Jeff Dumé

Jeff Dumé: Reducing Hiring Risk Through Structured Evaluation & Responsible AI Use

Organizations consistently overestimate the cost of a vacancy and underestimate the cost of filling it wrong. A bad leadership hire does not announce itself immediately; it piles up quietly through missed targets, eroded team morale, and the slow drain of institutional momentum until someone finally names the problem that everyone has been working around for months. By that point, the damage is already significant, and the fix is expensive. 

Jeff Dumé, founder of Mainframe Solutions, has spent years working across executive search, operational systems, and workforce infrastructure in both public and private sectors. “Most hiring goes wrong before the first interview,” Dumé states. “Teams chase impressive resumes without agreeing on what success actually looks like in the role.”

Impressive Is Not the Same as Right

The instinct to start a search by profiling the ideal candidate feels logical and consistently produces the wrong result. Without a clear definition of what the role needs to deliver, in the first 90 days, across the first year, and into the future, a hiring team has no reliable basis for evaluating anyone. They default to impressiveness, which is not a measure of fit, and to likeability, which has never once been a measure of competence.

Specificity about outcomes changes everything. When a team agrees on exactly what success looks like before the first resume is reviewed, interviews transform from pleasant career conversations into actual assessments against defined criteria. Strong candidates become easier to identify. Wrong candidates get disqualified early, before the organization has invested three rounds of interviewing, two committee presentations, and considerable goodwill into someone who was never going to work. Skipping this step is the single most common reason leadership searches produce expensive regrets.

Gut Feel Has a Terrible Track Record

Consistency is what separates a hiring process that produces defensible decisions from one that produces sophisticated guesses dressed up as judgment. Consistent questions, clear scoring criteria, and the same structured evaluation framework applied to every candidate create a comparison. Without that structure, the candidate who interviewed on a good day, or who most resembles the person currently in the role, or who makes the hiring committee feel most comfortable, tends to win. Regardless of actual fit.

The candidate who interviewed brilliantly on a Tuesday after a good weekend is not necessarily better than the one who was slightly nervous on a Thursday. Structured evaluation removes that noise and creates documentation that holds up when a decision needs to be defended to a board, a regulator, or the organization itself when a hire does not deliver.

Governance Is Not Optional, Especially When AI Is Involved

As AI tools enter hiring and operational decisions, the governance requirement becomes more critical, not less. Knowing how a decision was reached, keeping human judgment at the center of the process, and establishing checkpoints that surface the reasoning behind each evaluation are what keep organizations accountable to the candidates they evaluate and the outcomes they are responsible for producing.

Good governance is not bureaucracy. It is the organizational discipline that transforms a risky guess into a confident, documented, and defensible decision. Build in the checkpoints before anyone starts lobbying for their preferred candidate, keep the human in the loop, and remember that the goal of every hiring process is not to find someone impressive, but to find someone whose strengths align precisely with what the role actually demands. 


Follow Jeff Dumé on LinkedIn for more insights on executive search, structured hiring, and building the decision-making frameworks that reduce hiring risk at every level of the organization.

Total
0
Shares
Prev
Akila Kesavasamy: How do you build a product team in the AI age?
Akila Kesavasamy

Akila Kesavasamy: How do you build a product team in the AI age?