When AI-enabled work consistently disappoints — when outputs miss the mark, reviews take forever, teams keep correcting the same problems, or people quietly stop using the tools — the instinct is often to blame the technology. More often, the problem is in one of four specific places, and knowing which one changes everything about how you fix it.
Intent Management™ is built around four elements: Outcome Definition, Evaluation Criteria, Decision Authority, and Communication Cadence. When AI-enabled work breaks down, it's almost always traceable to one of these four things failing.
Outcome Definition
The first thing to check is whether the outcome was defined clearly enough in the first place. Signs this is the problem: outputs are technically correct but miss the actual need, different reviewers keep applying different standards, or the team is spending significant time trying to figure out what the output was supposed to accomplish before they can evaluate it.
Evaluation Criteria
Even when outcomes are well-defined, work can still go sideways if the criteria for evaluating outputs aren't explicit. Signs this is the problem: reviewers are spending a lot of time on outputs that technically meet the brief, feedback cycles are long and contentious, or the same type of output gets approved in some contexts and rejected in others with no clear explanation.
Decision Authority
This is the most frequently overlooked element. Signs this is the problem: outputs are stuck in review, people are escalating decisions that should be made locally, or there's inconsistency in what gets approved depending on who reviews it. The fix is explicitly mapping who approves what, at what quality threshold, and what the escalation path is.
Communication Cadence
The fourth element is about what happens over time. Intent drifts. Signs this is the problem: the team is following the process correctly but producing outputs that no longer seem relevant, or feedback keeps surfacing the same issues that were supposedly resolved in earlier cycles.
Most AI workflow problems are not technology problems. They're intent problems — places where what the organization is asking for and what it actually needs have come apart.
In practice, these elements compound. A weak Outcome Definition makes Evaluation Criteria impossible to get right. The diagnostic question to start with is: where in the workflow does the work first go wrong?
Naming the right problem is most of the work. Once you know which element is breaking down, the fix is usually more obvious than the symptom makes it appear.
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