エピソード

  • Ep 810: End-to-End Hiring Intelligence

    Ep 810: End-to-End Hiring Intelligence
    2026/07/24
    AI has been applied to almost every step of the hiring process. Sourcing, screening, assessments, interviews; each has its own tools, and many of them are effective. For many organizations, though, the gains from optimizing individual stages are flattening out. Hiring quality is shaped by the entire journey, not by any single step, and most hiring technology was never built to connect those steps. The focus is shifting toward connecting the whole process so that each stage learns from the others and improves over time. So what does it take to move from optimizing separate steps to building connected intelligence across the hiring process? My guest this week is Ben Chino, Co-founder and CPO of Maki. In our conversation, Ben explains why improving hiring one step at a time has hit its limits, what end-to-end hiring intelligence looks like in practice, and what it means for recruiters and candidates. In the interview, we discuss: Why optimizing individual hiring steps with AI has hit diminishing returns The difference between a system of record and a system of intelligence How a connected hiring process improves decision-making at every stage Where the ATS fits in the next generation of hiring technology Why human judgment in hiring is less consistent than most people think Freeing recruiters for better judgment and more time with candidates Turning 800,000 applications into a real candidate experience Why adopting AI in hiring is an organizational change challenge, not a technology decision What does the future of hiring look like? https://www.linkedin.com/in/ben-chino/
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    24 分
  • Ep 809: The Data Foundation For AI In Hiring

    Ep 809: The Data Foundation For AI In Hiring
    2026/07/17
    Agentic AI is only as useful as the data it can access, and getting that foundation right is proving to be the harder half of the work. Years of mergers, acquisitions, and local decision-making have left many talent operations running on data and processes that were never meant to work together, and no amount of AI on top will fix what lies beneath. Some organizations are now rethinking their technology strategy in light of that problem. So what does getting AI-ready actually involve, and what does it change about the decisions you make? My guest this week is Lia Manafova, Talent Technology Strategy Lead at Sanofi, a global pharmaceutical company hiring at scale across more than 70 countries. In our conversation, Lia explains why the data foundation must come first, what an anchor product strategy looks like in practice, and what she has learned about making technology stick. In the interview, we discuss: Why AI readiness starts with data, not AI Building a bridge between the business and the digital team The challenge of constant transformation and change fatigue What is an anchor product strategy? How the Workday, Paradox and HiredScore acquisitions changed the options Best-of-breed or a single source of truth? Keeping recruiters in one system rather than three Piloting with the people who will use it every day The case for keeping the semi-automated option Building an ROI story the business understands What does the future look like?
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    30 分
  • Ep 808: The Starting Point For TA Innovation

    Ep 808: The Starting Point For TA Innovation
    2026/07/15
    Innovation in recruiting is hard. TA leaders are experts at running their operations, but improving them in a structured way is a different discipline, and the AI revolution has made it one that no one can avoid. Before any function can innovate, though, it has to know where it is starting from, and that is where benchmarking becomes critical. Recent research from the Recruiting Excellence Foundation, which has assessed the maturity of hundreds of TA teams worldwide, reveals that TA teams are struggling to move from operational to strategic. So how should TA leaders get started? My guest this week is Toni de Graaf, Co-Founder of the Recruiting Excellence Foundation. In our conversation, Tony shares what the global benchmark reveals, why so many teams are stuck in operational mode, and how to prioritize the improvements that matter most. In the interview, we discuss: Benchmarking TA maturity across the globe The most surprising insight from the results The two areas where TA teams struggle most The difference between an operational and a strategic function The impact of AI AI and broken processes Resource, capacity and value Knowing your starting point What does the future look like? Take the recruiting excellence assessment
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    29 分
  • Round Up June 2026

    Round Up June 2026
    2026/07/11
    If you've not listened to Round Up before, it's a short review of the episodes that I've published in the last month to make sure you don't miss out on the valuable insights that my guests are sharing. This month Round Up returns to its live format, and this is a recording of my live conversation with Ben Chino, Co-Founder and Chief Product Officer of Maki People, about five of the episodes published in May and June 2026 Episodes featured in this Round Up: Ep 790: Rethinking Work In The Age Of AI Ep 791: Making Agentic AI Work For HR & Talent Ep 797: Hiring The Humans Behind The Robots Ep 799: Growing the Talent You Can't Hire Ep 800: Will AI Break Recruiting? Follow this podcast on Apple Podcasts. Follow this podcast on Spotify.
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    30 分
  • Ep 807: Trust, Transparency And The AI Interview

    Ep 807: Trust, Transparency And The AI Interview
    2026/07/10
    Application volumes are climbing fast, and AI has made it far easier for candidates to produce a strong-looking resume. For talent teams trying to give every candidate a fair hearing, the traditional model of recruiting is starting to break down. Some employers are now handing the first conversation to an AI voice agent, and that raises some obvious concerns. Does automating the first step strip out the human connection that recruiting depends on? The teams doing this well are finding the answer isn't what a lot of recruiters expect, and that getting it right depends as much on how openly it's done as on the technology itself. So what does it look like in practice? My guests this week are Jean-Baptiste Anne, Global Director of Talent Acquisition at Mirakl, and Anneliese Muscari, their Head of AMER and Global Go To Market talent acquisition. In our conversation, they share why they made the change, how candidates have responded, and what it means for the future of the recruiter role In the interview, we discuss: Managing unprecedented application volume The growing limitations of resumes How do you give every applicant a fair chance and a great candidate experience? Handing the first conversation to an AI voice agent Moving from skepticism to trust Publishing AI guidelines for candidates How candidates have responded Keeping humans in charge of every decision What does the future look like? Follow this podcast on Apple Podcasts. Follow this podcast on Spotify.
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    31 分
  • Ep 806: The Real Trends In Talent Acquisition

    Ep 806: The Real Trends In Talent Acquisition
    2026/07/09
    Over the last year, I have been using AI to develop a searchable archive of the content in every episode of Recruiting Future, 3 million words from over a decade of unscripted conversations with practitioners and thought leaders across talent acquisition. James Whitelock, host of The Marketing Rules Podcast, has been doing the same thing with his own archive of more than 200 episodes over seven years. Between us, we now have over a thousand real conversations we can interrogate for trends, and the picture that emerges is an industry caught in familiar tensions: fighting to prove its strategic value, grappling with AI that moves faster than it can be adopted, and trying to figure out which parts of hiring should stay fundamentally human. So what do years of real conversations reveal about where talent acquisition actually stands? In my conversation with James, we compare what our respective archives reveal about TA's shifting identity, the real pace of AI's change, and the tensions the industry keeps returning to. In the interview, we discuss: Using AI to turn podcast archives into industry intelligence TA's journey from growth engine to cost center and back again How the AI conversation has shifted over time Adaptability as the critical skill for TA Is TA becoming a marketing function? Standing out when AI content all sounds the same Ring-fencing the parts of hiring that should stay human Bias in humans and bias in AI What does the future look like? Follow this podcast on Apple Podcasts. Follow this podcast on Spotify.
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    35 分
  • Ep 805: Hiring's Signal Problem

    Ep 805: Hiring's Signal Problem
    2026/07/06
    The talent market is sending mixed signals. Employers insist they can't find the people they need, while experienced, capable candidates say they are applying into a void and hearing nothing back. Both are describing the same market, so something in the middle is failing. A lot of recruiting technology was built to handle volume, to move large numbers of applicants through a process quickly. What it struggles to do is read signal, to interpret whether someone actually has the judgment and context to solve the problem a business has. So how do we fix this problem, and will AI give us the solution? My guest this week is James Gardner, a talent acquisition and transformation leader who has spent over twenty years building and scaling talent functions. In our conversation, he shares what his own data-driven job search revealed about the market, why volume systems and signal systems pull in opposite directions, and how AI could either fix the problem or make it considerably worse. In the interview, we discuss: What's really happening on both sides of the talent market Why the market isn't short of talent; it's short of signal. Running a job search as a data funnel Why silence, not rejection, is the real problem Why volume systems and signal systems contradict each other Where AI screening still can't read potential Applying AI to a broken process just makes it fail faster. Moving TA from a service function to a commercial lever Owning the outcome, not just the shortlist. What does the future look like? Follow this podcast on Apple Podcasts. Follow this podcast on Spotify
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    28 分
  • Ep 804: The Talent Pool Employers Need

    Ep 804: The Talent Pool Employers Need
    2026/07/02
    As AI reshapes how work gets done, the most valuable thing a person can bring to their job isn’t recent task experience; it is the depth of judgment, sector knowledge, and decision-making that takes years to build. That is precisely what AI augments rather than replaces. However, in a cautious hiring market, recency is being given overinflated importance, and a large pool of deeply experienced professionals is being filtered out because they have a gap on their resume. These are people with the experience and maturity, and strong appetite for engaging with new technology that the AI era needs. So why are employers overlooking this talent, and how should TA leaders rethink their hiring strategies to fix this My guest this week is Hazel Little, CEO of Career Returners. In our conversation, Hazel explains what the data reveals about the returner experience in 2026, why deep experience and judgment matter more than recency in an AI-augmented workplace, and shares some practical advice on making hiring more effective. In the interview, we discuss: How the landscape for career returners has worsened in the last year The unique benefits returners can bring to organizations. Why there is still so much stigma around career breaks and resume gaps How the hiring process amplifies the confidence gap The importance of potential over experience in a fast-changing world of work Why the human judgment needed to work with AI comes from experience The skills shortage hiding in plain sight Building potential rather than buying experience Screening and the hiring manager mindset What TA needs to do differently to harness this valuable talent pool. Follow this podcast on Apple Podcasts. Follow this podcast on Spotify.
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    29 分