Your automation says success. Did it actually do anything?

Most automation failures announce themselves. A step errors out, an alert fires, someone gets an email. Those are the easy ones.

The expensive failure is quieter. The workflow runs on schedule, reports success, and produces nothing. A branch condition never matches. A filter excludes every record. An upstream form renames a field and the automation processes zero rows without complaint. Nothing breaks, so nothing tells you.

You need a way to confirm your automation is still producing output, not just still running.

Success means the steps finished, not that the work happened¶

"Ran without error" and "did the work" are two different facts. Most automation tools only report the first one.

A success status confirms the sequence completed. It does not confirm the sequence found anything to act on. An automation that processes zero records completes perfectly. It has nothing to fail on.

This is why silent failures survive so long. Error handling, retries, and kill switches all trigger on something going wrong. When nothing goes wrong and nothing gets done, none of them fire.

Treat output as the signal, not status.

Check one: when did it last produce something¶

Ask for a date. When did this workflow last create a record, send a message, or file a document?

If your automation should produce output daily and the most recent one is nine days old, you have your answer. No log analysis required.

Set a threshold that matches the workflow's normal rhythm. Daily workflow: flag anything older than two days. Weekly workflow: flag anything older than nine days.

This single check catches most silent failures.

Check two: did it produce about the right number¶

Freshness tells you something happened. Count tells you whether enough happened.

A lead-routing workflow that normally handles 40 leads a week and handled 3 is not working, even though it produced output. Partial stoppages are harder to spot than total ones.

You do not need precision here. You need a rough expected range and a note when the number falls outside it. Owners usually know the range already.

Check three: open one output every week¶

Pick one item the automation produced. Open it. Read it.

Freshness and count both pass if your automation produces the right number of wrong things. A routing rule can file every lead in the correct volume to the wrong destination. Only a person looking at one real output catches that.

One sample a week, two minutes. This is the check people skip, and the one that finds what the other two cannot see.

Where the check lives: one line on your weekly scorecard¶

Do not build a dashboard for this. Dashboards get checked for two weeks and then ignored.

Put it where you already look. If you run A Weekly AI Scorecard Any Owner Can Run in 15 Minutes, add one line per automation: last output date, count this week, sample checked yes or no.

Three data points, one line, once a week. The named owner of the workflow fills it in. If a line sits blank two weeks running, that is its own signal.

SMB example: eleven days of green runs and an unmonitored folder¶

An 11-person services firm automated lead routing from their website form. New leads were classified and filed to the right team folder.

Then the web team renamed a form field. The automation kept running. Every run reported success. Every lead now failed the classification condition and dropped into a default folder nobody watched.

Eleven days. Thirty-one leads. They found it when a prospect called to ask why nobody had responded.

A freshness check on the team folders would have caught it on day two.

Keep exploring¶

These three checks work best when every workflow has a named owner and a runbook that says what to do when a line goes blank. For a full picture of where your automations need human checkpoints, start the AI Readiness Audit or contact FIT.