{"slug": "how-to-hire-an-ai-native-product-manager", "title": "How to hire an AI-native product manager", "summary": "Companies including Anthropic, Ramp, Notion, and Stripe are rewriting product manager hiring processes to focus on live AI direction and error detection rather than polished deliverables, according to an analysis of their current job postings and interview loops. The shift responds to AI tools making traditional screening signals like résumés and case studies unreliable, with the new bar being a candidate's ability to direct AI and catch its mistakes. The article provides a full rebuild of the hiring pipeline, including job description language, screening questions, a live interview format, and a decision rubric.", "body_md": "# How to hire an AI-native product manager\n\n### Rewrite the job description around real AI usage. Screen for the last thing they built. Watch them direct an AI live. Decide on judgment, with a fluency bar.\n\nThe headcount finally came through. You need a product manager, your recruiting partner needs a job description by Friday, and the process sitting in your ATS (the software your whole recruiting pipeline runs on) is the one you’ve run since 2023. Résumé screen, recruiter call, case-study interview, take-home, panel, offer. Every stage works the same way: the candidate produces something, and you grade it. A résumé. A structured answer. A polished deck. **Look at it honestly and your hiring process is a machine for grading documents.**\n\nHere’s the problem: AI just made polished documents free. The tight résumé, the crisp case structure, the take-home that used to signal “this person is serious”: any candidate with Claude and an evening now produces all three. You’ve probably felt it already, reading applications that all sound suspiciously excellent. And it happened at the worst possible moment, right when **you were asked to make your team AI-first** and every open seat became a chance to hire the mindset. **The signal collapsed at the exact moment the stakes went up.**\n\nSo I went and read what the companies furthest into this shift actually did about it. I pulled the live PM job posts at Anthropic, Ramp, Notion, and Stripe, straight from their own job boards this week. I read the interview loops Sierra and Canva published, and the one candidates describe at Shopify. I read the fluency rubric Zapier now applies to every single hire. Different companies, different stages, one converging move. **Stop grading the deliverable. Watch the candidate work.** The bar isn’t “knows AI.” It’s **“directs AI and catches it when it’s wrong.”**\n\nHere’s the whole arc in one picture.\n\n**The diagnosis.** I’ll show you why every stage of your current process stopped measuring anything real.**The rebuild.** Five moves, one per stage, with full requirements quoted from live postings and the interview questions from companies that already made them.**The calibration.** You’ll see the data on how much AI to actually demand, and the walk-back that warns against overshooting.**The walkaway.** One forwardable paragraph for leadership and your recruiter, plus a checklist to run on the req you have open.\n\nBy the end you’ll have a full replacement for the old process: job-description language you can borrow whole, two screening questions, a live interview format with a published AI policy, a work-sample design, and a decision rubric, all compressed into a closing checklist you can run this week. **Your next PM hire stops being a coin flip on polish and becomes the clearest AI-first signal you send your team this year.**\n\nLet’s rebuild it, stage by stage.\n\n## Why your hiring loop went blind\n\nWalk through the classic stages and name what each one actually measures. The résumé screen grades a document. The case interview grades a rehearsed structure, the kind a decade of candidates drilled from [Cracking the PM Interview](https://www.goodreads.com/en/book/show/19243347-cracking-the-pm-interview), McDowell and Bavaro’s 2013 prep book (”How many pizzas are delivered in Manhattan?”, “How do you design an alarm clock for the blind?”). The take-home grades another document, produced somewhere you can’t see. **The panel grades a performance, and every stage grades some kind of output.**\n\nFor years we told ourselves polish correlated with skill, that producing a crisp PRD required a crisp mind. The honest version is less flattering. Selection studies have long ranked résumé screens and unstructured interviews among the weakest predictors of actual job performance. **The old process wasn’t a good instrument that AI broke. It was a weak instrument whose weakness AI made undeniable.**\n\nPicture the machine you’ve been running.\n\nThere’s a famous precedent for this kind of audit. Back in 2013, Google ran the numbers on its own famously clever interviews. [Laszlo Bock](https://abcnews.go.com/blogs/business/2013/06/google-skips-waste-of-time-brainteaser-interview-questions), who ran People Operations there, went public with the verdict: **brainteasers were “a complete waste of time” that “serve primarily to make the interviewer feel smart.”** Google moved to structured interviews as a result. The lesson stings a little: a signal-free process can feel rigorous for years, because nobody checks. **AI didn’t create the weakness. It removed the excuse for ignoring it.**\n\nYou can watch the collapse happen in real interviews. In [Nikhyl Singhal’s The Skip](https://theskip.substack.com/p/what-pm-hiring-managers-actually) (Singhal ran product at Meta and Google), hiring manager Sam Stone observes that **candidates who used AI to prep their case structure “will abandon the structure the moment Q&A starts, because they never internalized it.”** In the same piece, product leader Mckenzie Lock shares the probe she used when screening at Netflix: when new information invalidates an assumption, “can you say ‘oh, that changes things’ and walk through updated logic?” And Meta gives the same diagnosis from the company side. Its new interview format puts an AI assistant inside the session, partly because, in [Meta’s own words](https://www.hellointerview.com/blog/meta-ai-enabled-coding), it “makes LLM-based cheating less effective.” **The companies that moved first aren’t fighting AI-polished candidates with detection tools. They moved the test to where polish can’t fake it.**\n\nMeanwhile the requirement itself went up, not down. In Microsoft and LinkedIn’s [Work Trend Index](https://news.microsoft.com/source/2024/05/08/microsoft-and-linkedin-release-the-2024-work-trend-index-on-the-state-of-ai-at-work/), a survey of 31,000 people, **66% of leaders said they wouldn’t hire someone without AI skills.** That survey is from May 2024, and nothing published since suggests the number softened. So you’re expected to hire for a skill your instruments can’t see, using stages that stopped measuring anything. **The fix isn’t a better lie detector. It’s a process that watches work instead of grading output.** Five stages, five moves.\n\n## Rebuild the loop stage by stage\n\nEverything below comes from choices real companies have published, plus my own read on how to adapt them to a PM seat. **Treat the five moves as parts to adapt, not a recipe to obey.** Your org, your seat, your constraints. Run the ones that fit.\n\nHere’s the full map before we go stage by stage.\n\n### Write AI into the job description\n\nStop writing “familiarity with AI tools is a plus.” The most advanced companies on this front put AI usage in the requirements section and name tools, the way postings used to name SQL. **The strongest reqs treat AI usage as a requirement with teeth, not a vibe.** All of these lines come from live postings I pulled off the companies’ own job boards this week, quoted in full so you can borrow whole requirements, not fragments:\n\n(PM, Workspaces, an everyday workspace-product seat): “AI-pilled: You have comfort and an ongoing curiosity with using AI tools for your product development process.” And on every one of its 141 live postings, Notion adds: “You don’t need deep AI expertise for every role, but we do expect every Notino to be intellectually curious, drawn to tinkering and discovery, and excited to use AI as a real collaborator in their work.”[Notion](https://jobs.ashbyhq.com/notion/35785e61-c4c3-44ec-a401-6741d89dd16a)(PM, Interfaces; the line recurs across their PM postings): “You use AI to move faster. You use AI tools to compress the slow parts of the job (research, drafting, synthesizing feedback) so you spend more time on judgment calls, and you’ve built enough with agents to have real opinions on what makes an interface agent-friendly.”[Supabase](https://jobs.ashbyhq.com/supabase/184c3ad5-5d5f-44a1-98f7-65ff6f287b2f)(PM, Incubations, a consumer seat asking for 10+ years of experience): “Demonstrated curiosity and point of view on incorporating AI tools into the product workflow.”[Airbnb](https://careers.airbnb.com/positions/8044715?gh_jid=8044715)(PM for Pexels, their stock-photo product): “We’re looking for an AI-embracing entrepreneurial type product manager.” And in the experience list: “You embrace AI for automation, prototyping, report generation, data analysis, etc.”[Canva](https://jobs.generalcatalyst.com/companies/canva/jobs/82275771-pexels-product-manager)(growth PM, in Minimum Requirements): “Experience building AI-powered, conversational, or agentic products. You understand what it takes to ship AI that works reliably for real users, not just in demos.” And under preferred qualifications: “You have shipped products on top of these technologies, not just evaluated them.”[Stripe](https://stripe.com/jobs/search?gh_jid=7809139)(PM, Agentic CX): “We’re looking for builders who can vibe-code a prototype before lunch and write the rollout plan after.” The requirements spell it out: “Deep hands-on experience building with AI: you’ve prototyped, shipped, and iterated on AI tools, agents, or workflows, not just managed roadmaps about them. Technical fluency with modern AI coding harnesses (Cursor, Claude Code, Codex).” And: “Fluency in data and AI evals: you know when to trust numbers and when to lean on principles.”[Ramp](https://jobs.ashbyhq.com/ramp/a3afd259-ba6b-4eb0-a1b6-05d01dddacd8)(research PM, in Minimum Qualifications, in full): “Have a deep passion and curiosity for AI and LLMs. Use AI regularly.”[Anthropic](https://job-boards.greenhouse.io/anthropic/jobs/5247407008)\n\nOne distinction before you borrow. At Anthropic or on Ramp’s agentic products, asking for AI depth is as natural as a payments seat asking for payments experience: the product is AI, so the requirement writes itself. The lines worth copying are the other kind: a workspace product, a home-rental team, and a stock-photo library writing AI usage into ordinary PM seats. **That’s the expectation spreading beyond AI products,** and it’s the part that applies to your posting whatever you build.\n\nNow decide which ask you’re actually making. The postings above split into three distinct tiers, and mixing them up is how job descriptions turn into wish lists.\n\n**Tier one asks for a PM who works faster with AI:** the Notion, Supabase, and Airbnb language. **Tier two asks for a PM who prototypes with AI coding tools by name:** the Ramp language, echoed in [Anthropic’s Labs PM posting](https://job-boards.greenhouse.io/anthropic/jobs/5096878008) (”Prototype with AI tools like Claude Code”). **Tier three asks for a PM who owns evals**, the tests that measure whether an AI feature actually works: Anthropic’s [Claude Code PM posting](https://job-boards.greenhouse.io/anthropic/jobs/5247640008) wants someone who has “personally built agentic evals,” and [Figma](https://boards.greenhouse.io/figma/jobs/6100482004) and [Databricks](https://databricks.com/company/careers/open-positions/job?gh_jid=8560772002) write eval ownership straight into PM responsibilities. **Most seats need tier one or two, and writing all three into one req doesn’t get you a unicorn, it gets you an empty pipeline.** Pick the tier, write two or three specific bullets, and move on. The req is now making a promise the rest of your process has to keep.\n\n### Screen for their last real AI workflow\n\nThe old screening question (”are you familiar with AI tools?”) is answerable by anyone with a pulse and a ChatGPT subscription. Replace it with two questions that can’t be prepped.\n\nFirst:\n\n**“Walk me through the last thing you used AI for, end to end.”** Second:\n\n**“When has AI been confidently wrong for you, and how did you catch it?”**\n\nBoth were published by [Metaview](https://www.metaview.ai/resources/blog/ai-fluency-rubric-interview-skills-framework), a recruiting-software company, building on the fluency rubric Zapier’s co-founder Wade Foster made public. Vendor source, but the questions stand on their own. **Ten minutes with these two questions tells you more than an hour of tool-name bingo.**\n\nThe second question is the real detector. Anyone who genuinely works with AI has been burned by it: the hallucinated citation, the confidently wrong data pull, the beautiful answer built on a misread premise. They’ll tell you the story with texture and a little embarrassment, and then, crucially, they’ll tell you **what they changed about how they verify.** A candidate who has only read about AI gives you abstractions about “hallucination risks.” **Real usage leaves scar tissue, and scar tissue can’t be improvised in a screening call.** The question is kind, too: you’re inviting a story they’ll enjoy telling, not running a quiz.\n\nZapier, the most codified case I found, is worth mining beyond the two questions. It checks AI fluency at four fixed points for every hire in every role (”The application, Initial (human or AI) screen, Skills test, Executive interview”) and probes four components: “**AI mindset, strategy, building, and accountability.”** The detail worth stealing came in [the rubric’s second version](https://zapier.com/blog/raising-ai-fluency-bar-in-hiring/): they now assess **“AI fluency slope, not a snapshot,” the candidate’s trajectory rather than today’s toolkit.** Listen for the slope in the workflow answer: someone whose story ends “and last month I changed how I do it” is climbing; someone reciting a setup from a year ago has plateaued. Metaview’s read of the same rubric describes what the top of the scale sounds like: **someone who “redesigns the work around AI” and “builds systems, prompts, or agents that other people use.”**\n\nYou don’t need four checkpoints. **You need these two questions asked early enough that nobody spends three weeks interviewing someone who fails the bar in minute ten.** Your recruiter can ask them word for word, which is exactly the point. The candidates who clear them earn the round where things get interesting: the one with AI in the room.\n\n### Put AI in the interview room\n\nNext decision: **what happens when a candidate wants to use AI in your interviews?** Right now most teams have no answer, which is the worst answer. Your interviewers assume it’s banned, your candidates assume it’s allowed, and whatever happens tells you nothing either way. The companies that took a position landed on three different doors, and all three work. **The only wrong policy is not having one, and not telling candidates before they walk in.**\n\n**Door one: require it.**[Canva insists](https://www.canva.dev/blog/engineering/yes-you-can-use-ai-in-our-interviews/)that engineering candidates use AI tools in its technical interviews, because the old process “asked candidates to solve coding problems without the very tools they’d use on the job.”**Door two:** allow it and score it. Shopify runs two AI interviews, and as candidates[describe them](https://www.hellointerview.com/blog/shopify-ai-enabled-coding), “Any AI tool is fair game. Cursor, Claude Code, ChatGPT, Copilot, whatever you normally use.”**Door three:** keep the live round unassisted.[Anthropic publishes its candidate policy:](https://www.anthropic.com/candidate-ai-guidance)draft your application yourself and refine it with Claude, but the live interview is you alone.\n\nThese examples all come from engineering hiring, and that’s worth being honest about. **The policy decision transfers to a PM loop as-is; the exercise formats need adapting, and the work-sample stage does that adapting.**\n\n**Once AI is in the room, score direction, not output.** One Shopify interviewer, in those same candidate reports, put the rubric in one line: “We don’t want the AI to work for you. We want you to tell it what to do.” Canva’s own interviewer guide tells candidates to [”take time to review and understand everything that gets generated”](https://www.canva.dev/blog/engineering/ai-interview-success/). And [Hello Interview](https://www.hellointerview.com/learn/ai-coding/overview/introduction), the site documenting these formats, states the stakes plainly: “The moment it starts making the architectural calls while you sit and watch, you’ve given away the exact thing the interview is there to assess.” One Rippling candidate was rejected for having “relied too heavily on AI even though their initial approach was correct.” **Over-delegation is a scoring axis now, not just under-use.** Watching someone accept AI garbage without blinking is the most informative thirty seconds in the whole process.\n\nFor the conversational rounds, the questions change too. [Aakash Gupta](https://aakashgupta.medium.com/i-coached-47-people-into-ai-pm-roles-at-300k-here-are-the-exact-questions-they-got-asked-36e239182010), who coached dozens of PMs through these rounds and collected the exact questions they got asked, reports prompts like: “Your chatbot’s response quality dropped 15% last week. How do you debug this?”, “Tell me about a time you shipped an AI feature that backfired,” and “Explain embeddings to a product designer on your team.” **Notice what these test: judgment about systems that fail probabilistically, not feature-design theater.** Lift the ones that fit your product and retire a case study to make room.\n\n### Swap the take-home for a live build\n\nThe take-home is the most broken stage in the old process, because it’s a pure artifact produced entirely off-camera. You’ll be tempted to keep it and bolt on AI-detection. Don’t. **Detection is a race you can’t win, and it aims at the wrong question:** you don’t actually care whether AI touched the deck, **you care whether this person can think.** So replace the stage entirely: watch the work happen live.\n\nThere’s solid science behind this swap, and it’s older than the AI wave. A live build is a work-sample test, an instrument that personnel-selection research has ranked among the strongest predictors of job performance for decades ([Schmidt and Hunter’s](https://psycnet.apa.org/record/1998-10661-006) 1998 meta-analysis of 85 years of hiring data; the [2021 revision by Sackett and colleagues](https://pubmed.ncbi.nlm.nih.gov/34968080/) puts structured interviews at the very top). You’re not chasing a trend. **You’re adopting the instrument hiring science recommended all along, now with AI in the room.**\n\nThe PM version already exists in the wild, usually as a 45-minute prototyping case. [Aakash Gupta has coached PMs through this round](https://www.news.aakashg.com/p/vibe-coding-interview) at “v0, Bolt, Figma, and Perplexity,” with real prompts like “Design Google Maps for the Blind” and “How would you prototype a Facebook Dating app,” and candidates choosing tools like Lovable, Bolt, or v0 for prototyping, or Cursor and Replit if they lean technical. [Lewis Lin](https://lewislin.substack.com/p/dear-lewis-how-do-i-survive-a-vibe), who has written PM interview prep books for a decade, describes what it actually measures: **“rapid problem-solving and product judgment under pressure,” not code quality.** One calibration note so you don’t over-index: Google has only piloted this round, and it’s [”not a standard part of Google’s process (yet)”](https://substack.com/@aakashgupta/note/c-136652951). Where it does run, [Jaclyn Konzelmann, Director of AI Product at Google, has described what she looks for](https://substack.com/@aakashgupta/note/c-171847295). You’re early, not late.\n\nIf you want a deeper shape to borrow, [Sierra published its redesigned onsite](https://sierra.ai/blog/the-ai-native-interview) in April 2026: Plan, a working session where **“the candidate drives ideation, while interviewers ask questions to strengthen it”**; Build, two hours with “the AI tooling and frameworks of their choice”; Review, where the team debates “the key product flows and choices they made.” It’s their engineering loop, so here’s my PM adaptation: **plan a feature together for twenty minutes, give them a time box and whatever tools they like, then spend the last half hour on why they built what they built.** What did they cut, what did they verify, what would they ship. **The prototype is just the excuse. The review conversation is where the judgment shows.**\n\n### Decide with a bar, not a ranking\n\nNow the debrief, where all of this either compounds or unravels. **The move: treat AI fluency as a minimum bar, and product judgment as the ranking.** You’re not hiring the most AI-enthusiastic candidate in the pool. **You’re hiring the best product judgment among everyone who cleared the bar.**\n\nMake the bar concrete, and borrow Zapier’s “Capable” level to do it: **AI embedded in their actual work, “repeatable systems, not one-off prompts,” and clear impact on quality or speed.** A candidate who can’t show a single real workflow in the screen, and who delegates every decision to the AI in the live build, doesn’t clear it. **That’s a no in 2026 the same way “can’t write a PRD” was a no in 2019.** But clearing the bar higher earns no extra points, because the ranking runs on something else.\n\nThe data backs the weighting. Across [592 AI-PM postings analyzed in early 2025](https://axialsearch.com/insights/ai-product-management-jobs/), product management and cross-functional collaboration each appeared in 32% of listings, while prompt engineering showed up in 9%. Even the roles with AI in the title rank on classic judgment. The sharpest new skill to probe for is evals. [Kevin Weil](https://www.lennysnewsletter.com/p/kevin-weil-open-ai), who was leading OpenAI’s product org at the time, said it plainly in April 2025: **“Writing evals is going to become a core skill for product managers.”** Honest calibration: eval experience is a hard requirement for AI-product seats (it’s literally in Anthropic’s and Figma’s postings) and a strong differentiator everywhere else. **Match the evals expectation to the tier you picked when you wrote the req, and don’t punish a tier-one candidate for missing a tier-three skill.**\n\nOne more upgrade, borrowed from decision science. In [Noise](https://www.hup.harvard.edu/books/9780316451406) (2021), Daniel Kahneman, Olivier Sibony, and Cass Sunstein show how wildly hiring judgments vary between judges, and prescribe the fix: **score each dimension independently, and delay the overall verdict until the end.** “Fluency is a bar, judgment is the ranking” is that protocol in one line. And in your judgment scoring, weight the moment Mckenzie Lock’s probe targets: did the candidate ever say “oh, that changes things” and re-reason live? **That moment can’t be faked, and it’s the single strongest signal your new process produces.** Which leaves one question: how hard should you actually push the AI requirement?\n\n## How much AI to actually demand\n\nHere’s the objection you might already be forming: “We’re not Ramp or Anthropic. Most PM postings don’t ask for any of this. My next PM still needs discovery, stakeholder work, and judgment. Isn’t rewriting the whole process premature?” The data honestly supports half of it. [Itamar Gilad’s analysis](https://itamargilad.com/ai-pm-future/) of TrueUp data found that **roughly one in seven open PM roles asked for AI skills as of early 2026,** and he’s blunt about the hype: “There’s no indication that all PM roles will require deep AI knowledge and skills.” I’ll add my own check: when I scanned OpenAI’s 13 live PM postings this week, most carried no AI-usage bullets at all. **At AI-native companies the expectation is ambient culture, not req language, and the explicit bullets cluster at companies making the shift deliberate.** If your org writes its expectations down rather than absorbing them by osmosis, the bullets above are your template.\n\nBut look at what the objection is actually objecting to. The rebuilt process isn’t a higher AI bar bolted onto every stage. **It’s better instruments for the things you already hire on.** Even your most classic hire, the discovery-and-stakeholders PM with no AI ambitions, now walks through a world where every résumé, case answer, and take-home can be AI-generated. The screen question, the live build, and the re-reasoning probe measure judgment better than the old stages did, whatever the seat. **Fix the instruments because they’re broken, not because every PM must become an AI specialist.**\n\nThe walk-backs teach the closing lesson. Duolingo went AI-first loudly in April 2025, and a year later its co-founder Luis von Ahn [dropped the idea of evaluating employees on AI usage](https://fortune.com/2026/04/13/duolingo-ceo-luis-von-ahn-ai-usage-requirement-employee-performance-evaluations/): **“the most important thing in your performance is that you are doing whatever your job is as well as possible.** A lot of times AI can help you with that. But if it can’t, I’m not going to force you to do that.” His mandate was about evaluating current employees, not hiring, but the lesson transfers to the bar you set at the door. **Don’t make AI zealotry the bar. Make judgment-with-AI the bar.** The process you just rebuilt does exactly that: fluency is one gate, and it’s never the ranking.\n\n## Start with the req on your desk\n\nWhen leadership or your recruiter asks what’s changing, here’s the paragraph to hand them:\n\nAI made polished artifacts free, so\n\nour hiring process can’t grade artifacts anymore.We’re redesigning it towatch how candidates work:the req names real AI usage, the screen asks for their last AI workflow end to end, the interview puts AI in the room with a policy we publish, the work sample is a live build we review together, and the decision holds a fluency bar while judgment keeps the casting vote. The bar isn’t “knows AI.”It’s “directs AI and catches it when it’s wrong.\n\nIt travels well: to leadership, to your recruiter, to the staff PM who’ll run half the interviews.\n\nA generation of hiring managers learned from [Who](https://www.goodreads.com/book/show/6361445), Geoff Smart and Randy Street’s 2008 hiring classic, to write the scorecard before meeting a single candidate. Same discipline here, one page shorter. The checklist, for the opening you already have:\n\n**Job description:** pick tier one, two, or three, and write it into the requirements as plainly as Anthropic’s “Use AI regularly.”**Screen:** the two questions, word for word, in the recruiter’s script.**Interview:** pick a policy (require, allow-and-score, or unassisted-live) and put it in the candidate prep email.**Work sample:** one prompt, their tools, 45 minutes, then thirty minutes on their choices.**Decision:** fluency is a bar, judgment is the ranking; one line in the debrief template.\n\nYour hiring process is also a message, and not just to candidates. The team you already have reads every posting you put up and every interview you run as a statement of what the org actually values. **When they see you watching how people work with AI instead of counting buzzwords, that tells them more about the AI-first mandate than any kickoff deck.** The first candidate through the rebuilt loop will show you more in 45 live minutes than the last ten take-homes combined. The posting on your desk is due Friday anyway. **Make it the first artifact of the new process.**", "url": "https://wpnews.pro/news/how-to-hire-an-ai-native-product-manager", "canonical_source": "https://www.theaithinker.com/p/how-to-hire-an-ai-native-product", "published_at": "2026-07-20 11:00:59+00:00", "updated_at": "2026-07-20 14:06:03.799841+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-products", "ai-tools", "ai-ethics"], "entities": ["Anthropic", "Ramp", "Notion", "Stripe", "Sierra", "Canva", "Shopify", "Zapier"], "alternates": {"html": "https://wpnews.pro/news/how-to-hire-an-ai-native-product-manager", "markdown": "https://wpnews.pro/news/how-to-hire-an-ai-native-product-manager.md", "text": "https://wpnews.pro/news/how-to-hire-an-ai-native-product-manager.txt", "jsonld": "https://wpnews.pro/news/how-to-hire-an-ai-native-product-manager.jsonld"}}