eCommerce AI Agent ROI Calculator
Model one candidate workflow: hours recaptured, net monthly benefit, and how long the build takes to pay for itself.
- Your numbers, not ours
- Instant results
- ~3 min
How much of this work happens each month?
Pick one specific repetitive workflow — WISMO enquiries, refund investigations, reorder decisions. Model one at a time; a blended average across several will flatter the result.
Tip: Use handling time, not first-response time. If the work is spread across a queue, take total agent-hours on that contact reason divided by volume.
Who This Tool Is For
eCommerce and operations leaders who have been asked to justify an AI agent build and need a defensible number rather than a vendor projection. Also useful as a sanity check when a proposal already has an ROI slide and you want to test its assumptions.
Why We Built This Tool
Every agent proposal comes with a business case, and most of them are built on an automation rate nobody has validated and a run cost nobody has modelled. This calculator makes both explicit inputs so you can see how sensitive the answer is to them. We built the scoring logic while writing our 64-part field guide on eCommerce agents, where the same pattern kept appearing: the projects that failed did not fail on ROI, they failed because the underlying data could not support the agent at all.
What Problem It Solves
- Automation rate is the whole model. Move it from 55% to 80% and the answer changes completely. Making it a slider shows you how much of the case rests on an assumption.
- Run cost gets ignored. Agents bill per conversation. At high volume the inference line is material and it is routinely left out of business cases entirely.
- Payback beats annual savings. A large annual number with an eighteen-month payback is a harder sell than a modest one that pays back in a quarter.
- Recaptured hours are not headcount. Presenting capacity as cost reduction is the fastest way to lose credibility with a CFO.
Pair this with the Agentic Commerce Readiness Checker, or read what to automate first.
Frequently Asked Questions
What automation rate should we assume?
Lower than you want to. The honest way to set it is to look at the workflow and ask what share is genuinely a lookup or a rule versus what requires judgement. For WISMO the straightforward majority is usually a lookup, and the remainder — split shipments, delivered-not-received, address changes mid-transit — is where the design work is and where a human belongs. Start conservative; a model that only works at an optimistic rate is telling you something.
Why is run cost per instance in cents rather than a monthly total?
Because agents bill by conversation and tool call, so cost scales with the same volume that drives the benefit. Expressing it per instance makes that relationship visible: at low volume the build cost dominates, and at high volume run cost can quietly consume a large share of the benefit. Model your own number with the AgentCore pricing calculator rather than accepting a default.
What could make this number wrong?
Three things, in order of how often we see them. An automation rate set from a vendor demo rather than your own case mix. A run cost that omits tool calls, which are frequently the larger share of a conversation. And a data layer that cannot actually support the agent, which does not show up in this model at all — it shows up in month three when the agent starts producing confident, wrong answers. Run the readiness checker before trusting this.
When is the answer to not build an agent?
When the decision is already deterministic. If a person follows the same rule every time with no judgement, that is a workflow, and workflows are cheaper, faster and easier to audit. Also when nobody will own the agent in production — unowned agents accumulate edge cases until someone quietly switches them off, and this calculator will happily show you a healthy payback for one of those.
Do you use these numbers as a benchmark?
No. Every input here is yours, and we publish no default drawn from client engagements — we have no eCommerce agent case studies yet and would rather say so than dress up an average. The calculator is a model, not a claim.