Risk Management
What Happens If AI Automation Goes Wrong?
Automation failures are manageable when the system detects problems, limits impact, preserves evidence, and hands work to a person. The dangerous failures are silent, broad, irreversible, or impossible to reconstruct.
The Short Answer
Failures can come from bad input, model errors, changed APIs, expired credentials, rate limits, duplicate events, unavailable systems, incorrect rules, or unauthorised access. A production design assumes these events will occur.
What Shapes the Decision
Assess the consequence and detectability of each step. Decide what can retry, what must pause, what can be reversed, who is alerted, how long the business can wait, and which actions always require approval.
A Practical Way to Start
Create failure modes and runbooks, test them deliberately, add idempotency and checkpoints, launch at limited volume, and practise disabling or reverting the workflow. Keep manual procedures current.
Controls and Common Pitfalls
Use transaction limits, confidence thresholds, approval gates, circuit breakers, dead-letter queues, audit logs, backups, and scoped credentials. Communicate incidents transparently and correct affected records rather than only restarting the automation.
How to Measure the Outcome
Track incident frequency, affected cases, detection and recovery time, repeat causes, manual backlog, and customer impact. Use post-incident reviews to improve design without blaming the person who noticed the problem.