Courtney PackardDesign hiring expert · ex-McKinsey design recruiter
Pauline BertryCo-founder, Polar Bear · ex-McKinseyWho to hire now that AI moved the goalposts, how to assess designers when a polished portfolio proves nothing, and how to run the process without burning your team.
Every design leader we talk to is asking the same question: how should my team look now? Heads of design compare notes at conferences and nobody has a settled answer. AI moved the goalposts on what one designer can do, and with them, the goalposts on what companies expect from one hire.
For a 40-person agency this is a sharper problem than for a 4,000-person firm. You have no recruiting team, your people run at 95 percent utilization, and one wrong hire costs you months of salary plus the client work that suffered while you figured it out. A bad hire was always expensive. In 2026 the interview itself got harder too, because a polished portfolio no longer proves the person behind it can do the work.
This guide covers who to hire, how to assess them, how to run the process without burning your team, and what to do about the junior talent question everyone is avoiding. It comes out of a long conversation between Courtney Packard, a design recruiter who spent years building and scaling design teams for one of the world's largest consultancies, and Pauline Bertry, co-founder of Polar Bear.
Three shifts define the design hiring market right now. Each one crept in over the past few years and accelerated hard in 2026, and the data backs up what design leaders feel on the ground.
Mentions of AI in job listings across design and adjacent industries more than doubled in both 2023 and 2024, and design has overtaken technical expertise as the most in-demand skill in AI-related postings. Companies post fewer roles for a pure visual designer or a pure researcher; they want capability across the whole design process. One caveat: end-to-end does not mean shallow. The strongest candidates keep a T-shape, one deep area surrounded by working competence.
New-role starts for people with under one year of experience fell by half between 2019 and 2024 at large tech firms and scaled startups, consistently across functions including design. Junior roles still exist, but fewer of them, and the median posted role now asks for someone who can operate without supervision from week one.
Junior roles in the most AI-exposed fields are seven times more likely to demand traditionally senior skills such as leadership and strategic thinking, and these "seniorised" entry-level roles are the segment that keeps growing. Senior design postings reach past design too: design and engineering, design and product, designer plus vibe coder.
The common thread: we expect more of every human we hire, because AI raised the ceiling on what one person can produce. A single job posting in 2026 asks for what two or three postings asked for in 2021.
"Knows AI" is too vague to hire against. In practice, three distinct skill sets emerge, and your next hire needs at least one of them depending on what your studio sells.
The person knows how to design products with AI features in them: conversational interfaces, AI-assisted workflows, the emerging patterns around how AI capability shows up inside a product. If your clients are building AI into their products, and in 2026 almost all of them are, you need this on the team.
The person uses AI inside their own design workflow: prototyping with AI tools, generating and pressure-testing variations, moving from idea to testable artifact in hours instead of weeks. This skill set changes your unit economics as a studio.
The rarest of the three, and the most optional. Where trust forms and breaks, what users expect a system to remember, how a conversational product handles uncertainty. Not every studio needs this on payroll. If you offer UX research and testing as a service, you do: your clients ship AI products, and researching them well requires someone who understands AI from the inside.
Decide which of the three you are hiring for. Product-mindedness sits underneath all of them as the foundation. A designer who cannot connect design decisions to business outcomes will struggle in a small studio regardless of their AI fluency.
Small teams do not need a seven-round process. You need three interviews, each with a written, agreed answer to one question: what exactly are we assessing here? When that answer lives only in each interviewer's head, every interviewer assesses against a different imaginary candidate, and your debriefs turn into opinion battles.
The trait to probe hardest is adaptability. Ask about a project where the plan changed, feedback contradicted their design, or new information arrived late. Listen for whether the story includes an adjustment. The red flag pattern: the candidate describes holding their position because they believed in it, with no compromise and no update to their thinking. Conviction is good. Conviction that never once bent to evidence is a preview of your next difficult quarter.
You are assessing two separate things and it helps to name them. First, craft: is the work good? Second, ownership: can the candidate walk you through the decisions, defend them, and tell the story in a structure you can follow? A surprising share of designers, including talented ones, cannot narrate their own work. In a small studio where your designer talks to clients, structured storytelling is a core job skill.
The past two interviews cover what the person did. This one covers what they can do, in the room, on a problem they have never seen. Keep it small and realistic, and tell the candidate in advance what a good outcome looks like.
If you level your roles (designer, senior designer, lead), write down what you expect at each level in the portfolio review and the design exercise. Titles from previous jobs tell you almost nothing. We have both interviewed a "lead designer" who turned out to be the only designer at their company. The title was accurate in the loosest sense and useless as a signal. Written level expectations let you set the title aside and ask what the person did.
Compress to two sessions by extending the portfolio review and the design exercise by ten minutes each and weaving the behavioral questions in. Do not skip the behavioral content itself. Skip only the separate calendar slot.
Three focused interviews for a shortlist of candidates costs your team real hours. The alternative costs more. A wrong hire means the salary, the months of ramp-up, the client work that suffered, and the second recruiting round you now run anyway.
One question does most of the work:
"How are you using AI in your design process?"
The candidate tells you where AI helps them, where it fails, where they stay careful, and how their usage changed over the past year. Specific tools, specific tasks, specific judgment calls.
The candidate spends the answer on skepticism: why AI output is bad, why it threatens the craft, why they avoid it. Skepticism itself is healthy. An answer that contains only skepticism, from a candidate joining a studio that works AI-forward, predicts friction you will manage for the next two years.
The mirror image. AI does everything, no limitations mentioned, no place where their own judgment overrode the tool. This candidate may be outsourcing their thinking, and the portfolio problem below tells you why that matters.
A pattern every design recruiter now recognizes: a stunning portfolio arrives, the visual work looks exceptional, and the candidate then falls apart the moment you ask them to explain a decision. AI built the portfolio. The candidate cannot speak to the work because the work is not theirs in any meaningful sense.
You will not always spot AI-built portfolio pieces from the artifacts alone, and you should stop trying. The interview process is the filter. A candidate who did the work can defend it under questioning. A candidate who prompted the work cannot.
Portfolios have one job: show how the designer thinks. For years the standard was a case study walking through problem, process, and outcome. The next iteration, which we expect to become standard, adds a layer: where in this project did AI do the work, and where did human judgment come in? Where did the designer push back on what AI produced, and why?
Almost no portfolios show this today. As an employer, you can ask for it anyway. In the portfolio review or the design exercise, ask the candidate directly: how would you use AI on this problem, and where would you not trust it? The candidates worth hiring light up at this question. They have opinions, examples, and scars. The candidates who used AI as a substitute for thinking go quiet.
The third level separates a designer who works with AI from an operator who forwards its output.
The full assessment sits on three levels: does the person use AI at all, how far along are they, and, for the advanced ones, where does their human judgment enter the loop.
Most small agencies run hiring as an inbox: applications arrive, someone looks when they have a spare hour, candidates fall through cracks, and six weeks later nobody remembers who was promising. Two lightweight structures fix most of this.
Assign a specific designer on your team to review incoming portfolios weekly. This does not need to be your most senior person; a mid-level designer with good judgment works fine. Even the most seasoned design recruiter wants a practicing designer's eye on portfolios, and in a studio without a recruiter this role is the backbone of your pipeline. The weekly cadence matters more than the hours spent. Portfolios reviewed within a week convert to interviews. Portfolios reviewed within two months convert to candidates who took another offer.
You need one place that answers: who applied, who is in which stage, who is waiting on us. A Notion board or a Trello works at small volume. Purpose-built applicant tracking systems exist at price points a 40-person agency can afford, Lever being a solid example, and they pay for themselves once you run more than a few searches a year. The tool matters less than the discipline: no candidate exists outside the system.
Add the written assessment criteria from Part 03, and you have the full lightweight stack: clear profiles, three structured interviews, one owner for portfolio review, one source of truth for pipeline. This is a weekend of setup work that most agencies never do.
The seniority shift comes with uncomfortable math. If you only hire experienced designers, you have no bench. Your seniors will leave or get promoted, and in three years you will shop for the same expensive, scarce profiles again, in a market where everyone else does the same.
The assumption worth challenging: that end-to-end capability requires years of experience. It often correlates, but the underlying traits are innate more than earned. Some designers with one year of experience are product-minded, structured problem solvers with real curiosity. Both of us have hired at that level and watched those hires outperform their titles. You find them by assessing for the traits directly, through the behavioral interview and the design exercise, rather than filtering by years.
There is a second argument for junior hires that is specific to this moment. Junior designers entering the market now grew up professionally with AI tools. Many of them carry the skill sets from Part 02 that your existing team lacks and has no utilization slack to learn. Hiring a curious junior who builds with AI natively, and letting them absorb your industry expertise, is often a better bet than hiring a resistant senior and trying to convert them.
Two honest caveats before you post the junior role:
A junior hire without someone who has time to coach them is a junior hire you set up to fail. If your team runs at full utilization, solve that first or scope the junior's first quarter with care.
The same curiosity that makes them valuable makes them expansive. Someone senior needs to hold the frame so the energy lands on the right problems, and your product vision does not get relitigated the day before a release.
Before you decide you cannot afford junior talent, price the alternative: a team with no succession plan, no AI-native capability, and a single point of failure at every senior seat, three years from now.
Hiring well solves half the problem. The designers this guide helps you find are exactly the people other companies will try to take from you, and they stay for one reason: they can see themselves growing where they are.
That is what Ron does. Ron is Polar Bear's AI Talent Manager. He lives in your Slack or Teams and does the people work that growing agencies always plan to do and never have time for, every week:
You usually can’t — not reliably, from the artifacts alone. Stop trying to spot it and let the interview do the filtering: a candidate who did the work can walk you through and defend every decision under questioning; a candidate who prompted the work cannot. A beautiful portfolio plus vague answers about decisions is the tell.
Three structured interviews, each with a written answer to “what exactly are we assessing here?”: a behavioral interview that probes adaptability, a portfolio review that assesses craft and ownership separately, and a small, realistic live design exercise with expectations shared in advance. Lean teams can fold the behavioral questions into the other two sessions.
At least one of three distinct skill sets, depending on what your studio sells: designing for AI (AI features and conversational interfaces in client products), building with AI (prototyping and iteration inside the designer’s own workflow), or researching human‑AI interaction. “Knows AI” is too vague to hire against — decide which one before writing the posting.
Yes — if coaching capacity exists. Juniors are the succession plan: seniors leave or get promoted, and the whole market is crowding into the same senior profiles while the junior on‑ramp narrows. Juniors entering now grew up professionally with AI tools and often carry exactly the skills existing teams have no utilization slack to learn.