Human Aspect
AI delivery needs taste
Taste is the human ability to know when output is technically correct but strategically wrong.
By JirakJ
5 min read
The work becomes easier when somebody writes down what good output actually means. AI output looks complete but misses nuance, positioning or the buyer's real concern. That is the real buying signal.
If the workflow depends on one expert's memory, start there before adding agents. For founders, product leaders and senior operators, the practical question is whether the workflow is ready to be made more reliable.
The boardroom version
The boardroom version is simple: the company is paying for repeated work because AI output looks complete but misses nuance, positioning or the buyer's real concern. That is a margin problem before it is a technology problem.
The operating version
The operating version is just as direct: review outputs against customer reality, product strategy and operational constraints. Make the work visible enough that a non-specialist can follow the handoff.
The standard
A taste review rubric is the minimum standard I would want before calling this mature. Otherwise the process still lives in somebody's head.
The upside
Human taste keeps AI systems commercially useful and context-aware. That upside is easier to defend than a generic claim about AI productivity.
Monday morning checklist
- • List the sources the workflow is allowed to trust and the sources it should ignore.
- • Write down the artifact that would make the work reviewable: in this case, a taste review rubric.
- • Decide who owns the next version if the first version works.
- • Mark the part of the workflow where human judgment must stay visible.
If this sounds familiar
Start with one workflow. FlowMason AI can map it, identify the right intervention, and define whether the next step should be a prototype, agent, documentation pipeline or delivery system.
Request audit fit review