Written by: David Carneal – Digital Efficiency Consulting Group – DECG

Read Time: 3 min

The safest automation projects are the ones that feel boring by the end. Boring means predictable. Predictable means your future self won’t be awakened by a 2:00 a.m. outage notification.

The golden rule: don’t build in production

Always test in a non-live environment first. Live data is not a training dummy. It will fight back.

What to test (in order)

  1. Happy path: normal, correct inputs.
  2. Common mistakes: missing fields, wrong formats, duplicates.
  3. Edge cases: weird but valid scenarios that happen monthly.
  4. Exception handling: confirm the system stops and explains why.
  5. Audit trail: verify you can trace changes and approvals.

How to keep humans in control

  • Approval points: let people approve batches or exception sets.
  • Override rules: allow authorized overrides with a reason logged.
  • Visible alerts: don’t hide problems. Surface them early.

A simple launch checklist

  • Document the rules (so tribal knowledge isn’t the operating system).
  • Train on exceptions (that’s where humans add value).
  • Define rollback steps (what happens if something goes sideways).
  • Set success metrics (cycle time, error rate, rework volume).

The ‘boring’ metrics to track after launch

  • Exception rate: % of items that fail validation
  • Cycle time: start to finish (median, not best case)
  • Rework volume: corrections, credits, re-shipments, re-runs
  • Touch count: how many people touch the workflow

If something goes wrong, what should happen

Design the failure path. A good automation system should stop safely, explain the issue, and let humans resolve exceptions without corrupting data.

  • Stop the batch: don’t partially create records unless you can roll back
  • Show the reason: point to the row/field/rule that failed
  • Log it: so the same issue is fixed at the rule level next time

When the system fails gracefully, your team trusts it. Trust is what makes adoption happen.

Adoption: the part nobody budgets for

Even great automation fails if people don’t trust it. Trust comes from transparency: clear messages, visible exceptions, and a predictable way to fix issues.

  • Explain: show exactly why something failed
  • Guide: suggest the fix (format, value, missing field)
  • Confirm: show what changed after the fix

A quick rollback plan

  1. Pause automated creation if exception rate spikes.
  2. Revert to manual creation for a limited set of transactions.
  3. Fix the rule/template, then re-run in test first.
  4. Resume automation with a smaller pilot group before full rollout.

Stability tricks that save your future self

  • Run automation jobs on a schedule (predictable) instead of ad hoc (chaos).
  • Use feature flags so you can turn parts on/off without deploying panic fixes.
  • Keep exception handling simple: one queue, clear categories, clear owners.

The goal is not ‘no exceptions ever.’ The goal is ‘exceptions are easy to see and resolve.’

Post-launch tune-ups (weekly for the first month)

  1. Review the top 5 exception causes and decide: rule change, template change, or training gap.
  2. Confirm audit logs are usable (not a junk drawer).
  3. Measure touch count: how many humans touched the workflow this week vs. last.
  4. Adjust notifications so the right people get the right alerts (not everyone, all the time).

CTA: Want a low-risk first win? Automate validation before you automate creation. Catching errors early is the cheapest improvement you can buy.