Why Adam Grant Actually Recommends Interview Do-Overs
Most companies view one-shot interviews as definitive, often discarding candidates after a single poor performance. Adam Grant, the Wharton organizational psychologist, recommends giving uncertain applicants a “do-over” on job-related tasks to better gauge their fit.
This approach hinges on the mechanism of **shifting evaluation from static snapshots to dynamic task performance**.
By testing how candidates handle real work scenarios over time instead of relying solely on first impressions, companies unlock a more durable hiring signal. This can transform talent acquisition from a costly guessing game into a self-correcting system.
Breaking the Single-Interview Constraint
The traditional hiring process treats interviews as binary gates: pass/fail based on a single encounter. This represents a rigid constraint tying decision-making tightly to momentary candidate behavior, which is often affected by nerves or ambiguous questions.
Adam Grant challenges this by advocating for allowing a second chance—an operational “do-over”—focused on topical tasks, not just conversational interviews. This shifts leverage from subjective impressions to objective task-based evidence.
For instance, instead of a single hour-long interview, a candidate could complete a brief project or a simulation relevant to the role. This reveals real aptitude rather than polished rehearsed answers.
This constraint shift reduces false negatives—candidates who might perform poorly once but excel when given more relevant assessments.
System Design That Grows Talent Signal Over Time
Grant's recommendation is essentially a mini system for talent discovery embedded within the hiring process. The key mechanism is to treat interviews as iterative tests rather than one-off judgments.
Systems that incorporate this approach gather richer data points on candidates across multiple interactions. This compounds the quality of decision-making with low additional cost.
Compare this to AI screening tools or standard resume filters that provide snapshots but often miss growth potential or context. Grant’s method sidesteps these bottlenecks by testing actual job-related tasks directly.
This approach resembles process documentation best practices, where continuously updated inputs lead to better outcomes, as covered in our analysis on process documentation best practices.
Why Operators Should Care About Talent Evaluation Beyond Resumes
Companies lose on talent every time they fail to identify real capabilities masked by interview stress or poor question design. By embracing this “do-over” mechanism, they turn hiring into a system that self-corrects.
It's a leverage move that reduces costly mis-hires and boosts team performance sustainably, since better matches improve collective output. This also addresses the hidden constraint of evaluation noise in hiring.
Similar to why one bad employee hurts whole teams, avoiding bad hiring decisions compounds operational risk. Grant’s method reduces this risk not by more interviews, but by smarter, task-centered repetitions.
This insight applies not only to startups but also to large enterprises struggling with scaling recruitment without exploding costs or quality variance.
Avoiding Common Alternatives That Waste Resources
Most businesses escalate costs by simply adding more interview rounds or relying on AI resume scanners. These don't solve the underlying constraint: lack of accurate job-task performance data during evaluation.
Grant’s approach avoids this by focusing on the real-world task constraint—whether a candidate can do the job once given a fair chance to demonstrate it.
This is more scalable than extensive panel interviews, which can bottleneck scheduling and strain human resources, while also improving accuracy beyond biased CV reviews.
Operators can think of this as shifting from constant human intervention to a more autonomous evaluation system, lowering friction and enhancing quality simultaneously.
Related Tools & Resources
Adam Grant’s emphasis on iterative, task-based evaluation reflects a broader need for clear, well-documented operational processes that grow and improve over time. Platforms like Copla empower teams to create and manage standard operating procedures that capture best practices, making it easier to implement and scale such innovative hiring methodologies effectively. Learn more about Copla →
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Frequently Asked Questions
Why should companies consider offering interview do-overs?
Companies should consider interview do-overs because single interviews can be a rigid constraint influenced by nerves or ambiguous s. Giving candidates a second chance on job-related tasks helps reveal true capabilities and reduces costly mis-hires by turning hiring into a self-correcting system.
How do task-based evaluations improve the hiring process compared to traditional interviews?
Task-based evaluations test candidates on real work scenarios over time, providing objective evidence of skills rather than relying on subjective first impressions. This method reduces false negatives and leads to more durable hiring signals, improving the accuracy of talent selection.
What are the drawbacks of relying heavily on AI resume scanners and multiple interview rounds?
Relying on AI resume scanners and adding more interview rounds increases costs and often misses candidates' growth potential or task performance. These approaches do not address the core evaluation constraint which is accurate job-task performance data, leading to inefficiencies and possible quality variance.
How can a multi-interaction interview system benefit talent discovery?
A system that treats interviews as iterative tests collects richer data points across multiple interactions, compounding decision quality with low additional cost. This dynamic approach grows the talent signal over time and shifts evaluation from snapshots to continuous performance assessment.
What operational risks can bad hiring decisions cause and how can interview do-overs help?
Bad hiring decisions compound operational risk by harming team performance and increasing turnover costs. Interview do-overs reduce this risk by focusing on task-based repetitions that better match candidates’ real capabilities to job requirements.
Why is shifting from subjective impressions to objective task-based evidence important in hiring?
Subjective impressions can be influenced by candidate nerves or ambiguous s leading to inaccurate hiring decisions. Objective task-based evidence grounds evaluations in actual job performance, creating fairer assessments and improving hiring outcomes.
How can companies implement interview do-overs without significantly increasing costs?
Companies can implement interview do-overs by assigning brief projects or simulations relevant to the role instead of long interview panels. This leverages low-cost, task-focused assessments to gather valuable data, avoiding costly scheduling bottlenecks and resource strains.
What role do operational processes play in supporting task-based hiring approaches?
Well-documented operational processes help embed task-based evaluations into hiring systems, making them scalable and repeatable. Platforms that support standard operating procedures can simplify implementing iterative, objective talent assessments that improve over time.