Recruiting occupies an unusual position in a growing business, according to Dr. Connor Robertson, sitting at the intersection of being one of the most time-intensive processes a
founder handles and one of the most consequential. He points to the cost of a wrong hire in a key position as commonly running 12 to 18 months of organizational energy, a figure that frames just how much is riding on a process many businesses still run informally. Robertson is careful to note that AI does not make the actual hiring decision. What it does, in his framing, is dramatically compress the time between opening a role and having a genuinely qualified finalist in front of a decision maker.
The first step in his approach is writing a stronger job description with AI assistance. Robertson’s method starts from a structured brief rather than a blank prompt, one covering the outcomes the role is actually accountable for, the profile of someone who has succeeded in similar work before, the cultural context that would help a person thrive or cause them to struggle, and the three characteristics that would make a candidate genuinely stand out. Feeding that brief to an AI tool such as Claude and asking for a job description built around that specific profile, he argues, produces a noticeably better result than a generic template.
Before a single application arrives, Robertson recommends building a weighted scoring rubric for the review stage, with criteria weighted according to their actual importance, experience in the specific domain, evidence of the outcomes the role requires, and signals of the cultural qualities that matter most. Deciding what good looks like in advance, and then applying that rubric consistently, is what he credits with eliminating the influence of first-impression bias and producing a ranked list based on criteria a business actually cares about, rather than on who happened to make the strongest first impression.
Screening interviews are the next stage, and Robertson’s approach here emphasizes consistency above almost everything else. He recommends preparing a structured set of screening questions derived from the role’s core requirements and asking every candidate the same questions in the same order. After each call, feeding interview notes to an AI tool alongside the scoring rubric, asking it to score the candidate’s responses against the defined criteria, adds a layer of consistency that Robertson considers the single most reliable predictor of hiring quality that most growing businesses currently lack.
Final interviews, in his framework, should be structured around behavioral frameworks rather than open conversation. Robertson points to research suggesting that behavioral interview questions, standardized scoring sheets, and calibrated panel evaluation predict actual job performance significantly better than unstructured conversational interviews do. He describes AI as capable of building a complete interview guide for each competency being evaluated, generating the behavioral questions themselves, and producing a scoring sheet that individual panelists can complete independently before comparing notes with each other.
Robertson is equally direct about where he believes AI’s usefulness stops. It cannot evaluate whether someone will genuinely thrive in a specific company culture, and it cannot assess the interpersonal subtleties that separate a technically qualified candidate from someone who actively elevates the people around them. It cannot replace the intuition that comes from years of reading people in professional settings. In his framing, AI functions as a filter and a consistency tool for the front of the hiring funnel, and the final call should remain firmly, deliberately human.
Taken together, Robertson’s system is less about removing people from the hiring process and more about removing inconsistency from the parts of it that do not require human judgment in the first place. Writing a strong job description, applying a rubric evenly, and asking every candidate the same structured questions are tasks where consistency matters more than intuition, and those are exactly the tasks he hands to AI. The steps that depend on reading a person accurately, weighing cultural fit, and making a final judgment call under real uncertainty stay with the humans doing the hiring, which is precisely where he believes they belong.
About The Author
Dr. Connor Robertson is an entrepreneur, author, and strategic advisor based in Pittsburgh. He is the founder of Elixir Consulting Group and host of The Prospecting Show. More about his work is available at drconnorrobertson.com.
