AI Job Matching Tools

How AI-ready are you as a Product Manager? 🤔 I built an AI Skills Assessment using Claude's new Skills feature and I'm sharing it free because....most of us have no idea where we actually stand with AI. When I started upskilling in AI, I spent months watching tutorials and taking courses without knowing what I actually needed to learn. This assessment would have saved me so much time. ✏️ Here's what it evaluates: → AI Fundamentals – Do you understand how these systems actually work? → AI Strategy – Can you identify where AI adds real value vs. hype? → Hands-on Building – Are you actually building with AI or just talking about it? → Data & Privacy – Do you understand the risks and ethical considerations? → Product Development – Can you integrate AI into your product workflow? → Economics – Do you know how to evaluate ROI and costs? → Learning Domain Specific – Are you applying AI to your specific domain? For me it's learning - but you can remix my artifact for your domain! You'll get: • Your current AI proficiency level • Specific gaps to address • Personalized learning path Why this matters: The PMs who don't build AI fluency now will be managing products they don't understand in 6-12 months. The barrier to entry has never been lower—but the gap between dabbling and actually using AI strategically? That's growing fast. 👩🏻💻 Try it here: https://lnkd.in/ecZKdsrz You can do it section by section. You might be surprised. Drop a comment if you take it—I'd love to know what you think about it and what you learn!👇 #ProductManagement #AI #AISkills #AIPM #AITools

The AI Assessment Effect Candidates often tend to adjust their answers or behavior to match what they believe the “ideal candidate” profile looks like. A new study published earlier this month found that when candidates believe they’re being assessed by artificial intelligence, they emphasize analytical skills and downplay their intuitive and emotional skills. This so-called “AI assessment effect” stems from the widespread assumption that AI-based evaluations prioritize rational, data-driven attributes over human-centric abilities. Researchers warn that if job seekers tailor their behavior to what they think AI values, their true competencies and personalities may remain hidden, undermining the integrity of the recruitment process. In addition if most candidates assume AI favors analytical traits, the talent pipeline could become increasingly uniform, limiting diversity and reducing the variety of perspectives within organizations. The researchers recommend 1) Radical transparency: Don’t just disclose that AI is used in assessments—be explicit about what it evaluates. Clearly communicate that your AI values a range of traits, including creativity, emotional intelligence, and intuitive problem-solving. Share examples of successful candidates who excelled by showcasing these qualities. 2) Regular behavioral audits: Go beyond demographic bias checks. Look for patterns of behavioral adaptation: Are candidates’ responses becoming more homogeneous over time? Is there a noticeable shift toward analytical self-presentation at the expense of other valuable traits? 3) Hybrid assessment models: Combine AI and human judgment to ensure a more balanced and holistic evaluation of candidates. See research published in the June issue of the Proceedings of the National Academy of Arts and Sciences. https://lnkd.in/ebtD4HBd

I often hear statements that equate using data with fairness. In practice, this is quite a bit more complicated because there is data, and there is good data. Imagine that we are predicting the success of individuals in doing cooking tasks. We give them all an opportunity to cook in a kitchen and measure their speed and accuracy in the food prep. Sounds fair, right? On further examination, let’s say the kitchen is built for people who are around 5½ feet (168 cm) tall with no disabilities. We use that same kitchen to test the success of everyone: average-height people, those who are 4 feet tall, those who are 7 feet tall, and wheelchair users. People who do not “fit” the kitchen show incredible ingenuity to accomplish their task, their success requires extra time and effort. Very few pass the test and are hired. Even fewer become master chefs. Over time, we have enough data to justify that being 5½ feet tall is a valid predictor of an aspiring chef’s success. Which it is, but only because the kitchen is built for these people. Should we continue hiring 5½-feet-tall chefs, allowing the automated screening to exclude applicants who don’t meet the height requirement because it is technically “predictive?” Or should we make kitchens more flexible and accessible to avoid automatically screening out a wider pool of applicants? #neurodiversity #HumanResources #careers #hiring #talent #screening #AI

If AI is part of the job, it should be part of the assessment! That’s the real signal in McKinsey’s latest hiring pilot: candidates are expected to use AI, and are assessed on how they work with it: how they prompt, challenge, adapt, and apply judgment to AI output. This is where the line between AI-readiness and AI-fluency becomes real. Experimenting with AI isn’t enough anymore. Hiring now means testing whether people can use AI critically, contextually, and responsibly...not just generate answers. The companies that win won’t just add AI to the workflow. They’ll hire for AI-fluency across roles and seniority, and back it with skills-based assessment that reflects how work actually gets done.

Every month, I challenge myself to build one small solution using my own knowledge and skills, something that can help improve the way I work or benefit others around me. This month, I created an AI-powered app to test my own AI fluency and Artificial Intelligence knowledge based on the learning standards used in my organization. The questions are generated by AI using recent developments reported by global consultants, research institutions, and industry bodies, ensuring the assessment reflects what is actually happening in today’s AI landscape. I also designed five levels of AI fluency that resonate with how professionals can grow in an organization: L1 – Awareness: Understanding available AI tools. L2 – Productivity: Using AI tools to improve daily work. L3 – Process Transformation: Converting existing workflows into AI-assisted processes. L4 – AI Builder: Creating simple AI tools such as AI agents, vibe coding. L5 – AI Innovator: Building solutions that transform processes and deliver business value. After the assessment, the AI also recommends relevant training programs from the company’s learning catalog to close knowledge gaps. For me, AI leadership begins with continuous experimentation, learning, and improving AI fluency.

AI literacy is a critical skill. Most companies are asking for it. But almost no one is measuring it. Some ask candidates to write prompts on paper. Others throw in a vague “AI task” without structure or feedback. That’s not assessment. That’s chaos. With genAssess, I’ve built a purpose-built system to infer and measure AI prompting capability — using real tasks, real tools, and real feedback. It’s not about “pass/fail.” It’s about understanding how someone actually works with AI. And trust me — the gap between assumed skill and actual performance is eye-opening.

Built an AI-powered onboarding assessment tool you can use for free. It isn't like a typical AI, you've chatted to. I've configured this one to be extra sassy. How it works: 1. You paste any knowledge base (training materials, product docs, policies), it extracts the key knowledge areas, then an AI voice agent conducts a natural 5-minute conversational quiz with the new hire. 2. At the end of call, you get an instant scorecard with grades per area and personalised feedback. The voice bit uses ElevenLabs conversational AI which is surprisingly good at handling 'I don't know' gracefully and moving on (instead of getting stuck in a loop like most chatbots). (Warning: I’ve set up the agent to be fairly militant with not letting you get away with weaselly answers) No signup required. Paste content, start quiz, get results. Built it for onboarding assessment but reckon it works for any knowledge transfer verification... Most companies still do this manually (if at all). HR folk scheduling 30-minute sessions to quiz new hires on the employee handbook. Dave from compliance reading out policy questions like it's 1995. Meanwhile, the new starter's pretending to listen while secretly wondering if they can expense lunch… Let me know what you think in the comments. Give it a whirl (no API keys or sign-up required): https://lnkd.in/ekf2y2-5

𝟭.𝟰 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀. 𝗘𝘃𝗲𝗿𝘆 𝘆𝗲𝗮𝗿. 𝗝𝘂𝘀𝘁 𝗳𝗼𝗿 𝗚𝗖𝗖𝘀. That's the scale of the hiring problem we're solving. I've always been a tech founder at heart. Building ANSR and 1Wrk over the years, the problems that excite me most are the ones that seem impossible at first—where technology can fundamentally change how things work. GCC hiring-at-scale is one of those problems. Hiring managers in New York. Candidates in Bangalore. Recruiters coordinating across time zones. On Talent500 alone, we process up to 500,000 applications every month. The best talent gets lost in the noise. The wrong hires slip through. Conversations happen that never should have. The cost of a wrong hire is immense. So we built 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝘆𝗥𝗲𝘃𝗶𝗲𝘄. Candidates can fast-track their application through an AI-powered video interview—real-time screening, follow-up probes, skills analyzed on the spot. Hiring managers get video responses, transcripts, and competency grades 𝘣𝘦𝘧𝘰𝘳𝘦 scheduling a single call. We've been testing this in beta for months. The results: ➡️ 60% higher hiring conversions ➡️ 2.5x recruiter productivity ➡️ Hundreds of hours saved—from screening to selecting I'm very excited for what we are building here. Taking a massive, broken process and using technology to fix it at scale. PriorityReview is live now on 1Wrk by ANSR. 🚀 Lalit Charlene Vivek Ateet Latesh Shivam Monica Neil Monica Mukul Anand Sugata Ayush Gaurav Smitha Venkatesh Rana akhil

🚀 𝗛𝗼𝘄 𝗗𝗼𝗲𝘀 𝗮𝗻 𝗔𝗧𝗦 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗦𝗵𝗼𝗿𝘁𝗹𝗶𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗩 𝗔𝗺𝗼𝗻𝗴 𝗧𝗵𝗼𝘂𝘀𝗮𝗻𝗱𝘀 𝗼𝗳 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀? Many job seekers get frustrated when they realize their 𝗖𝗩 𝗻𝗲𝘃𝗲𝗿 𝗲𝘃𝗲𝗻 𝗿𝗲𝗮𝗰𝗵𝗲𝘀 𝘁𝗵𝗲 𝗵𝗶𝗿𝗶𝗻𝗴 𝗺𝗮𝗻𝗮𝗴𝗲𝗿. That’s because 𝗔𝗧𝗦 (𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝗻𝘁 𝗧𝗿𝗮𝗰𝗸𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺𝘀) filter thousands of resumes before a human ever sees them — and 𝟵𝟵% 𝗼𝗳 𝗰𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲𝘀 𝗱𝗼𝗻’𝘁 𝗿𝗲𝗮𝗹𝗹𝘆 𝗸𝗻𝗼𝘄 𝗵𝗼𝘄 𝘁𝗵𝗲𝘀𝗲 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘄𝗼𝗿𝗸. After months of research and testing with my mentees at CareerIreland, I discovered how 𝗪𝗼𝗿𝗸𝗱𝗮𝘆 𝗔𝗧𝗦 — one of the most used systems in Ireland — 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘀𝗰𝗼𝗿𝗲𝘀, 𝗿𝗮𝗻𝗸𝘀, 𝗮𝗻𝗱 𝗳𝗶𝗹𝘁𝗲𝗿𝘀 𝘆𝗼𝘂𝗿 𝗖𝗩. The results? 🎯 Candidates who applied these insights improved their shortlisting rate by up to 5x in just 4 weeks. ⚙️ 𝗦𝗼, 𝗛𝗼𝘄 𝗗𝗼𝗲𝘀 𝗮𝗻 𝗔𝗧𝗦 𝗙𝗶𝗹𝘁𝗲𝗿 & 𝗦𝗵𝗼𝗿𝘁𝗹𝗶𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗩? 1️⃣ 𝗪𝗲𝗶𝗴𝗵𝘁𝗲𝗱 𝗦𝗸𝗶𝗹𝗹 𝗣𝗿𝗼𝘅𝗶𝗺𝗶𝘁𝘆 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 The ATS assigns weights to each skill or keyword based on its position and density in your CV. 💡 𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: 👉Skills in the Professional Summary section get higher weight than those buried in “Experience.” 👉Repetition across sections boosts confidence in real expertise (vs. keyword stuffing). 👉A skill mentioned in recent roles carries more value than the same skill in older jobs. 2️⃣ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗞𝗲𝘆𝘄𝗼𝗿𝗱 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 Modern ATS (like Workday) doesn’t just scan keywords — it uses AI models (BERT-lite) to understand context. If the job asks for “Data Visualization using Power BI,” your CV can still score high with: 👉 “Developed dashboards using Power BI” 👉 “Created BI reports using Power BI and Tableau” 🧠 Use sentences that demonstrate application of a skill — not just list it. 3️⃣ 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗖𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻 𝗠𝗮𝘁𝗿𝗶𝘅 The ATS evaluates how your job titles correlate with your skills, using internal job ontologies built from thousands of hiring records. 💡 Example: 📌 If your title is Business Analyst, Workday expects skills like: Requirement Gathering, SQL, Excel, Stakeholder Management, Process Mapping ❌ But if your CV says “Business Analyst” + “React.js” + “Node.js”, it looks inconsistent — lowering your score. 4️⃣ 𝗖𝗮𝗿𝗲𝗲𝗿 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗶𝗻𝗴 ( This is very Imp) ATS AI looks for logical, upward movement in your career trajectory. 💡 Examples: “Junior Analyst → Analyst → Senior Analyst” = ✅ High score 𝗙𝗿𝗲𝗾𝘂𝗲𝗻𝘁 𝗷𝗼𝗯 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗼𝗿 𝘀𝗶𝗱𝗲𝘄𝗮𝘆𝘀 𝗺𝗼𝘃𝗲𝘀 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗽𝗿𝗼𝗺𝗼𝘁𝗶𝗼𝗻 = ⚠️ 𝗟𝗼𝘄𝗲𝗿 𝗿𝗮𝗻𝗸 Pls feel free to share this with someone who is actively seeking job and not getting interviews. If you’d like to access the full, detailed document (including real examples of ATS algorithms) in PDF format, simply comment “ATS Algorithm” below 👇 and fill out the short Google Form with your details.

Hiring teams today don’t struggle with finding candidates. They struggle with fairly evaluating them at scale. Too many resumes. Too little time. And screening ends up being inconsistent. Different interviewers. Different questions. Different judgments. Recently, tried OnScreen by HackerEarth, an AI powered interview platform. Went in expecting the usual: Rigid flows. Scripted questions. Keyword based evaluation. But what stood out was this: It did not behave like a checklist. It behaved like an interviewer. During responses, it picked up on how the problem was being approached, not just what was being said. It adjusted the direction of the interview in real time. And the follow up questions were contextual and relevant. It felt less like clearing a round and more like being evaluated on actual thinking. 🔹 Zero bias with consistent evaluation 🔹 Conversational, human like interaction 🔹 Adaptive depth with contextual follow ups 🔹 Clear end review with structured feedback 🔹 Ability to scale interviews without bottlenecks Why this matters: The biggest drop off in hiring happens during screening, where inconsistency filters out good candidates. This does not replace human interviews. But it ensures every candidate gets a fair, consistent, high quality first evaluation. If used right: ✔ Better shortlisting quality ✔ Faster hiring decisions ✔ Reduced interviewer load ✔ Improved candidate trust AI will not replace hiring teams. But it can fix the most broken layer of hiring, screening at scale. Big thanks to the team for letting me try this early, especially Vikas Aditya, Geetartha Kaustav G. and Zahra Khan.