Why the Future of Job Search Is Human-Led and AI-Powered
Series: Voices in Business | Guest: Aviv Ziv, Founder at Resumella
When Aviv Ziv and I worked together at AppsFlyer back in 2019, she was already the person in the room who thought most carefully about people, understanding what they were truly capable of, what they needed to grow, and how to position them for success. Seven years on, she has built an entire company around that instinct.
Resumella is a talent tech consultancy and, increasingly, a platform and the more Aviv explains it, the more it sounds like the antithesis of everything that is broken about modern job searching.
It is methodical where the job market is chaotic. It is personal where algorithms are blunt. And it is, quietly, one of the more thoughtful examples I have come across of how artificial intelligence can amplify human judgment rather than replace it.
We caught up recently, and I left the conversation with pages of notes and a renewed conviction that the human in the loop is not a nice-to-have in the age of AI. In Aviv's world, that is the whole point.
The Problem She Set Out to Solve
Aviv spent years inside some of the world's fastest-growing tech companies — AppsFlyer, Elementor — building and scaling learning and development functions.
She had a front-row seat to how hiring actually works: what makes a recruiter pause on a profile, what gets a CV into the yes pile, and what causes strong candidates to systematically undersell themselves.
"I kept seeing brilliant people who were invisible to the market," she says. "Not because they lacked experience or skill, but because they didn't know how to articulate their value in the language that hiring systems and hiring managers respond to."
She also spent six years as a founding member of LeadWith, a non-profit advancing women in tech, building communities and pipelines for professionals who were overlooked by traditional sourcing. The pattern she saw there was the same: the gap was rarely about capability. It was almost always about positioning and strategy.
Resumella was her answer — a structured, research-backed approach to job search that treats career progression not as a series of applications thrown into the void, but as a strategic campaign.
The 3-Pillar Model: Story, Value, Research Strategy
At the heart of everything Aviv does is a framework she calls the 3-Pillar Job Search Model. It sounds deceptively simple, but the rigour underneath it is significant.
Pillar 1 — Story. Before anything else, Aviv works with clients to excavate and articulate their professional narrative. Not the sanitised, generic version that lives on most CVs, but the actual through-line: what they have consistently done, how they uniquely approach problems, and what kind of impact they create. This is the foundation everything else is built on. Once the story is clear, the work shifts to translating it into value language — the specific, quantifiable, contextual evidence of what a person brings to an organisation. This is where most professionals struggle most. We are trained to describe tasks, not outcomes. Aviv retrains that instinct.
Pillar 2 — Market. The second pillar is essentially a thorough market intelligence whereby Aviv researches the client's market, the target industry, companies, roles, and competition. This is where data becomes critical. The goal is to create a very clear alignment between the individual's professional story and the market.
Pillar 3 — Job Search Strategy. The third pillar is arguably the most underestimated. Job searching without a strategy is just luck. Aviv builds a clear targeting framework with each client: which companies, which roles to target, in what channels to search, and how to increase visibility — and how to position accordingly.
"The model works because it creates alignment," she explains. "Between who you actually are, what the market needs, and how you show up. Most people are solving only one of those three. You need all three working together."
When AI Enters the Room
Here is where the conversation got particularly interesting for me, sitting on the other side of the table as someone who thinks daily about how automation can serve human work.
Aviv has built automation deep into her process — but not in the way you might expect.
She uses tools like Apify to scrape and aggregate real-time data: job postings, company signals, hiring trends, language patterns that recruiters and hiring managers actually use. This feeds into her research and strategy pillar in a way that would have taken weeks of manual work just a few years ago. Today, she can surface patterns across hundreds of data points and translate them into a targeted positioning strategy for a specific client in a fraction of the time.
She is also developing a proprietary app that generates a score for each job against each candidate profile. Not a generic compatibility match — a nuanced, context-aware assessment that draws on her model — the research, the candidate's value proposition, and the specific language of the job description. It helps clients prioritize where to invest their energy, and it makes the strategy tangible rather than abstract.
But what strikes me most is how Aviv talks about prompting. She treats prompt engineering the way a good writer treats editing — it is never done. She is constantly reframing, refining, testing new angles, pushing the model to surface insights she had not thought to look for. The intelligence is hers. The AI is her leverage.
"AI gives me superpowers," she says, without a trace of hyperbole. "It lets me do in hours what used to take days. But the judgment — what to ask, what to do with the answer, what it means for this specific person — that is still entirely human."
The Human in the Loop Is Not Optional
This is the part of our conversation I keep coming back to.
There is a version of what Aviv is building that could be fully automated. You could imagine a platform where a candidate uploads a CV, an algorithm generates a score and a suggested positioning, and off they go. Faster. Cheaper. Scalable.
Aviv has consciously chosen not to build that. Not because she is anti-technology — she is clearly anything but — but because she understands that the value she creates is not extractable from the human relationship.
"Job searching is one of the most emotionally loaded things a person can do," she says. "It involves identity, fear, self-worth, and a lot of uncertainty. An algorithm cannot hold that. And if you skip over it, you get technically correct output that doesn't actually land — because the person isn't ready to use it."
The coaching, the questioning, the reframing of how someone sees their own experience — that is what creates the conditions in which her frameworks and her tools can do their best work.
AI is only as good as the human intelligence guiding it, and human intelligence is only as effective as the trust and understanding behind it.
She has built a methodology in which every layer depends on the one before it. Strip out the human, and the whole thing loses its purpose.
The Measure of Success
Aviv's success metrics are, I think, some of the most elegant I have heard in the talent space. It is not only time-to-placement. It is not only the application-to-interview rate. It is also this: clients getting headhunted on LinkedIn after she revamps their profile.
"Though there's never a guarantee of getting headhunted, once I write a new profile for my clients that takes into account LinkedIn SEO, it maximises the chances of being headhunted, and it's a strong indication of what works," she says. "When someone is not only chasing opportunities — but when opportunities are also coming to them. That is the shift we are trying to create."
It is a high bar. It means that the story, the value articulation, and the positioning have all landed with enough precision that the market starts responding without the candidate having to push. The algorithm — LinkedIn's this time — surfaces them to the right people. Recruiters reach out unprompted. The candidate goes from invisible to sought-after.
It is also, I would note, a deeply human metric. Not a ratio. Not a data point. A moment where someone's professional life changes.
What This Means for the Rest of Us
I started this conversation thinking about Aviv's work as a story about job search. I finished it thinking about something much broader.
The question of how much to automate, and where to keep the human in the loop, is one that every business using AI is navigating right now. Aviv's model is a case study in getting that balance right — not by avoiding technology, but by being relentlessly clear about what technology can and cannot do.
It can process more data, faster. It can surface patterns. It can score, rank, and prioritize. It can extend the reach of a skilled human practitioner enormously.
What it cannot do is build trust. Ask the right question at the right moment. Understand the gap between what someone says and what they mean. Know when to push and when to hold space.
Aviv does all of that. The AI does the rest. And the combination is, by her own account, the most powerful version of what she has ever been able to offer.
That, to me, is what good AI looks like in practice — not a replacement for human expertise, but a multiplier of it.
Aviv Ziv is the Founder of Resumella, a talent development consultancy helping professionals in global tech navigate job search with strategy, clarity, and precision. You can find her on LinkedIn and at resumella.com.
Want to share how AI is reshaping your professional life? Reach out — you might be the next voice in this series.
Sarah Rolland is the founder of Operato AI, an AI automation agency helping businesses build smarter workflows. Follow the conversation on operato-ai.com/blog.