Most owners have an honest read on one or two dimensions of their readiness for AI, usually the systems and the budget, and a partial read on the rest. The plan then gets built on the parts they know well, and the parts they do not know get absorbed as risk nobody priced.
When a quarter gets tight, the people investment is the first line cut, because nobody has put a dollar on it. That is a measurement failure rather than a spending decision, and for most small companies the number sitting inside it is larger than the software budget.
Two people describe the same AI rollout in the same week, and both are telling the truth. The owner is measuring the decision. The team is measuring the work. The distance between those two measurements is where most of the money goes, and it is nearly invisible from the top.
Two people describe the same AI rollout in the same week, and both are telling the truth. The owner is measuring the decision. The team is measuring the work. The distance between those two measurements is where most of the money goes, and it is nearly invisible from the top.
Most owners have an honest read on one or two dimensions of their readiness for AI, usually the systems and the budget, and a partial read on the rest. The plan then gets built on the parts they know well, and the parts they do not know get absorbed as risk nobody priced.
When a quarter gets tight, the people investment is the first line cut, because nobody has put a dollar on it. That is a measurement failure rather than a spending decision, and for most small companies the number sitting inside it is larger than the software budget.
A career built through one technological revolution is disrupted by another, revealing why experience still matters and why adapting should never mean starting from zero.
