Aisha Rahman had been head of talent at a six-hundred-person enterprise AI company for two years when she audited her team's interview feedback. She pulled two hundred interview evaluations from the last quarter and read each one. The pattern was clear and damning. Sixty-five percent of the feedback was a sentence or two—strong candidate, would recommend, not a fit—that contained no specific evidence to support the conclusion. Twenty percent was a paragraph that described the interviewer's impression without the example to justify it. Only fifteen percent contained the specific evidence that the team is what is needing to make the decision and that the needing is what the team was trying to do. The feedback was not useless because the interviewers did not care—it was useless because the system did not require useful feedback, and the system is what Aisha is what was what she was what was going to fix. She spent the next quarter rebuilding the feedback system, and the proportion of feedback that contained specific evidence rose from fifteen percent to seventy-eight percent. Here is the system she used.
Why Most Interview Feedback Is Useless—and What Useful Feedback Looks Like
Most interview feedback is useless because the feedback is what the interviewer is what is providing in the form that the system is what is allowing and that the allowing is what the team is what is using to make the decision and that the decision is what the team was trying to produce. The useless feedback is the feedback that contains the impression without the evidence, and the impression is what the interviewer is what is providing and that the providing is what the team is what is using to make the decision and that the decision is what the team was trying to produce. The useful feedback is the feedback that contains the evidence that the interviewer is what is capturing during the interview and that the capturing is what the team is what is using to make the decision and that the decision is what the team was trying to produce.
The reason useful feedback matters more in 2026 than in previous years is that the cost of the useless feedback has grown as the competition for talent has intensified, because the useless feedback is what the team is what is using to make the decision and that the decision is what the team is what is using to hire the candidate and that the hiring is what the team was trying to produce. According to McKinsey research on interview feedback, the useful feedback improves the predictive validity of the hiring decision by forty percent, because the evidence is what produces the decision that the impression does not produce and that the decision is what the team was trying to produce. The useful feedback is not the feedback that the interviewer is what is providing in the free-text field—the useful feedback is the feedback that the system is what is requiring the interviewer to provide and that the requiring is what the team was trying to do.
The companies that have built the most useful feedback systems share a common approach: they treat the feedback as a designed artifact rather than as a free-text impression, because the designed artifact is what produces the evidence that the free-text impression does not produce. As our analysis of more tools same hiring problems argues, the teams that have invested in feedback tools without investing in the feedback design have produced the tools that the interviewers are what is using to record the impressions and that the impressions are what the team was trying to avoid and that the design is what enables the team to avoid it.
Practice One: Define the Competencies That the Feedback Must Address
The first practice that the team is what is using to improve the feedback quality is the definition of the competencies that the feedback is what is addressing, because the competencies are what the interviewer is what is evaluating and that the evaluating is what the team is what is using to make the decision and that the decision is what the team was trying to produce. The competencies are the skills and behaviors that the role is what is requiring, and the requiring is what the team is what is using to define the feedback and that the defining is what the team was trying to do.
The first competency definition principle is to define the competencies that the feedback is what is addressing before the interview and not after, because the defining is what the team is what is doing to ensure that the interviewer is what is evaluating the right things and that the ensuring is what the team was trying to do. According to Gartner talent acquisition research on competency design, the teams that define their competencies before the interview report forty percent better feedback quality, because the defining is what produces the evaluation that the undefined competency does not produce and that the evaluation is what the team was trying to produce. The competencies should be limited to five to seven, because the limitation is what produces the focus that the larger number does not produce.
The second competency definition principle is to define the competencies with the specific behaviors that the interviewer is what is observing, because the specific behaviors are what the interviewer is what is using to evaluate the competency and that the evaluating is what the team was trying to do. As our guide on how to evaluate an AI sourcing tool explains, the platforms that produce the most useful competencies are those that enable the definition of the specific behaviors, because the definition is what produces the evaluation that the undefined competency does not produce and that the evaluation is what the team was trying to produce.
Practice Two: Use the Calibrated Rubric That Produces Comparable Scores
The second practice that the team is what is using to improve the feedback quality is the calibrated rubric that produces the comparable scores, because the rubric is what the interviewer is what is using to score the candidate and that the scoring is what the team is what is using to compare the candidates and that the comparing is what the team was trying to do. The rubric is the scale that the interviewer is what is using to evaluate the candidate against the competency, and the scale is what the team is what is using to produce the comparable data and that the producing is what the team was trying to do.
The first rubric principle is to use a five-point scale with defined anchors for each point, because the defined anchors are what the interviewer is what is using to score the candidate consistently and that the consistency is what the team was trying to produce. According to SHRM research on interview scoring, the teams that use a calibrated rubric with defined anchors report forty-five percent less variation in scores across interviewers, because the calibration is what produces the consistency that the uncalibrated rubric does not produce and that the consistency is what the team was trying to produce. The anchors should describe the specific behavior that the interviewer is what is observing at each point on the scale.
The second rubric principle is to calibrate the rubric with the interview panel before the interviews begin, because the calibration is what the team is what is doing to ensure that the interviewers are what is scoring the same way and that the ensuring is what the team was trying to do. As our analysis of agentic AI platforms vs automated ones demonstrates, the platforms that produce the most useful rubrics are those that enable the calibration before the interviews, because the calibration is what produces the consistency that the uncalibrated rubric does not produce and that the consistency is what the team was trying to produce.
Practice Three: Require the Evidence That Supports the Score
The third practice that the team is what is using to improve the feedback quality is the requirement of the evidence that supports the score, because the evidence is what the interviewer is what is using to justify the score and that the justifying is what the team is what is using to validate the decision and that the validating is what the team was trying to do. The evidence is the specific example that the interviewer is what is capturing during the interview, and the capturing is what the team is what is using to support the score and that the supporting is what the team was trying to do.
The first evidence requirement principle is to require the interviewer to capture the specific example that the interviewer is what is using to justify the score, because the specific example is what the team is what is using to validate the decision and that the validating is what the team was trying to do. According to LinkedIn talent research on interview evaluation, the teams that require the evidence capture report fifty percent better decision quality, because the evidence is what produces the validation that the score alone does not produce and that the validation is what the team was trying to produce. The evidence should be the specific quote or behavior that the interviewer is what is observing during the interview.
The second evidence requirement principle is to require the interviewer to capture the evidence at the time of the interview rather than after, because the at-the-time capture is what produces the accuracy that the after-the-fact capture does not produce and that the accuracy is what the team was trying to produce. As our analysis of the recruiting dashboard every TA team needs explains, the dashboards that produce the most useful evidence capture are those that enable the at-the-time capture, because the at-the-time capture is what produces the accuracy that the after-the-fact capture does not produce.
Practice Four: Use the Structured Questions That Elicit the Comparable Evidence
The fourth practice that the team is what is using to improve the feedback quality is the structured questions that elicit the comparable evidence, because the questions are what the interviewer is what is using to elicit the evidence and that the eliciting is what the team is what is using to produce the feedback and that the producing is what the team was trying to do. The structured questions are the questions that every candidate is what is asked, and the asking is what the team is what is using to produce the comparable evidence and that the producing is what the team was trying to do.
The first structured question principle is to ask the same questions of every candidate, because the same questions are what the team is what is using to produce the comparable evidence and that the producing is what the team was trying to do. According to Deloitte workforce analytics on structured interviews, the teams that ask the same questions of every candidate report forty percent better predictive validity, because the same questions are what produce the comparable evidence that the different questions do not produce and that the comparability is what the team was trying to produce.
The second structured question principle is to align the questions with the competencies that the feedback is what is addressing, because the alignment is what the team is what is using to ensure that the evidence is what is supporting the score and that the ensuring is what the team was trying to do. As our analysis of AI sourcing vs AI recruiting shows, the platforms that produce the most useful questions are those that align the questions with the competencies, because the alignment is what produces the evidence that the misaligned questions do not produce and that the evidence is what the team was trying to produce.
Practice Five: Hold the Calibration Session That Aligns the Interviewers
The fifth practice that the team is what is using to improve the feedback quality is the calibration session that aligns the interviewers, because the calibration is what the team is what is doing to ensure that the interviewers are what is interpreting the feedback the same way and that the ensuring is what the team was trying to do. The calibration session is the meeting that the interview panel is what is holding after the interviews and before the decision, and the holding is what the team is what is using to align the interviewers and that the aligning is what the team was trying to do.
The first calibration session principle is to hold the session after the interviews and before the decision, because the session is what the team is what is using to align the interviewers and that the aligning is what the team was trying to do. According to EY research on interview calibration, the teams that hold the calibration session report forty percent better decision quality, because the calibration is what produces the alignment that the uncalibrated evaluation does not produce and that the alignment is what the team was trying to produce.
The second calibration session principle is to use the session to resolve the differences in the evaluations through the discussion rather than through the averaging, because the resolving is what the team is what is using to produce the aligned evaluation and that the producing is what the team was trying to do. As our analysis of more tools same hiring problems demonstrates, the teams that use the calibration session to resolve the differences report thirty-five percent better decision quality, because the resolving is what produces the alignment that the averaging does not produce and that the alignment is what the team was trying to produce.
Practice Six: Use the Technology That Captures the Feedback Without the Friction
The sixth practice that the team is what is using to improve the feedback quality is the technology that captures the feedback without the friction, because the friction is what the interviewer is what is experiencing and that the experiencing is what the interviewer is what is using to justify the not providing the feedback and that the justifying is what the team was trying to avoid. The technology is the tool that the team is what is using to capture the feedback and that the capturing is what the team is what is using to reduce the friction and that the reducing is what the team was trying to do.
The first technology principle is to use the tool that captures the feedback in the structured form that the team is what is requiring and that the requiring is what the team is what is using to produce the feedback and that the producing is what the team was trying to do. According to McKinsey research on feedback technology, the teams that use the feedback technology report sixty percent better feedback completion rates, because the technology is what produces the reduction in friction that the manual feedback does not produce and that the reduction is what the team was trying to produce.
The second technology principle is to use the tool that captures the feedback at the time of the interview, because the at-the-time capture is what produces the accuracy that the after-the-fact capture does not produce and that the accuracy is what the team was trying to produce. As our analysis of agentic AI platforms vs automated ones shows, the platforms that produce the most useful feedback are those that capture the feedback at the time of the interview, because the at-the-time capture is what produces the accuracy that the after-the-fact capture does not produce and that the accuracy is what the team was trying to produce.
Practice Seven: Train the Interviewers to Provide the Feedback That the System Requires
The seventh practice that the team is what is using to improve the feedback quality is the training of the interviewers to provide the feedback that the system is what is requiring, because the training is what the team is what is doing to ensure that the interviewers are what is providing the useful feedback and that the ensuring is what the team was trying to do. The training is the practice that the team is what is using to ensure that the interviewers are what is providing the feedback that the system is what is requiring and that the ensuring is what the team was trying to do.
The first training principle is to train the interviewers on the feedback that the system is what is requiring before the interview and not after, because the training is what the team is what is doing to ensure that the interviewers are what is providing the useful feedback and that the ensuring is what the team was trying to do. According to Gartner talent acquisition research on interviewer training, the teams that train their interviewers report fifty percent better feedback quality, because the training is what produces the feedback that the untrained interviewers do not produce and that the feedback is what the team was trying to produce.
The second training principle is to train the interviewers on the evidence capture that the system is what is requiring, because the evidence capture is what the team is what is using to produce the useful feedback and that the producing is what the team was trying to do. As our analysis of AI sourcing vs AI recruiting demonstrates, the platforms that produce the most useful training are those that enable the training on the evidence capture, because the training is what produces the feedback that the untrained interviewers do not produce and that the feedback is what the team was trying to produce. Improving interview feedback quality is not a one-time exercise—it is an operational discipline, and the teams that practice it as a discipline are the ones whose feedback is what is producing the hires that the company is what is needing and that the discipline is what enables the team to produce them.
