Hiring in the age of AI
Every candidate now looks flawless on paper and in the take-home — so your process can't tell you who's actually good. It was built for a different decade. I rebuild hiring for technology and fintech companies: the process, and the profile you're looking for in the first place.
Same loop. Seven years apart.
A process built when a polished CV and a clean take-home still told you something.
The shift
Everyone noticed that candidates now use AI. Far fewer have noticed that this makes the job different — and therefore makes a different kind of person the right hire. None of this means your team got worse at interviewing. The ground moved.
A candidate can generate twenty tailored CVs in an afternoon and produce a flawless take-home in twenty minutes. Every screening stage you built assumed effort was expensive. It isn't any more, so the stages that used to separate people now just pass everyone through.
Banning AI isn't the answer — it's unenforceable. Move evaluation to where AI use reveals ability instead of hiding it.
The skills that made someone a strong hire in 2019 are the ones now most easily automated. Meanwhile the traits that used to be nice-to-have — judgment under ambiguity, knowing when the output is wrong, taste — are the whole job.
AI doesn't make soft skills less relevant — it's the opposite. When technical work gets cheap, what's left is judgment, communication, and whether someone teaches the team what they've figured out. Almost nobody interviews for that.
Elez Shenhar
What I do
No benchmark report. Every deliverable is built with your hiring managers in the room, so it reads like your company wrote it — because your company did.
A week-long read on your current process. Low commitment, and it tells you whether the bigger work is worth doing.
Stage by stage: what still produces signal, what has been quietly zeroed out, and what it's costing you in bad hires and missed ones.
New stages designed for a world where candidates have AI — including formats where they're expected to use it, and you evaluate how.
A hiring bar rewritten for AI-augmented teams: the competencies that predict performance when the routine work is already automated.
A standing line to someone who has run these teams, for founders, CTOs and first-time heads of function.
How it works
I don't sell a redesign before I know what's broken, and I don't take a retainer with someone I haven't worked with. It protects your budget and my judgment.
You describe what's going wrong. I tell you whether it's a problem I'm the right person for — and if it isn't, who is.
A paid first look at your loop, small enough to approve without a procurement cycle. If you go on to the audit, the fee comes off it.
Interviews with your hiring managers, recruiters and recent hires. Funnel and outcome data. Ends with findings presented to your leadership team — by both of us.
We fix the highest-value thing the audit found. Your managers co-build it, run it under observation, and get coached on the first real loops.
Train-the-trainer so it survives without me, then a light retainer if you want a sounding board. The goal is that you stop needing one.
Free · No email required
Five plain questions about the process you're running right now. The read at the end matters more than the score.
Answer all five to see your read.Nothing is stored or sent anywhere.
Talk it throughProof
Three years running data science hiring at Klarna, at the scale where a process either holds or quietly falls apart.
About
Elez Shenhar · Founder
I spent 17 years building data and ML teams — most recently as Head of Data Science at Klarna, where I also owned hiring. Now I fix hiring processes that AI has quietly made useless.
At Klarna I led 30 people across seven teams in the Payment Methods domain, and hired around a hundred data scientists over three years. I inherited the interview process and rebuilt it around one idea: put candidates at ease early, so you see who they actually are instead of who they perform being under pressure. It worked better than I expected. Not one person hired through that pipeline was ever let go.
The hire I'm proudest of had no degree at all. He was talented and he wanted it, and every conventional filter would have removed him before anyone spoke to him. That's the part most companies still get wrong, and AI has made it worse — the credentials and the polished application are now the cheapest things a candidate can produce, and they're still what most loops screen on.
The belief I hold that most of my peers don't: AI is making soft skills more important, not less. When the technical work gets cheaper, what remains is judgment, communication, and whether someone teaches the rest of the team what they've figured out. Almost nobody is interviewing for that.
Before Klarna: Head of Machine Learning at MotionTag in Berlin, and years in quantitative research and software before that. I do this because the hiring work was the part of the job I never wanted to hand over — and I’m also CTO and co-founder of eXation.ai, where we build generative-AI systems for other companies, so I’m not theorising about what AI does to technical work. I watch it happen to my own team every week. On the advisory side you get me personally on your calls, not an associate, and I hold to three engagements at a time so that stays true.
Fit
Writing
Anyone can get a good answer out of a model now. The signal is in what happens next: whether they build a check that fits the specific task, or trust the output because it reads well.
A CV is worth one conversation about someone’s history. Past that it tells you nothing AI can’t manufacture — so evaluate the work you watch them do, and stop letting paper into the room.
Ask about the thing they actually love doing. A candidate who is at ease thinks clearly for the next two hours — and you spend those hours assessing ability instead of nerves.
Start here
A 30-minute call, no pitch. If I'm not the right fit I'll say so on the call and point you somewhere better.
I run three engagements at a time. As of September 2026: accepting new work.
Or email directly: elez@shenharconsulting.com · LinkedIn
Legal
Shenhar Consulting & Holding, Inc. (Delaware, USA) is the controller of personal data collected through this site. Contact: elez@shenharconsulting.com.
Visitor numbers are measured with a privacy-first analytics tool that sets no cookies and does not follow anyone between websites. It records aggregate counts only — page views, referring sites, countries, and device types — and never identifies individual visitors. There are no advertising tags, no tracking pixels, and no cookies set by this site. Fonts are served from this site itself, so no third party sees your visit that way.
Answers to the hiring diagnostic are processed entirely in your browser. They are never transmitted, recorded, or seen by anyone. Closing the page discards them.
Solely to reply to you and to discuss possible work together — steps taken at your request before entering a contract, and a legitimate interest in responding to business enquiries. Your details are never sold, rented, or used for marketing. There is no newsletter and no automated sequence.
Enquiries that do not lead to work are deleted within 24 months. Records relating to actual engagements are kept as long as required for contractual, accounting, and tax purposes.
Only the providers needed to operate the business: the site and its contact form run on Netlify, and email is handled by the domain’s mail provider, each acting on instruction. Data is handled in the United States, where this company is established.
If you are in the EU, EEA, or UK you may request access to your data, correction, deletion, restriction or portability, and you may object to its use. Write to the address above and you will get a reply within 30 days. You may also complain to your national supervisory authority — in Portugal, the Comissão Nacional de Proteção de Dados.
This site is aimed at businesses and is not directed at anyone under 16.
Last updated 24 August 2026. Material changes will be posted here.