Most conversations about AI governance are about prevention: checking sources, comparing models, flagging unsupported claims, signing off before delivery. That work matters, and a good review process catches most problems before they leave the team. But no process catches everything, and the firms that take AI seriously plan for the day something gets through, not just the day everything works.
What happens when AI-assisted work goes wrong is a different question from how to stop it going wrong, and it is the question most teams have not answered. A wrong figure reaches a client. A fabricated citation survives review. A recommendation built on a superseded document gets acted on. The mistake is made, the work is out, and now the question is not how to prevent it but what the firm actually does next. The maturity of an AI process shows up in that moment more than in any other.
When AI-assisted work goes wrong in most firms today, the response is informal and invisible. Someone notices, sends a corrected version, has a quiet word, and moves on. Nothing is recorded. The same weakness in the process that let the error through stays exactly where it was, because nobody captured what happened, why it happened, or what would stop it happening again. The mistake is treated as an embarrassment to be smoothed over rather than information to be used.
A logged incident is the opposite. It records what went wrong, when it was caught, who was involved, what the impact was, and what was done about it. That record is not about assigning blame. It is about giving the firm a way to see patterns, fix the underlying process, and demonstrate, if anyone ever asks, that the firm takes failures seriously enough to track them. The difference between a quiet mistake and a logged incident is the difference between a firm that learns and a firm that repeats.
Not every incident is a mistake that reached a client. Some of the most valuable things to record are the near misses: the fabricated citation that review caught, the contradiction between two documents that surfaced before the analysis ran, the confident answer that turned out to rest on a stale source. These never reached anyone, so the instinct is to feel relief and move on.
But a near miss is a free lesson. It shows exactly where the process is under strain and where the next failure is likely to come from, without any of the cost of an actual client-facing error. A firm that records near misses, not just failures, builds a map of its own weak points over time. That map is worth more than any single correction, because it lets the firm strengthen the process before the near miss becomes a hit.
The reason most firms handle AI mistakes informally is that they have never built incident handling as a deliberate capability. There is no place to record what happened, no path to escalate it, no way to triage whether a given problem is minor or serious, and no record afterward that it was resolved. So every incident is handled from scratch, by whoever happens to notice, in whatever way seems reasonable at the time.
A real capability looks different. There is a clear way to report an incident, manually when a person spots something and automatically when the system itself detects a risk signal. There is a way to triage what comes in, so a minor formatting slip and a serious factual error in a client deliverable are not treated the same. There is a way to resolve and close each one with a record that survives. And there is visibility for the people responsible, so incidents are not buried in one person's inbox. That is the difference between reacting to problems and managing them.
That incident layer is part of what Qonera is built for, alongside the structured review and approval workflow that aims to catch problems before they reach a client in the first place. Incidents can be reported manually when a reviewer spots a problem, and created automatically when the platform's risk screening detects a signal in AI-assisted output. Admins receive notifications for new incidents and can triage them, move them through review, and close each one with a recorded outcome, whether that is remediation or a documented decision that no action was needed.
Because every incident links back to the chat and the message that produced it, the firm is not starting from a blank page when it needs to understand what happened. From that link it can trace through the tamper evident audit trail to the work behind the flagged output, who reviewed it, and what was decided. When a client, a partner, or a regulator asks how a problem was handled, the answer is a record rather than a recollection.
The same principle sits behind Article 73 of the EU AI Act, which sets expectations around reporting serious incidents involving high-risk AI systems. The regulation reflects a simple idea that good firms already act on: when something goes wrong with an AI system, there should be a way to record it, escalate it, and act on it, rather than letting it disappear into informal handling. Most of the obligations under the EU AI Act apply from August 2026, and teams that already treat incidents as something to track rather than smooth over end up close to what the incident-reporting expectation pushes toward.
No team wants to think about what happens when its AI-assisted work goes wrong, but the teams that think about it anyway are the ones that handle it well when it does. Prevention catches most problems, and it should. Incident handling is what catches the rest, turns each failure and near miss into something the firm can learn from, and gives the firm a record to point to when the question comes. A process that only plans for success is not a process. It is a hope, and hope is not something a firm can show a client when the work is challenged.
This article is for general information only and does not provide legal advice. Organisations should consult qualified legal counsel about how Article 73 and the EU AI Act apply to their specific systems, workflows, and obligations.
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