AI Outcome Check
A good answer. A completed job?
Compare two ways of doing the same job. See completion, missing outcomes and the human work behind the AI bill. Leave with a brief and a concrete next check.
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Need to collect your figures first?
Use this free plan with your team to define outcomes, record every attempted case and account for human work. Fill it in using your own approved system, then return with aggregate figures.
- Define what counts as complete before testing. Use the same criteria for both groups.
- Check final outcomes against records or independent human review. An AI saying “done” is not an outcome receipt.
- Choose comparable cases and time windows. Record how cases are assigned; use a paired or randomised test where appropriate.
- Count every attempted case, including failures and abandoned work. Keep unknown outcomes visible.
- Track machine costs and human review/rework across all attempts. Allocate other direct costs within the same boundary.
- Review source records, critical errors, case selection and sample size before choosing a workflow. These checks do not approve a deployment.
Preview the complete plan
NexTool · Evidence collection plan · v1 Blank planning template; no observations supplied. Decision and scope — fill in with your team Decision to make: [fill in] Workflow A / version: [fill in] Workflow B / version: [fill in] Same task mix, information access and time window: [define] Written completion criteria and critical failures: [define before testing] Outcome record or independent human check: [define] Case selection, repeat attempts, sample size and follow-up date: [agree] Cost boundary, currency and human hourly cost: [agree] Next checks — assign an owner and date to each 1. Define what counts as complete before testing. Use the same criteria for both groups. 2. Check final outcomes against records or independent human review. An AI saying “done” is not an outcome receipt. 3. Choose comparable cases and time windows. Record how cases are assigned; use a paired or randomised test where appropriate. 4. Count every attempted case, including failures and abandoned work. Keep unknown outcomes visible. 5. Track machine costs and human review/rework across all attempts. Allocate other direct costs within the same boundary. 6. Review source records, critical errors, case selection and sample size before choosing a workflow. These checks do not approve a deployment. One record per attempted case — repeat this block in your own approved system Internal case reference: [no names or customer content] Group A or B / workflow version / task category / time window: [fill in] Outcome: [confirmed complete / known not complete / unknown] Outcome reference / reviewer / follow-up date: [keep within your organisation] Machine cost / total human review and rework minutes / other direct cost: [empty means unknown] Critical error or hard constraint: [record separately] Aggregate and return Include failed and abandoned attempts. Count each attempted case once in one outcome category. If completion was not checked, mark the outcome unknown, not failed. Retried work and its costs must not disappear from the accounting. Sum costs across every attempt in each group. If any cost record is missing, keep that aggregate cost unknown; a partial sum is not a full cost. Use one currency; the calculator does not convert exchange rates. Enter aggregate figures into AI Outcome Check. Keep raw records in your organisation. The calculator does not import case records or run an evaluation. Review the plan for your workflow before use; it is not a validated study protocol. https://getnextool.com/business/outcome-check?lang=en