How to Use This Tool
Apply deflection only to eligible tickets and compare full handling costs. Calculate AI customer-support savings from ticket volume, deflection rate and editable human versus AI handling cost.
The failure AI Support Savings is designed to catch
Deflection is not resolution: measure repeat contacts and escalations or the apparent savings may only move work to a later queue. The boundary is the job stated in Estimate Net Savings After AI Handling and Escalation Cost; AI Support Savings is not intended to score or transform a different workflow.
The AI Support Savings input contract
The fields used for this specific operation are Eligible support tickets, AI deflection rate %, Human cost per ticket, AI cost per handled ticket. Keep the source values beside the AI Support Savings result, because replacing the original would remove the evidence needed to reproduce or reverse the operation.
- For AI Support Savings, Eligible support tickets starts at
10000in the worked case; replace that example with the matching source value. - For AI Support Savings, AI deflection rate % starts at
30in the worked case; replace that example with the matching source value. - For AI Support Savings, Human cost per ticket starts at
6in the worked case; replace that example with the matching source value. - For AI Support Savings, AI cost per handled ticket starts at
0.5in the worked case; replace that example with the matching source value.
Worked result for AI Support Savings
The executable case called Default decision scenario expects out: $16,500.00. Verify that observation before entering real material, and then change one AI Support Savings field at a time so an unexpected direction or formatting change can be traced to a specific input.
Reading the AI Support Savings output
It combines eligible support tickets, ai deflection rate %, human cost per ticket and ai cost per handled ticket into one decision result using the formula explained on the page. Apply that answer only when Eligible support tickets, AI deflection rate %, Human cost per ticket, AI cost per handled ticket describe the same scope and format as the worked operation. If the source uses different units, quoting, nesting, timing or account rules, a plausible-looking AI Support Savings output is not sufficient validation.
Assumptions attached to AI Support Savings
- AI Support Savings assumes that all inputs describe the same unit or reporting period unless the field explicitly says otherwise.
- AI Support Savings assumes that the model includes only the four visible inputs and does not infer hidden platform charges.
- AI Support Savings assumes that the page never calls an AI model; token ratios, prices, limits and observed rates are user-supplied planning assumptions.
If one of these AI Support Savings assumptions is false, keep the result as a diagnostic rather than production or decision data, and choose an implementation that explicitly supports the missing rule.
Evidence maintained for AI Support Savings
The recorded reference is NIST — AI Risk Management Framework. Reopen that source when the definition, format, fee or policy behind AI Support Savings changes; private configuration and downstream acceptance still have to be checked in the user's own system.
Where AI Support Savings runs
The named operation executes in browser JavaScript without an ecech calculation API. For AI Support Savings, local execution reduces transmission but does not control browser extensions, device security or the destination where the result is pasted, so sensitive inputs still require the user's normal handling rules.
Sources & assumptions
Tool Spec v2 · verified 2026-08-19. Platform rules and fees can change; the editable inputs remain authoritative for your account.
Official references
- NIST — AI Risk Management Framework (checked 2026-08-19)
Model assumptions
- All inputs describe the same unit or reporting period unless the field explicitly says otherwise.
- The model includes only the four visible inputs and does not infer hidden platform charges.
- The page never calls an AI model; token ratios, prices, limits and observed rates are user-supplied planning assumptions.
