Your Gut Instinct Isn't the Problem. Your Decision System Is.
Why growth companies need decision science - not more interview data - to make smarter, faster, and more defensible hiring decisions.
"I just have a better feeling about this candidate." Almost every executive has said some version of it. I have too. Gut instinct is not the problem. Unchecked gut instinct is.
Experienced leaders develop pattern recognition. They notice how candidates frame problems, respond to pressure, navigate ambiguity, and connect their experience to the work ahead. That
judgment matters. It should remain part of the hiring process. But judgment becomes risk when nobody can explain what created the feeling, test it against contradictory evidence, or compare it consistently across candidates.
That is where decision science belongs in hiring. Decision science is not about letting an algorithm select your next executive. It is the disciplined practice of combining human judgment, behavioral science, structured evidence, and analytical methods to improve how a decision is made.
In my conversations with growth companies, I am seeing the demand shift. Leaders are not only asking how to reach more candidates or move them through the process faster. They are asking
how to make the decision once strong candidates are in front of them. They want a clearer view of risk, evidence gaps, and tradeoffs - without surrendering the judgment or accountability of the people making the hire.
The distinction is important because companies are adopting more technology without necessarily building better decision systems. SHRM reported that AI adoption in HR tasks rose from 26% in 2024 to 43% in 2025. LinkedIn's 2025 recruiting research found that 89% of talent professionals believe measuring quality of hire will become more important, but only 25% feel highly confident in their organization's ability to do it well.
That gap:
- More candidate information, but not always more decision clarity
- More interviewers, but not necessarily a shared definition of success
- More technology, but not always a more defensible decision
- More scorecards, but too often completed after opinions have already formed
Confidence Is Not the Same as Calibration. The strongest argument for gut instinct is that experienced leaders have seen enough people and situations to recognize patterns others miss. Sometimes - that is absolutely true, sometimes it’s more ego driven.
I recently read an awesome article from Daniel Kahneman and Gary Klein which helps draw the line between genuine expertise and overconfidence. They said that intuition is most trustworthy when the environment contains patterns that are sufficiently predictable and the
decision-maker has had repeated opportunities to learn those patterns through actual
experience & feedback. They also said that subjective confidence is not, by itself, a reliable indicator of accuracy, not now, nor ever has it been.
Hiring is a tough task for calibration. The feedback is delayed because a new hire's performance may take months to understand. The role may change after the person joins. Leadership, market conditions, onboarding, and team dynamics all influence the outcome. The
executive who made the decision rarely learns what would have happened had the
rejected finalist been hired instead.
This does not make experience irrelevant. It means experience should be tested, not blindly taken as fact. Another study by Jason Dana, Robyn Dawes, and Nathaniel Peterson demonstrated how easily people can create a confident narrative from low-quality information. In an experimental screening involving predictions of student performance, participants formed coherent impressions even when interview answers were generated randomly. Access to the
unstructured interview sometimes made their predictions worse than relying on the relevant background information alone.
The lesson is not that interviews have no value. The lesson is that humans are remarkably good at turning almost any conversation into a convincing story. The solution is not panel interviews either, I hate panel inteviews :) - unchecked intuition does not disappear with a panel, but usually becomes group intuition.
Most companies try to improve hiring by focusing on one piece of the process: better sourcing, better interview questions, another assessment, or a new piece of software. Decision science is a more strategic topic. How does the organization move from a business need to a
decision it can explain, defend, and learn from? That requires an intentional decision chain:
1. Define success before defining the candidate
The process should begin with the business outcomes the person must produce, not a recycled job description or a list of desirable credentials.
- What must be different 12 months after this person joins?
- What business risk will this hire own?
- Which capabilities are necessary on day one, and which can be developed?
- What would strong performance look like in observable terms?
If leaders cannot answer those questions before interviews begin, no amount of candidate analysis will create clarity later.
2. Translate success into evidence
Broad labels such as"strategic," "entrepreneurial," or "culture fit" are not decision criteria until they are connected to behaviors and evidence.
A useful scorecard establishes:
- The outcomes and capabilities that matter most
- The relative importance of each criterion
- The behaviors that demonstrate the company's values in practice
- The minimum requirements, acceptable tradeoffs, and true deal-breakers
3. Design interviews to generate evidence - not impressions
An interview should not be a collection of interesting conversations. Each stage should have a clear purpose.
- Who is responsible for testing each critical outcome or capability?
- What evidence has already been established?
- Where are the gaps?
- Which follow-up question would confirm or challenge the current hypothesis?
This is where strong HR and talent teams create enormous value. They bring discipline to the process, protect the candidate experience, coach interviewers, and ensure the organization gathers job-relevant information instead of simply repeating the same conversation five
times.
4. Compare candidates before negotiating the narrative
This is the step many otherwise sophisticated companies miss.
They collect resumes, interview notes, transcripts, assessments, references, and scorecards. Then the hiring team enters a debrief and begins discussing whichever candidate someone
mentions first. The loudest opinion, most senior executive, or most recent interview can shape the conversation before the evidence has been compared.
A decision-quality comparison should make several things visible:
- Where each candidate has strong, direct evidence against the most important outcomes.
- Where the conclusion depends on inference rather than demonstrated evidence
- Which risks are material and which are manageable
- Where interviewers reached conflicting conclusions from the same information
- Which evidence gaps require one more targeted question
- What tradeoff the organization is actually accepting with each candidate
In my recent work, I have seen the comparison itself become the turning point. A perceived weakness was actually an evidence gap. A strong resume did not carry the same strength against the role's most important outcomes. A risk that felt minor became more significant
when viewed beside the alternatives. In some cases, the apparent front-runner changed after every candidate was evaluated against the same weighted criteria.
That is not technology replacing a hiring team. It is a decision-support layer making the team's work more visible and useful. For established companies with larger HR and talent functions, this may be where talent intelligence creates the greatest leverage. The organization already has recruiters, interviewers, notes, and process. The missing capability is often a consistent way to synthesize what the team has learned and present the tradeoffs clearly to the executive making the final call.
5. Close the loop after the hire
Decision science requires feedback.
After the person joins, the company should revisit the original scorecard and ask:
- Which predictions proved accurate?
- Which risks materialized, and which were overstated?
- Which interview evidence was genuinely predictive?
- Which criteria failed to differentiate candidates?
- What did the process miss?
More Interviewers Do Not Automatically Create a Better Decision
Adding stakeholders can improve perspective, but only when the group is evaluating the same definition of success. If five interviewers are each assessing something different, the
company has not created five sources of evidence. It has created five incompatible data sets.
This is why the debrief should not begin with, "Who did everyone like?"
It should begin with:
- What evidence did we collect against the outcomes that matter most?
- Where is the evidence strong, weak, conflicting,or absent?
- What risk would we knowingly accept with each candidate?
- What new information could still change the decision?
The purpose of a comparison is not to create certainty. Hiring will always involve uncertainty. The purpose is to make that uncertainty visible before the company commits.
Technology Should Improve the Decision - Not Own It
The current appetite for AI and talent intelligence is understandable. Hiring teams are managing more inputs, greater executive scrutiny, and increasing pressure for speed. Technology can
help organize evidence, identify contradictions, surface risk, expose missing information, and create a consistent comparison across candidates. But the differentiation should be at the forefront. Technology should not quietly decide who deserves access to employment. It should not use BS criteria that the hiring team cannot inspect. It should not turn a recommendation into an automatic outcome.
For a high-consequence decision such as hiring, human oversight has to mean more than clicking "approve."
The accountable leader should beable to:
- See the evidence behind the analysis
- Understand where the information is incomplete
- Challenge the weighting or interpretation
- Ask for additional evidence
- Override the recommendation
- Explain and own the final decision
If nobody can explain why the decision was made, the company has not implemented decision science. It has outsourced judgment.
Growth companies do not need to choose between gut instinct and data. They need a system that makes instinct testable and evidence usable.
Human judgment without structure creates inconsistency. Data without context creates false precision. Decision science connects the two. It gives executives a clearer view of risk, gives HR
and talent leaders a stronger way to demonstrate the value of their work, and gives the organization a hiring process that can improve over time.
The best hiring decision is no talways the candidate with the highest score. It is the decision where leaders understand the evidence, see the tradeoffs, know what remains uncertain, and
can explain why the chosen risk is the right one for the business.
Gut instinct can stay in the room. It just should not be allowed to run the room unchecked. If you are wrestling with an important hire, ask me how talent intelligence can bring greater clarity to the evidence you already have.