Calculation	Example
LLM API	$0.003-$0.06 per 1K tokens	$5K-$50K/month
Infrastructure	Compute + storage	$2K-$20K/month
Support	FTE allocation	$10K-$30K/month

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Benefit Categories

Direct Benefits

Benefit	Measurement	Calculation
Time savings	Hours saved × hourly rate	Direct
Error reduction	Errors avoided × error cost	Direct
Throughput increase	Additional output × value	Direct

Indirect Benefits

Benefit	Measurement	Typical Value
Employee satisfaction	Retention improvement	10-20% reduction in turnover cost
Quality consistency	Customer satisfaction	5-15% improvement
Knowledge leverage	Junior productivity	20-40% increase

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ROI Calculation Template

Input Parameters

# Labor Inputs
fully_loaded_hourly_rate = $75
affected_employees = 100
hours_per_week = 40

# Productivity Inputs
current_productivity = 1.0
projected_multiplier = 2.5

# Cost Inputs
platform_cost = $200,000
integration_cost = $150,000
monthly_operations = $25,000
implementation_months = 6

Calculation

def calculate_roi(params):
    # Annual labor cost baseline
    annual_labor = (params.hourly_rate * 
                   params.employees * 
                   params.hours_per_week * 52)
    
    # Productivity value
    productivity_gain = (params.projected_multiplier - 1) / params.projected_multiplier
    annual_productivity_value = annual_labor * productivity_gain
    
    # Total costs (Year 1)
    year1_cost = (params.platform_cost + 
                 params.integration_cost + 
                 params.monthly_operations * 12)
    
    # ROI
    year1_roi = (annual_productivity_value - year1_cost) / year1_cost
    
    # Payback period (months)
    monthly_benefit = annual_productivity_value / 12
    monthly_cost = params.monthly_operations
    net_monthly = monthly_benefit - monthly_cost
    payback_months = (params.platform_cost + params.integration_cost) / net_monthly
    
    return {
        "year1_roi": year1_roi,
        "payback_months": payback_months,
        "annual_benefit": annual_productivity_value,
        "year1_cost": year1_cost
    }

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Industry Benchmarks

Industry	Typical ROI	Payback	Key Drivers
Financial Services	200-400%	6-12 months	Compliance, research
Legal	150-300%	8-14 months	Research, review
Healthcare	100-250%	10-18 months	Admin, revenue cycle
Manufacturing	150-350%	8-12 months	Quality, maintenance
Professional Services	200-400%	6-10 months	Leverage, throughput

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Quick Reference

ROI by Use Case

Use Case	Typical Multiplier	Annual Value per User
Research automation	3-5x	$30K-$75K
Document processing	4-8x	$25K-$60K
Code assistance	2-3x	$40K-$80K
Customer service	2-4x	$20K-$50K
Compliance	3-6x	$35K-$70K

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Document maintained by CODITECT Strategy Team