AI can make an organization slower by producing more work than the organization can absorb.
Analysis, decks, research, customer responses, and draft decisions are now cheap. When production gets cheaper, supply rises until it reaches the next constraint. In most companies that constraint is review, integration, or management attention.
This is why “hours saved” is a weak return metric. A model may turn four hours of analysis into twenty minutes, then hand a manager three versions to reconcile. An agent may prepare several account plans while the commercial team still has the same number of leaders who can approve pricing and tradeoffs. The active task got faster. The queue got longer.
The new inventory is hard to see because drafts can look finished long before anyone has checked whether they are correct or worth using. Each artifact still needs an owner who can accept it, reject it, or send it back.
I would measure the time from request to accepted consequence. How long until the decision sticks, a process changes, or the customer sees a result? Track review burden and discarded output along the way. Then name the old process that stopped because the AI workflow replaced it.
A team adopting agents should set a limit on unreviewed work in progress. Generated artifacts need an owner and an expiry time. New work should pause when the review queue crosses the limit, just as a production system would apply backpressure instead of accepting an infinite load.
If the old process remains, machine output adds another review queue and can increase total elapsed time. The deployment is not complete until an old queue or handoff has actually disappeared.