See how much an AI chatbot could save your support team every month — net savings, ROI, and agent hours freed. Updates live as you type.
Estimate based on your inputs. Actual results depend on your use case.
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Support chatbots are sold with case studies about someone else's business. What you need is whether one pays for itself in your support operation — with your ticket volume, your handle times, and your agent costs.
This calculator answers that from five inputs, updating live as you type.
Monthly support volume — total tickets and chats handled per month.
Average handle time in minutes. Pull this from your help desk rather than estimating; the figure is usually higher than managers expect.
Agent fully-loaded cost per hour. Not the wage. Wage plus benefits, tools, and management overhead. Using the raw wage understates cost by a substantial margin and makes every automation look worse than it is.
Expected deflection rate, defaulting to 45% — the share of tickets the bot resolves without an agent.
Chatbot monthly cost — subscription plus amortised build cost.
Monthly net savings · Return on investment · Annual net savings · Agent hours freed per month
The last one is often the most useful internally. A saving expressed in currency invites a conversation about headcount. The same saving expressed as hours freed invites a conversation about what the team could do with that time — clearing backlogs, improving response quality, or handling the complex cases properly. In most support teams the second conversation is the one worth having.
The 45% default is a reasonable starting point, but the realistic figure depends entirely on your ticket mix.
If a large share of your volume is repetitive — order status, password resets, opening hours, shipping policy — deflection can run high. If most tickets are unusual, account-specific, or emotionally charged, it will be considerably lower, and a bot attempting them makes things worse rather than better.
Run the calculation twice: once at a rate you believe and once at half that. If the project still works at the pessimistic figure, it is a robust decision. If it only works at the optimistic one, you are betting on the best case.
Stated plainly, because it cuts both ways.
Counted: agent time saved on deflected tickets at your fully-loaded cost, and the bot's monthly subscription plus amortised build cost.
Not counted, and usually favourable: faster replies, round-the-clock coverage, and higher customer satisfaction. A customer who gets an answer at 11pm rather than waiting until morning is worth something the model does not capture.
Not counted, and usually unfavourable: one-off setup, integration work, and ongoing tuning. A bot is not a set-and-forget purchase; it needs its answers reviewed as your product and policies change.
An ecommerce business handles a large monthly volume, most of it order status and returns questions. The support manager is being asked to justify a chatbot before budget season.
She enters her real volume from the help desk, her actual average handle time, and a fully-loaded agent cost including tools and management. She sets deflection conservatively below the default, because roughly a third of her tickets involve account-specific problems.
Even at the conservative rate, the hours freed figure is the number that lands with her director — not because of the cost saving, but because it represents the backlog her team has never had time to clear.
Support managers building a business case. Operations leads comparing automation options. Founders deciding whether support automation is premature. Anyone asked to justify a tool before budget approval. Plan the bot itself with the AI Customer Support Bot Planner and draft its conversation with the Chatbot Script Generator. Our AI integrations service builds them, and our post on why 40% of AI projects fail is worth reading before you commit.
Someone else's case study will not get your budget approved. Enter your real ticket volume and agent cost above, test a conservative deflection rate, and see whether the case holds.
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