Interview with Efrat Dagan, Founder of Workaround

Introduction

Efrat Dagan is a recruitment powerhouse who turns hiring chaos into strategic advantage, drawing from over 25 years of scaling global talent operations at tech giants. From leading teams at Google in Israel and Silicon Valley, to Lyft's autonomous vehicles division and global recruiting at Next Insurance, she's seen it all - and spotted the gaps that hold companies back. Now as founder of Workaround, launched in 2021, she helps tech firms build data-driven hiring infrastructures for sustainable growth. In this interview for the Top HR to Follow list, we dive into her career evolution, the real challenges of scaling recruitment in an AI-fueled world, and why smart decisions beat sheer volume every time. Efrat's approach - methodical, insight-led, and unapologetically practical - positions her as a key voice for leaders navigating talent in uncertain times.

Can you share your career path - and was founding Workaround something you planned from the start, or did it evolve along the way?

"I've been in the field for over 25 years, managing and leading global recruiting operations at some of the world's most complex and fast-growing companies. From Google in Israel and Silicon Valley, through Lyft's autonomous vehicles division, to heading global recruiting at Next Insurance. Over the years, especially working closely with CEOs, founders, and executive teams, I identified a recurring gap: Many companies reach the 'scale' stage without being truly ready - not in infrastructure, methodologies, or recruiting strategy and decision-making. I realized the strongest companies don't necessarily hire more; they hire smarter. Founding Workaround in 2021 wasn't a 'backup plan' - it was an organic outcome of a real market need. Stemming from the sense that recruiting can and should be done differently: data-based, with a clear strategy, tailored to the pace and complexity of the tech world. It was born to help tech companies build the right infrastructures for hiring, growth, and systematic, sustainable decision-making. Since then, I've been guiding companies at various stages - from early-stage startups to global organizations - in constructing decision-making systems for recruiting."

What are you most looking forward to in your work today - and what still excites and thrills you?

"What I'm most looking forward to today is the moment a company 'deciphers' its hiring and growth challenges and achieves a breakthrough. The process where an organization shifts from routine, intuitive hiring to a structured, precise, and efficient growth system is fascinating, and it gives me immense satisfaction. What still truly excites me is the moment when leadership realizes that hiring isn't a tactical action to fill roles - it's one of the most important strategic engines for company growth."

When you say you're 'shaping how tech companies hire, scale, and make decisions' - what does that look like in practice?

"In practice, it happens across three main dimensions. The first is methodological: We help companies define success very precisely, translate it into measurable signals, and examine over time which signals truly predict performance and contribution. The second is process-oriented: How does the hiring panel look end-to-end, where data fits in, where it's right to integrate AI, and where it's forbidden to replace human judgment. How decisions are made in a changing organization, and how to maintain quality and standards even during scale. The third is strategic-managerial: Close work with founders and executive teams on complex hiring decisions - as when to bring on the first senior role, how to build a 12-month Hiring Plan, and how to avoid costly hiring mistakes that can impact years ahead. The goal isn't to 'hire fast,' but to build a system that knows how to make good decisions over time."

What do you see as the biggest hiring challenges today in tech companies - and what do you think most companies misdiagnose?

"The big challenge today isn't a talent shortage - it's excess noise. Too many resumes, too much data, too many tools, and too little clarity on what truly predicts success. Many companies diagnose the problem as 'hard to find candidates,' when really it's in role definition, interview quality, or inconsistent decision-making. Copy-pasting from other companies is almost always an imprecise solution. There are three key challenges now: Maintaining quality - in a time when job tasks change rapidly, every hire becomes critical; quality is no longer a luxury, it's a growth prerequisite. Economic uncertainty: The need to hire in a targeted way, with a clear view of ROI for each hire. And competition for new skills - especially in AI, data, and ML, where the shortage is structural and requires entirely different thinking."

How is AI changing the hiring process (sourcing, screening, interviews, decision-making) - and where do you think companies are overusing or underusing it?

"AI is already changing nearly every stage of the hiring process. In sourcing: It enables more precise identification of passive candidates and creating personalized outreach at scale. In screening: It can perform quick, more objective skills matching, but here's the biggest risk - algorithmic errors can amplify biases and miss potential. In interviews and decision-making: The significant shift is in processing interview data, transcription, pattern analysis, and providing predictive scores. I see two common mistakes: Overuse - relying too heavily on AI in early screening, as if success can be predicted from resumes and text alone. And underuse - almost not using AI for post-hire depth analysis: Which interview truly predicted success, which signal worked, and where did we err. In my view, AI should be a partner, not a judge."

What role should data play in hiring decisions? Which signals best predict success - and which are mostly 'noise'?

"Data's role isn't to replace judgment but to sharpen it. It allows validating intuitions, comparing candidates consistently, and linking the hiring process to actual business outcomes. The strongest signals, in my eyes, are: Relevant past performance, learning and adaptability, fit to the opportunity and culture, and ability to work with tools and data-based thinking. The big noise comes from: Relying on educational institutions or grades, fleeting impressions without methodology, and years of experience instead of quality and relevance. The common mistake is measuring what's easy to measure, not what truly predicts success."

How do you help executive teams make better hiring decisions under uncertainty, time pressure, and rapid growth?

"My focus here is turning reactive decision-making proactive. We work in three dimensions: Risk mapping - to understand where we must be very precise and slow down (exec roles, core functions), and where we can afford higher speed. Consistency and standardization - building a Hiring Matrix that pre-defines clear success criteria, so all interviewers seek the same signals even under time pressure. And scenario planning - creating a flexible Headcount Plan for different market scenarios, so leadership doesn't start thinking only after reality has shifted. The goal isn't to eliminate uncertainty but to manage it."

What is 'scale' really beyond headcount growth - and what People infrastructures/systems must companies build before the next stage?

"Scale isn't headcount growth. Scale is the organization's ability to maintain decision quality as complexity increases. Before the next stage, companies must build three infrastructures: Role infrastructure - high clarity on responsibilities, expectations, and interfaces. Decision infrastructure - how to hire, promote, and move people. And data infrastructure - not just reports, but understanding what truly predicts success in this specific organization. Without it, scale looks good in numbers and crumbles from within."

Looking ahead: What will differentiate companies that hire and grow right in the next decade - and what's one tip you'd give founders or People leaders building now?

"The companies that succeed in the next decade will be those that build people decision-making as a real competitive advantage. Not just good hiring, but: Smart role definitions in a world where roles change rapidly, interviews that spot potential, adaptability, and learning - not just past experience; proper AI use as a decision supporter, not replacer; and data tied directly to performance, not just process metrics. The big challenge is that not only people change, but roles themselves. Many core roles today will look entirely different in three-five years, and companies still hiring to old job descriptions are basically hiring for the past. So my tip for founders and People leaders is simple: Don't build hiring systems. Build decision systems. Systems that work even when the future is unclear, even when the role isn't fully defined yet."

Conclusion

In essence, Efrat Dagan embodies the shift from reactive recruiting to strategic foresight, proving that in tech's volatile landscape, success hinges on clear signals, smart data, and human-centered decisions. Her insights cut through the noise: Hiring isn't about volume - it's about building scalable systems that predict and sustain growth amid AI disruptions and economic flux. As companies race to adapt, Efrat's grounded expertise reminds us that the future favors those who prioritize quality over quantity, turning talent challenges into enduring advantages.