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

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

initial_costs = {
    "engineering_months": 3,
    "engineer_monthly_cost": 8_000_000,   # 시니어 기준
    "engineering_total":    24_000_000,   # 2,400만 원

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

    "total": 24_850_000  # 약 2,500만 원
}

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

# 하루 1,000건, 건당 2,000 토큰, claude-sonnet-4-6
monthly_opex = calculate_monthly_opex(1_000, 2_000, 3.00)
print(f"월 운영비: {monthly_opex:,.0f}원")  # 약 38만 원

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  # 월 22일 근무

# 고객 문의 500건/일, 기존 5분 → AI 지원 후 1분, 시급 25,000원
saving = labor_saving(500, 5, 1, 25_000, automation_rate=0.6)
print(f"월 인건비 절감: {saving:,.0f}원")  # 약 2,200만 원

# 문서 검토 오류 월 50건, 70% 감소, 건당 50만 원 손실
error_saving = 50 * 0.7 * 500_000
print(f"월 오류 감소 효과: {error_saving:,}원")  # 1,750만 원

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

    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 (18일만에 회수)
# annual_roi_percent: ~1,890%

[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