The ROI Question Every Executive Asks
You know AI can help your business. But can you prove it? When the CFO asks "what's the return?", vague promises about efficiency don't cut it. You need numbers.
Here's the uncomfortable truth: only 5% of enterprises see real, measurable returns from AI. Not because AI doesn't work — but because most implementations lack clear ROI frameworks from the start.
This guide gives you the framework.
The Simple ROI Formula
AI ROI = (Value Created – Total Cost) / Total Cost × 100
The challenge isn't the formula — it's accurately measuring both sides.
Measuring Value Created
AI automation creates value in four categories:
1. Cost Reduction
The most straightforward to measure. AI handles tasks previously done by humans, reducing labor costs.
| Metric | How to Measure | |--------|---------------| | Tasks automated per month | Count of interactions handled by AI vs. humans | | Cost per task (human) | Fully loaded hourly rate × average task duration | | Cost per task (AI) | API costs + infrastructure / number of tasks | | Monthly savings | (Human cost – AI cost) × tasks automated |
Example: A dental practice receptionist handles 80 calls/day at a fully loaded cost of $25/hour. An AI voice agent handles 60 of those calls at $0.30 each.
- Human cost for 60 calls: ~$95/day (60 calls × 3 min avg × $25/hr)
- AI cost for 60 calls: $18/day
- Daily savings: $77 → $2,310/month
2. Revenue Recovery
AI captures revenue that would otherwise be lost — missed calls, dropped leads, after-hours inquiries.
| Metric | How to Measure | |--------|---------------| | Missed calls/leads before AI | Track missed call rate or lead response time | | Capture rate with AI | Percentage of previously missed interactions now handled | | Average revenue per interaction | Historical data on conversion rate × average deal value | | Monthly revenue recovered | New captures × conversion rate × average value |
Example: A business misses 30% of inbound calls (40 calls/day × 30% = 12 missed). Each answered call has a 15% conversion rate at $300 average value.
- Calls recovered by AI: 12/day
- Revenue recovered: 12 × 15% × $300 = $540/day → $16,200/month
3. Speed and Throughput
AI processes faster than humans, enabling higher volume without proportional cost increases.
| Metric | How to Measure | |--------|---------------| | Average handle time (before) | Time per task with human processing | | Average handle time (after) | Time per task with AI | | Additional capacity created | Tasks that can now be handled without new hires | | Value of additional capacity | Avoided hiring cost or additional revenue enabled |
4. Quality and Consistency
AI doesn't have bad days. It follows the same process every time, reducing errors and improving customer experience.
| Metric | How to Measure | |--------|---------------| | Error rate (before/after) | Track mistakes, re-work, and complaints | | Customer satisfaction (before/after) | NPS, CSAT, or review scores | | Compliance adherence | Audit results, missed steps |
Measuring Total Cost
Initial Build Cost
One-time investment to design, develop, and deploy the AI system. Range: $25K–150K depending on complexity.
Monthly Operating Costs
| Cost Category | Typical Range | |--------------|---------------| | LLM API calls (GPT-4, Claude) | $200–2,000/month | | Infrastructure (hosting, DB, telephony) | $200–1,500/month | | Monitoring and logging | $50–300/month | | Total monthly | $450–3,800/month |
Maintenance Costs
Budget 10–15% of initial build cost annually for updates, prompt optimization, and model upgrades.
Benchmark: What Good ROI Looks Like
Based on industry data and our project experience:
| Metric | Benchmark | |--------|-----------| | Average ROI for production AI | 1.7x within 12 months | | Cost savings across operations | 26–31% | | 3-year ROI for coding AI tools | 376% | | Payback period (typical) | 4–12 months |
ROI Calculator: A Worked Example
Scenario: AI voice agent for a healthcare practice
| Item | Value | |------|-------| | Build cost | $50,000 | | Monthly operating cost | $800 | | Calls handled by AI/month | 1,500 | | Cost per call (human) | $3.50 | | Cost per call (AI) | $0.35 | | Monthly cost savings | $4,725 | | Missed calls recovered/month | 300 | | Revenue per recovered call | $45 (15% conv × $300) | | Monthly revenue recovered | $13,500 | | Total monthly value | $18,225 | | Monthly net (minus operating) | $17,425 | | Payback period | ~3 months |
Common Mistakes in AI ROI Calculations
1. Ignoring operating costs — LLM APIs aren't free. A high-volume application can cost thousands per month.
2. Overstating automation rate — Not every task can be fully automated. Plan for 60–80% automation in the first version, with human escalation for the rest.
3. Forgetting ramp-up time — AI systems improve over time. Month 1 performance isn't the same as month 6. Factor in an optimization period.
4. Comparing to zero — The comparison isn't "AI vs. nothing." It's "AI vs. current process." If your current process already works well, the delta is smaller.
5. Missing qualitative benefits — 24/7 availability, consistent quality, employee satisfaction (fewer repetitive tasks), and scalability are real but harder to quantify.
When AI Automation Isn't Worth It
Be honest about when AI doesn't make sense:
- Low volume: If you handle 10 calls a day, the ROI math doesn't work.
- High complexity, low repeatability: Tasks that are unique every time and require deep expertise are poor candidates for current AI.
- No clear success metric: If you can't define what "success" looks like, you can't measure ROI.
Getting Started
- Pick one process to automate — high volume, repetitive, measurable
- Measure the baseline — current cost, error rate, volume, missed opportunities
- Build and deploy the AI system
- Measure the same metrics after 30, 60, and 90 days
- Calculate ROI and decide whether to expand
At Autor, we help businesses identify, build, and measure AI automation that delivers real returns. Let's run the numbers together.