AI coding assistants are now standard equipment on many teams. The measured effect on delivery speed varies widely between studies and between tasks, and the honest position is that the benefit is real for some work and marginal for other work. That uncertainty is a reason to roll out deliberately rather than not at all.
Start with the questions that are not about productivity. Where does your code go when a suggestion is requested? Is it retained? Is it used for training? For a services firm working under client confidentiality agreements, these are contractual questions before they are technical ones, and several client contracts prohibit exactly this without notice.
Get the answer in writing from the vendor, and check which tier it applies to. Enterprise tiers commonly exclude customer code from training while consumer tiers do not, and engineers signing up individually will land on the wrong one.
Set expectations about review. Generated code arrives looking confident and reviews as though a colleague wrote it, which encourages lighter scrutiny at exactly the moment more is warranted. The rule worth stating explicitly is that the engineer who accepts a suggestion owns it as fully as if they had typed it.
Watch for the failure mode where juniors stop learning the underlying material. An assistant that produces a working answer removes the struggle that builds understanding. Teams that pair assistant use with genuine code review conversations avoid this; teams that measure only throughput do not notice it until someone has to debug something the assistant cannot fix.
Measure something. Cycle time, change failure rate, review turnaround — whatever you already track. Rolling out on the strength of vendor claims and never checking leaves you unable to answer whether the licence spend is justified, and that question arrives at renewal.
