Customer Support AI ROI Calculator
A support desk is the most measured team in the company. Ticket volume, handle time and CSAT already live in your helpdesk. That makes support the easiest place to put a defensible number on AI. Set the volume and hourly cost to your team below.
The calculator opens with 4,000 support tickets a month, 8 minutes of an agent's time per ticket at a fully loaded 35 euros an hour, against a Sonnet-class model using 1,200 input and 400 output tokens per reply, with a person reading one reply in five. Change any input and every figure updates, including the break-even chart.
Your numbers
With taxes and overhead the hour costs €16.25.
Advanced · minutes per task, build, hosting, tokens
Since you opened this page, the task has cost
The same day, two ways
At 4,000 tickets a month, that is 192 a working day. A person spends 8 minutes on each.
Each square is one ticket. The gold sliver is the model writing all 192 answers, about 6 seconds each. The hatched part is the person reading 20 of every 100 before they go out.
Twelve months, the build paid once
The build pays for itself in 17 days. After that every month keeps €7,928. Twelve months, €90,641.
Out of 100 answers, how many does a person read?
Click a square. This is the honest number in the whole calculation, and it costs more than the model does.
Honestly, it barely matters
Seven models at list price, per ticket with the same checking. Cheapest to dearest, the whole price list fits in €0.0138. The SaaS agent billed per resolution is 5.2× away.
With Claude Sonnet 5.5 the tokens are €0.0071 of the €0.17 per ticket. The rest is the person reading. Pick the model for quality, decide the checking for cost.
Your inputs and published prices. Nothing else.
8 min / 60 × €16.25 = €2.17(1,200 × €1.84 + 400 × €9.20) / 1M × 1.2 = €0.007120% × 3 min / 60 × €16.25 = €0.16(4,000 × (€2.17 − €0.17) − €60) × 12 − €4,500 = €90,641All formulas, model prices and task defaults
Every formula with your values
8 min / 60 × €16.25 = €2.17(1,200 × €1.84 + 400 × €9.20) / 1M × 1.2 = €0.007120% × 3 min / 60 × €16.25 = €0.16(4,000 × (€2.17 − €0.17) − €60) × 12 − €4,500 = €90,641€4,500 / €7,928 a month = 17 days(€4,500 / 12 + €60) / (€2.17 − €0.17) = 218 a monthPublished list prices, USD per million tokens
Prices as of 2026-10-21. Copied from the vendors' pricing pages on that date. Check the linked page before you sign anything.
| Model | Input | Output | Source |
|---|---|---|---|
| Claude Haiku 5.5Anthropic | $0.1 | $0.5 | Pricing page ↗ |
| Claude Sonnet 5.5Anthropic | $2 | $10 | Pricing page ↗ |
| Claude Opus 5.5Anthropic | $4 | $20 | Pricing page ↗ |
| Gemini 3.8 FlashGoogle | $0.75 | $3.75 | Pricing page ↗ |
| Gemini 3.1 ProGoogle | $2 | $12 | Pricing page ↗ |
| GPT-6 LunaOpenAI | $0.1 | $0.5 | Pricing page ↗ |
| GPT-6 SolOpenAI | $2 | $10 | Pricing page ↗ |
| Intercom Fin (per resolution)SaaS, per resolution | $0.99 / task | Pricing page ↗ | |
What each task assumes
| Task | Minutes | Tokens in / out | Checked | Build | Saving a year |
|---|---|---|---|---|---|
| Support ticket reply | 8 | 1,200 / 400 | 20% × 3 min | €4,500 | €90,641 |
| IT request (reset, access) | 12 | 900 / 300 | 15% × 2 min | €5,000 | €51,222 |
| Lead research and qualification | 15 | 2,500 / 600 | 30% × 4 min | €3,500 | €31,543 |
| Sales email or proposal draft | 40 | 3,000 / 1,200 | 100% × 8 min | €3,000 | €27,408 |
| Invoice parsing and entry | 6 | 2,000 / 500 | 25% × 2 min | €6,000 | €46,547 |
| Email classification and routing | 2 | 600 / 100 | 5% × 1 min | €2,500 | €47,247 |
| RFP / security questionnaire answer | 25 | 4,000 / 800 | 60% × 6 min | €5,000 | €8,148 |
| Payment reminder and collections touch | 6 | 1,500 / 300 | 30% × 2 min | €4,000 | €21,486 |
| Your own task | 10 | 1,500 / 500 | 30% × 3 min | €4,000 | €24,749 |
Working estimates from projects delivered in 2025 and 2026, not benchmarks. Every number is editable above. The saving uses your salary, currency and model.
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What AI actually automates in customer support
Support automation is not one thing. In practice a first project picks two or three of these workflows and ships them behind guardrails, with everything ambiguous handed to a human:
- 01Ticket classification and routing that decides intent, language, urgency and the right queue in seconds instead of a triage shift.
- 02Drafted replies grounded in your knowledge base, so an agent edits and sends instead of writing from scratch.
- 03Self-serve answers for the top repetitive questions about order status, returns, passwords and billing basics, resolved without an agent at all.
- 04Escalation summaries, so that when a human takes over, the AI hands them the history and a proposed next step instead of a raw thread.
- 05After-contact work such as tagging, CRM updates and follow-up notes that agents currently type between conversations.
Realistic ranges to expect
Across deployments we have seen and public vendor data, a grounded assistant typically resolves 30 to 60 percent of incoming conversations without a human, with the rest arriving at an agent pre-classified and pre-drafted. The calculator does not use a deflection rate. It prices every ticket twice, by the minute for a person and by the token for the model, plus the share of replies a person still reads. If your knowledge base is thin or your tickets are mostly edge cases, raise the review share to 40 or 50 percent and plan around that number.
Payback for a scoped support automation is usually measured in weeks, not years, because tokens cost cents next to a loaded agent hour. With the defaults the build pays for itself inside the first quarter even with the review share doubled.
What the research says
Three dated, sourced data points worth knowing before you trust any calculator, ours included:
In February 2024 Klarna reported that its AI assistant handled 2.3 million conversations in its first month, two-thirds of all customer service chats, doing the equivalent work of 700 full-time agents and cutting average resolution time from 11 minutes to under 2.
In January 2024 Intercom reported that customers of its Fin AI agent achieved an average conversation resolution rate of 41 percent, a useful reality check against louder marketing claims.
MIT's Project NANDA reported in July 2025 that while 95 percent of enterprise GenAI pilots showed no measurable P&L return, the organizations that succeeded cited 2 to 10 million dollars in annual savings from automating customer service and document processing.
MIT Project NANDA, State of AI in Business 2025, July 2025 ↗
How to read your result
Treat the number as the size of the prize. The savings assume the freed hours go to harder tickets, coverage or backlog. Klarna's own arc is instructive. After the celebrated 2024 launch it publicly re-hired human agents for quality in 2025. The durable wins keep humans on the judgment calls and let the machine do triage, drafts and lookups. That is exactly the scope we quote fixed prices for.
Customer support ROI questions
01Will AI replace my support agents?
The economics work without replacing anyone. The calculator values freed hours, and most teams redeploy them: faster responses, longer coverage, cleared backlog. Klarna's 2025 partial walk-back is a good caution against staffing cuts based on a pilot.
02What resolution rate should I assume for a chatbot?
Plan around 30 to 45 percent fully self-served for a well-grounded assistant on a real knowledge base, in line with Intercom's published 41 percent average. Vendor claims above 60 percent usually count partial deflections.
03What does a support automation project cost?
Our customer support automation engagements start around 2,000 dollars for ticket classification and drafted replies, and a RAG assistant on your docs starts around 4,000. The task default uses 12,000 euros as a representative full build. The exact figure is fixed in writing after a free scoping call.
04How fast can this go live?
A scoped first system typically ships in 1 to 2 weeks: classification plus drafted replies on one channel, measured against your current handle time from day one.