How to Calculate LLM Project ROI: Proving AI Investment Value with Numbers

The moment you answer "How much better will it get if we adopt AI?" with "I think it'll get a lot better," budget approval slips further away. You need to present ROI in numbers.

The Basic Structure of ROI Calculation

CODE

ROI (%) = (Net Profit / Total Investment Cost) × 100
Net Profit = Cost Savings + Increased Revenue - Operating Costs

Estimating Costs by Line Item

Initial Investment Costs

Python

initial_costs = {
    "engineering_months": 3,
 "engineer_monthly_cost": 8_000_000, # when
 "engineering_total": 24_000_000, # 2,400

    "vector_db_setup":  500_000,
    "embedding_initial": 100_000,
    "qa_total":          250_000,

 "total": 24_850_000 # 2,500
}

Monthly Operating Costs

Python

def calculate_monthly_opex(daily_calls, avg_tokens, cost_per_1m_usd,
                            vector_db=200_000, monitoring=100_000):
    monthly_tokens = daily_calls * 30 * avg_tokens
    api_cost_krw = monthly_tokens * cost_per_1m_usd / 1_000_000 * 1350
    return api_cost_krw + vector_db + monitoring

# 1,000, 2,000 , claude-sonnet-4-6
monthly_opex = calculate_monthly_opex(1_000, 2_000, 3.00)
print(f" : {monthly_opex:,.0f}") # 38

Measuring Impact

Labor Cost Savings

Python

def labor_saving(tasks_per_day, min_before, min_after, hourly_wage, automation_rate=0.7):
    hours_saved = tasks_per_day * automation_rate * (min_before - min_after) / 60
 return hours_saved * 22 * hourly_wage # 22

# 500/, 5 → AI after 1, when 25,000
saving = labor_saving(500, 5, 1, 25_000, automation_rate=0.6)
print(f" : {saving:,.0f}") # 2,200

Error Reduction Impact

Python

# 50, 70% , 50
error_saving = 50 * 0.7 * 500_000
print(f" : {error_saving:,}") # 1,750

Calculating the Break-Even Point (BEP)

Python

def calculate_bep(initial_investment, monthly_benefit, monthly_opex):
    monthly_net = monthly_benefit - monthly_opex
    if monthly_net <= 0:
 return "ROI — "

    bep_months = initial_investment / monthly_net
    annual_roi = (monthly_net * 12 - initial_investment) / initial_investment * 100
    return {
        "monthly_net_krw":     monthly_net,
        "break_even_months":   round(bep_months, 1),
        "annual_roi_percent":  round(annual_roi, 1)
    }

result = calculate_bep(
    initial_investment=24_850_000,
    monthly_benefit=22_000_000 + 17_500_000,
    monthly_opex=380_000
)
print(result)
# break_even_months: ~0.6 (18 )
# annual_roi_percent: ~1,890%

One-Page Executive Summary

Rather than listing numbers, tell them as a story.

CODE

[LLM Customer Support Bot ROI Summary]

Current state: 10 agents handle 15,000 tickets/month, averaging 5 min/ticket
Goal: Reduce agent workload by 60% with AI first-line handling

Investment costs:
  - Initial build: KRW 25 million (3 months)
  - Monthly opex:  ~KRW 380,000

Expected impact (monthly):
  - Ticket handling time savings: +KRW 22 million
  - Error reduction impact:       +KRW 17.5 million
  - Net monthly profit:           +KRW 39.12 million

Break-even: ~18 days after launch
Annual ROI: ~1,890%

Non-financial benefits:
  - 24/7 response coverage
  - Agents can focus on higher-value work
  - Improved customer satisfaction

ROI calculations are forecasts, not guarantees. Presenting both a conservative scenario (50% of expected impact) and an optimistic one (100%) increases credibility.

Why ROI Numbers Drift and How to Verify Them

The ROI formula is simple, but months after reporting the numbers often don't hold. It is usually a baseline and hidden-cost problem.

Causes

The baseline (pre-adoption handling time, error rate) was estimated rather than measured
All saved time was converted into labor savings, while in reality people were only reassigned
Human review time, prompt/eval maintenance, and model price changes were left out of operating cost

Verification: measure baseline and after the same way

Starting two to four weeks before adoption, extract per-item handling time and rework rate from the ticketing system.
After adoption, pull the same metrics with the same query and compare.

SQL

-- e.g. monthly average handling time (minutes) and reopen rate
SELECT date_trunc('month', created_at) AS m,
       avg(extract(epoch FROM resolved_at - created_at)/60) AS avg_min,
       avg(CASE WHEN reopened THEN 1 ELSE 0 END) AS reopen_rate
FROM tickets
WHERE category = 'invoice_review'
GROUP BY 1 ORDER BY 1;

Aggregate monthly token usage from the provider's usage API or your gateway logs and compare with the estimate.

Fix

Report "more items handled" separately from "cost reduced."
Include reviewer hours and monthly evaluation work explicitly in operating cost.

Prevention

Recompute ROI each quarter with the same queries, and recalculate break-even whenever the model or pricing changes.

#LLM #ROI #AI strategy

✦ ✦ ✦

Sources and verification

Sources and checks vary by article. Only articles with a recorded check date, scope and sources below were checked, and only within that scope. For other articles, verify against the current docs for your version before applying anything, and email us about errors. Editorial policy · email us

This article has no recorded human check (date and scope) yet.

Published · May 20, 2026

How we write and review →Editorial policy →

Was this helpful?

Comments

Be the first to comment.