The tool is rarely the constraint
When an AI project disappoints, the post-mortem usually blames the technology. The model hallucinated, the integration was clunky, the vendor overpromised. Look closer and the real failure happened weeks earlier: nobody checked whether the automated step was the step that mattered.
Automating a bad process gives you a faster bad process. Automating a guess gives you a confident guess. The diagnosis is the work most teams skip because building feels like progress and asking questions feels like delay.
What a real diagnosis looks like
Map one workflow end to end. Where does the time actually go? Where do errors enter? Which steps need judgement and which are genuinely mechanical? Where would better context, agent help or a human checkpoint remove real work rather than move it?
That is the thinking behind the AI Reality Check: one function, mapped against people, agents, context and governance, with the gaps named and a build order that survives contact with real work. Two weeks of evidence beats six months of enthusiasm.
Try this
Before you build anything, write one sentence: 'This workflow costs us X because of Y.' If you cannot fill in X and Y with evidence, diagnose first.