Artificial intelligence no longer helps only salespeople with preparation. Customers use it as well, comparing suppliers, looking for risks and preparing objections before first contact. Salespeople therefore need to know how their company appears in such answers, address likely objections openly and prepare concrete evidence for claims about differentiation.
Sales teams usually discuss artificial intelligence from the perspective of their own productivity: researching customers, reviewing opportunities, preparing emails or evaluating meetings. The same tool now exists on the other side of the buying process. Before any direct contact, a customer can ask whether a product is worth buying, what risks a supplier presents, which alternatives to compare and what questions to ask the salesperson. Part of the decision therefore happens before the seller even knows the opportunity exists.
A practical test is to ask several commonly used systems the same questions a customer is likely to ask. Searching only for the company name is not enough. More useful questions include: Who is the product suitable for? When would you not recommend it? What are its main disadvantages? Which competitors should be considered? The result is not an objective brand audit and may contain errors, but it reveals a possible set of objections a customer may bring into the conversation.
The salesperson then needs to distinguish factual error from a legitimate concern. Incorrect information can be addressed through high-quality public sources, updated profiles and a clear description of the offer. A genuine disadvantage should not be hidden. It is better to raise it early and explain for whom it is a problem and for whom it is not.
This also changes preparation for sales conversations. Salespeople should not ask only about needs and budget, but also about how the customer researched the market before the meeting. A direct question about whether AI was used can uncover recommendations and doubts that have already shaped expectations. Even when AI was not used, the salesperson can still prepare for the common objections that arise from this kind of comparison.
Public information about the salesperson and company plays an important role. An outdated professional profile, inconsistent company descriptions across sites or a lack of credible expert material reduces the quality of evidence available to customers and automated systems. The solution is not to create large volumes of generic content. Companies need concrete case studies, parameters, limitations, answers to frequent questions and clear explanations of the situations in which the product makes sense.
AI can also support competitive differentiation. A salesperson can ask a system to compare publicly available offers and then verify every claim manually. The output becomes a list of areas for further investigation, not a finished argument to use with a customer.
The important shift is that selling no longer starts with the first phone call. Part of reputation building, comparison and objection formation already happens in an information environment the salesperson does not control directly. Teams therefore need to monitor not only how AI accelerates their own work, but also how it changes the buyer’s work.
Key terms
- Pre-contact supplier vetting: The stage in which a customer compares offers, risks and alternatives from public information before contact.
- Pre-meeting objection: A concern formed during the customer’s own research before speaking with a salesperson.
- Public evidence: Verifiable information, a case study or a parameter that supports a salesperson’s claim.