Human-Powered Hiring: Maximizing Manual Review Alongside AI in Tech

This article challenges the over-reliance on AI and ATS in tech hiring, advocating for a hybrid approach that prioritizes strategic manual resume review by hiring managers. It outlines a time-efficient process to improve candidate assessment, leading to higher quality hires, stronger teams, and more profitable organizations. #TechHiring, #Recruiting, #ProductManagement, #SoftwareEngineering, #UXDesign, #HumanResources, #AIinRecruiting, #BayAreaTech, #TalentAcquisition, #HiringStrategy

My recent post on the frustrations with Applicant Tracking Systems (ATS) resonated deeply [1]. It's clear from the unusually high response rate that the current hiring process, especially in tech, is broken. The rise of AI, while promising, seems to be exacerbating the problem, leading to candidate frustration and missed opportunities for companies.

In the competitive Bay Area tech landscape, roles for product managers, engineers, and designers often attract hundreds of applicants. While it's tempting to rely significantly on automated AI systems, I argue that a focused, human-centered approach to resume review is not only feasible but essential for quality hiring.

The Myth of Time Inefficiency

Let's address the elephant in the room: time. Based on conversations with hiring managers, a typical role might draw 700+ applications (let's round up to 1,000 for simplicity). I propose a three-pass manual screening process, detailed below, can be completed in approximately 5.4 hours (see Figure 1).

Figure 1:  Three-Pass Manual Resume Review Timing

Figure 1: Three-Pass Manual Resume Review Breakdown

• Pass 1 (Initial Screen): 1,000 resumes -> 200 promising candidates (approx. 200 minutes)

  • Quick scan (8 seconds) + open/close/flag (4 seconds)

• Pass 2 (Deeper Dive): 200 candidates -> Refined shortlist (approx. 80 minutes)

  • Detailed review (20 seconds)

• Pass 3 (Final Selection): Shortlist -> Top interview candidates (approx. 41 minutes)

  • In-depth analysis (120 seconds)

This 5.4-hour investment is often less than the time spent in meetings discussing using automated systems, and dealing with the associated shortcomings. More importantly, this human-centered approach directly impacts the quality of your team, a critical factor for any tech organization.

Why Human Review Matters

• Nuance and Context: AI struggles with transferable skills, varied role titles, and the subtle nuances that make a candidate exceptional. Human reviewers can identify potential beyond keywords and shallow associations.

• Quality Over Quantity: ATS systems often produce false positives and negatives, leading to missed opportunities and wasted interview time. A manual process allows for a higher initial bar, quickly eliminating clear mismatches.

• Fairness and Equity: Human review can mitigate biases inherent in AI systems, ensuring a more equitable hiring process. To further reduce human-biases, two, three or more human reviewers can independently process all three passes, then invite candidates for interviews whose resumes were selected by a majority or all of the reviewers. 

Practical Steps for Implementation

1. Strategic ATS Use: Limit ATS to essential filters like language proficiency or citizenship.

2. Hiring Manager Focus: Prioritize the hiring manager's time for creating a strong job description and conducting the three-pass review.

3. Delegation: Offload administrative tasks (resume collection, communication) to HR or administrative support.

4. Optimize Workflow: Simplify resume management by standardizing formats (PDFs) and using fast cloud storage, reducing open/close times that often result from poorly designed Human Resources systems.

In summary, a talent-driven market like tech requires hiring the right product manager, engineer, or designer for an organization to be as efficient and profitable as possible. While AI and ATS have their place, they should augment, not replace, human judgment. By reclaiming human-centered insight in the initial screening process, we can build stronger, more innovative teams.

[1] Swindells, C. (2025). Rethinking ATS: Are we losing top tech talent in the black box? Blog Article. https://www.techsuccesscoaching.com/post/rethinking-ats-are-we-losing-top-tech-talent-in-the-black-box

[2] Williams, M., & Moser, T. (2019). The art of coding and thematic exploration in qualitative research. International management review, 15(1), 45-55.