Sales teams adopted these tools faster than anyone asked them to, because the time saving is immediate and obvious. The risk is equally immediate: a confident claim about your own product, in writing, to a prospect. This track keeps the speed and puts a check on what leaves the building.
Ask an assistant to answer a prospect's objection about an integration, a certification or a limit, and it will produce a smooth paragraph built on what products like yours usually do. Sent as written, that paragraph is a commitment made by someone who never saw your roadmap. This is the single most expensive AI failure in a sales team and it is invisible until delivery. The rule we drill is blunt: anything describing what your product does gets checked against your own documentation before it is sent, every time, including when you are sure.
A merge field with a company name is not personalisation and prospects stopped being fooled by it years ago. What works is a first line that could only have been written to that person, and models are good at producing it from material you supply: the call notes, the thing they said they were worried about, the post they published. What they are bad at is inventing it, and an invented detail about someone's business is worse than a generic email because it proves you were not listening. The exercise separates the two on real examples from your own pipeline.
Ranking prospects by likely fit is a sensible use and a quiet trap: the ranking looks objective, it arrives sorted, and the reasoning behind it is invisible. Teams start working the list from the top and stop asking why number forty is at number forty. The track makes the model explain each placement in one line, which is enough to catch the ones ranked on the wrong signal and to keep the rep making the call.
Diagnostic, acceptable use policy, interactive tracks by function, adoption follow-up. The four steps work together, and the free 30-minute call scopes all of them.
See the four stepsAI training for sales teams
It is the normal starting point, and existing use makes the training land faster rather than slower. People who have already felt a model get something confidently wrong need no convincing that the verification step matters; what they are missing is a method and a written rule about what can leave the company.
In most companies the answer for a consumer account is no, and the policy says so explicitly with the reason attached rather than as a bare prohibition. A contract carries the other party's terms, their pricing and often named individuals, none of which is yours to put into a tool they never agreed to.
Yes, as a policy question rather than a tooling one: who has to be told, what happens to the recording, and whether the summary gets checked before it becomes the account history. The diagnostic usually finds at least one of these running on a personal account already.
We go through how your teams use AI today, what is missing to frame it, and what the first quarter would look like. You leave with enough to decide, whether you work with us or not.