Leslie Mason

A few weeks ago, a new US client asked me to find them a VP of Machine Learning on contingent terms. I said no. They were quite surprised. “You’re turning down the work?” “Not exactly.” I said. I was turning down the way they wanted to work. In my humble opinion, the approach to hiring Exec level AI/ML roles has to be fundamentally different… When you’re hiring a Chief AI Officer, VP Machine Learning, or Chief Data Officer, you’re not just filling a role. You’re more often than not making a bet-the-company decision. If you get it right, your product roadmap accelerates, your team scales properly, you attract better talent because of who the leader is. If you get it wrong, it could be 12-18 months of false starts, burned budget, and having to do the search again while your competitors move forward faster. That’s not a “send me 5 CVs and we’ll see” type situation. That’s a “let’s partner on this properly” type situation. When I work retained on exec roles, there’s a lot that changes: 👉 I’m not competing with 3-5 other recruiters who are all shotgunning the same candidates 👉 I can tell candidates “this company is serious, they’ve committed to this exclusive executive search” (which matters to exec candidates in my experience) 👉 I spend a solid amount of time understanding the role, the team dynamics, the actual problems they’re solving 👉 The client gets my full attention, not 20% of it split across multiple searches because I’m hedging my bets 👉 The client gets talent advisory up front including market mapping, compensation benchmarking and research intelligence on competitors and target candidates 👉 We work as partners. I occupy a true ‘Trusted Advisor’ status which if I’m honest is where I do my best work The client who asked for contingent terms was a Founder of a Series A VC backed startup in SF. He didn’t understand this yet. He thought retained meant “paying more for the same service.” It doesn’t. Retained means I’m accountable for solving the problem, not just presenting options. It means when a candidate asks me tough questions about the company, I have real answers because I’ve been properly briefed. It means the client commits to a proper process, not “we’ll interview when we get around to it.” For exec level roles, that commitment really matters. I’m not saying contingent doesn’t work. For mid-level or senior engineer type roles where the risk is lower and the candidate pool is larger, it’s fine. But for the roles that are business critical, when the perfect candidate is really hard to find, that demands a proper search and selection process. That, to me means retained and exclusive. Otherwise, we’re both wasting our time. Am I being too rigid here?

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One trend I’ve been closely watching is how global hiring patterns are changing. In the US and Europe, many companies are leaning towards contract-to-hire and gig models to stay agile. Meanwhile, in India, we are witnessing a rise in specialized tech hiring, especially in AI, cloud, and cybersecurity. This duality is fascinating: while the West is optimizing for cost and flexibility, India is building capability and depth. The bigger question: Will India just be the talent supplier or can we become the talent hub that drives innovation globally? What do you think? (Source: LinkedIn Global Talent Trends Report 2025 → LinkedIn Report) #GlobalHiring #Hiring #Workforce2025 #Economy #GlobalEconomy #RecruitmentStrategy #LeadershipTalks #ThomasChandy #HRTransformation

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With more resumes coming from AI, they are increasingly less valuable signals. The 2026 Hiring Report says 40% of hiring teams are actively moving away from resume-first hiring. There is no 𝘯𝘦𝘸 way, but you have to find better ways to get a sense of candidate work examples, real scenarios, and judgment. (2026 Hiring Trends ->https://buff.ly/relduCj)

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A lot of people think the risk starts when layoffs hit. It doesn’t. The real risk is staying too long in a role where your skills are quietly losing relevance in the new economy. By the time layoffs happen, the damage is already done. You’re competing with people who updated their skills, signals, and positioning years earlier, not weeks earlier. This market doesn’t punish effort. It punishes stagnation. You can be good at your job and still be drifting toward irrelevance if: your tools haven’t changed your scope hasn’t expanded your work no longer maps to where companies are investing That’s why waiting for a “trigger” moment is dangerous. The transition is slower and less visible than layoffs, but far more damaging when it catches up to you. The smartest moves right now are defensive, not dramatic. Adjust before you’re forced to. Reposition before urgency sets in. Stay relevant before the market decides for you. 👉 https://lnkd.in/gn8fN5sF

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Hiring is getting harder and according to business leaders, AI is adding pressure with 65% reporting hiring challenges from AI-generated applications. See the top ways that AI-generated applications are complicating hiring in the 2026 Demand for Skilled Talent report by #RobertHalf: https://lnkd.in/d8PwFznN #HiringTrends #MachineLearning

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In 2025, hesitation cost companies more than bad hires. Many tech leaders spent the year chasing 100% “perfect-fit” candidates. Meanwhile, faster movers snapped up talent, shipped projects, and kept momentum. The U.S. tech market is entering 2026 with a new reality: precision hiring matters — but so does pace. The lesson? Clarity and capability beat caution every time. Read it here: https://lnkd.in/eJsc3Q3s

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