Leadership1 publisher2 min readPublished
Cloning your top salesperson into a model copies the prospects they avoid
An Entrepreneur contributor column argues that a firm using an AI hiring or sales tool inherits its bias whether or not it built the model. Its one concrete client example came from the author's own work.
The Board Room · Leadership desk
What happened
- An Entrepreneur contributor column argues that AI systems inherit the assumptions and blind spots of their training data, so bias governance belongs to leadership and not only to engineering.
- The column cites the controversy over biased outputs in Google's Gemini rollout and says many companies treated that episode as a technical mistake.
- Its central example is a company the author had worked with that decided to train a sales AI on the methods of its single best salesperson.
- It says businesses now use AI in customer service, hiring, marketing, pricing and operational workflows, often with no human reviewing the output before it reaches the public.
- The column's central claim for buyers is that any organization using AI-powered tools inherits bias risk whether or not it built the model.
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Why it matters
- decision If risk travels with use and not with authorship, the sign-off on a bought tool has to name the person who will explain a single output to whoever it affected.
- constraint Encoding one top performer's methods narrows the approaches a sales organization will ever attempt, and the segments that person avoided stay untested.
- exposure With nothing human between the model and the public, the first reader of a biased decision is the candidate or customer it lands on.
- precedent A deployment with no named internal owner leaves the first account of a bad output to be given by someone outside the company.
The specifics under that sales example are the part a buyer can act on. A top performer may be less effective with customers of a different gender, the column says, or may unconsciously avoid prospects from certain regions [6]. On a team, colleagues with different strengths absorb some of that. The author wrote that "your best salesperson is not free of bias" [7], and put the difference this way: "On a human team, those tendencies are diluted by colleagues with different strengths. Once encoded in a system, they aren't." [8] Over time, the column argues, the system reinforces a narrow definition of success and filters out approaches that may work equally well in other markets or environments [18].
The column also traces bias back before the data. Leaders often assume cleaner data, model adjustments or additional training will fix it, when the problem frequently begins with the objectives a business chooses to optimize [14].
Then there is speed. Organizations used to rely on layers of human judgment: HR, customer support and public relations brought context and empathy to difficult situations, and accountability for them [12]. AI removes many of those checkpoints, allowing decisions to happen faster and often with less oversight; the column credits that for efficiency and blames it for exposure when a system produces harmful or biased outcomes [13].
On accountability, the argument gets concrete. Autonomous systems operate continuously, adapt dynamically and execute actions in real time, and when one produces a harmful outcome, responsibility becomes difficult to trace across leadership, operations and engineering teams [17][9]. Without clear governance structures, the column says, a business can face reputational and legal consequences before it fully understands what went wrong [10].
The piece is a contributor opinion column [16]. It cites two specific instances in total: Google's Gemini rollout and the unnamed client company [15]. Its only figure is Google's market cap, given as trillions of dollars, offered to make the point that resources and engineering talent did not prevent the problem [3]. It does not give a rate for biased outputs or a cost for one.
One anonymized example is thin ground for changing a governance process. The decision in front of a buyer this quarter does not wait for a base rate. A company signing for a hiring tool can write down now who reviews a rejection before it goes out, or find out who owns it when a rejected candidate asks.
What to watch
- Whether a tool buyer, rather than the model vendor, is named first in an enforcement action or suit over a rejected candidate.
- Whether vendors begin disclosing the objective a model was optimized for as a contract term.
- Whether firms that trained on a single top performer start reporting the segments the model stopped attempting.