After two years of watching professional teams adopt AI, the failure stories have stopped being surprising. The same five patterns account for nearly everything that goes wrong between an AI tool and an embarrassed firm, and they are worth naming together, because seeing them side by side reveals something the individual war stories hide: they all pass through the same unguarded gate.
The most famous failure. A citation that does not exist, a source that exists but says something else, a statistic assembled from plausibility rather than data. What makes it dangerous is not frequency but camouflage: the fabrication is formatted exactly like the real support around it, and a reviewer checking that citations exist, rather than that they say what the claim needs them to say, will pass it through.
The analysis is flawless and the input is expired. A superseded contract draft, last year’s pricing, a market definition the client abandoned two quarters ago. The model reasons impeccably from what it was given, so nothing in the output looks wrong. This failure enters through the documents, which is why checking answers can never catch it. Only checking sources can.
One model, asked once, produces an answer that sounds settled and happens to sit at the edge of what other models would have said. Nobody knows it is an outlier because nobody asked twice. The failure is invisible by construction: a single confident voice offers no signal about where reasonable analysis would disagree with it, and disagreement is exactly the signal a reviewer needed.
The work was fine; the proof is gone. Output was copied from a chat window into a deck, the chat is deleted or unfindable, and when the client asks six months later how a number was derived, the firm has a deliverable with no lineage. Nothing was wrong until the moment someone asked, which is precisely when nothing can be reconstructed.
Everyone thought someone had checked it. The author assumed the senior would look; the senior assumed the author had verified; the deadline assumed everyone. Without a named, required sign-off, review is a social expectation, and social expectations are the first thing pressure deletes. This is the quietest failure and the parent of the other four, because every one of them survives only where no accountable person had to stop and look.
Look at the list again and the pattern is plain. Fabricated support survives when claims are not tied to checkable evidence. Stale premises survive when sources are not audited. Confident outliers survive when one model goes unchallenged. Trails vanish when records depend on people remembering to keep them. Reviews get assumed when they are not structural. Five failures, one common cause: nothing in the workflow forced the moment of verification.
That is the design argument for Qonera in a paragraph. Per-claim citations make fabrication checkable. Source integrity checks catch the stale premise. The Multi Model Stress Test surfaces the outlier. The tamper evident audit trail keeps the lineage. And named sign-off through the review and approval workflow makes the review real instead of assumed. Not five tools for five failures, but one structural habit, verification before delivery, applied at the five places it pays.
Teams do not need to have lived all five failures to take the lesson. The firms that will use AI confidently through the next decade are the ones that noticed the pattern early: the failures are various, the gate is one, and the workflow that builds the gate in costs less than any single story on this list.
Multi-model stress testing, Conflict Heatmap, tamper-evident audit trail, and structured sign-off, built for teams who need defensible AI output.