Recruiting Future with Matt Alder - What's Next For Talent Acquisition, HR & Hiring?

AI transformation is accelerating, and for many organizations, the biggest risk isn't the technology itself; it's getting their strategic response wrong. Rush in without a framework, and you can destroy culture, trust, and capability. Hold back waiting for certainty, and more agile competitors will overtake you. Talent leaders are caught between these two failure modes with no clear playbook, and the pressure is intensifying by the week. So what does a disciplined, structured approach to navigating AI disruption actually look like in practice, and what role should talent and HR be playing?

My guest this week is Jagrity Singh, a transformation leader who specializes in integrating AI-driven talent strategies with process excellence disciplines. In our conversation, she introduces a model for understanding where work sits between fully human and fully automated, and explains why the organizations that win will be those that learn to surf the wave rather than get crushed by it.

In the interview, we discuss:

Differences in AI approaches between Europe, the Middle East, and North America.

The impact of AI on jobs and what approach employers should be taking

AI is an HR problem, not an IT problem.

Why CHROs need to orchestrate human and AI workforces

Strategic workforce planning

What should be automated and what shouldn’t

Advice to talent leaders

What does the future look like?



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Recruiting has always been shaped by the time and resources available. Resumes are short because recruiters only have a finite amount of time to read them. Interview shortlists are small because hiring managers can only meet so many candidates. The whole funnel narrows because no team can fully evaluate everyone who applies. None of these are strategic choices, they're simply workarounds for human capacity.

Now AI agents can screen hundreds of candidates in a matter of hours, run outside business hours, and deliver structured evidence for recruiters to review. The data coming back is already challenging assumptions about how these processes should work, while the growing influence of AI on who progresses through the hiring process makes questions around ethics, fairness, and regulatory compliance impossible to ignore.

So how should TA leaders rearchitect their processes while keeping them responsible?

My guest this week is Sachit Kamat, Chief Product Officer at Eightfold. In our conversation, Sachit shares early data from AI interviewing at scale and explains why it's time to reimagine recruiting processes as the traditional constraints around time and resources start to fall away.

In the interview, we discuss:

Lifting capacity limitations in recruiting

The impact of AI interviewing on the candidate experience

What humans do better than technology

Radically improving the candidate experience.

Building agent scale processes

The first steps to transforming recruiting

Regulation and responsibility

Could the time to hire be reduced to less than 1 hour?

What does the future look like?

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Hiring should be about finding the right person. Too often, though, the tools and methods organizations use actually work against them. Job postings filter candidates out for lacking skills they could easily and quickly learn. Competency checklists based on someone else's philosophy of what leadership looks like rather than what actually works inside their organization. Assessment tools that aren't scientifically validated or that screen for average profiles when the role needs something entirely different.

The funnel narrows before employers even realize it. And when a poor fit does get through, the individual can spend months or years struggling against expectations that were never clearly defined.

So how should organizations rethink the way they assess and select talent?

My guest this week is Dr. Stephanie Puckett, founder of SynergyMind Consulting. In our conversation, she draws on 20 years of experience in organizational psychology to reveal where hiring processes quietly break down and the implications for both employers and employees when they do.

In the interview, we discuss:

The most common mistakes employers make in hiring

Unintentional restriction of talent pools

Skill and competency transfer

The danger of using tools with no scientific validation

The critical role of talent acquisition teams

Data science versus psychology

Finding confirmation bias in big datasets

The importance of realistic job previews

How will hiring develop in the next 2 to 3 years

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The pressure on talent functions right now is intense. Budgets are tight, teams are stretched, and the mandate to do more with less has pushed many organizations to automate at speed without stopping to redesign what they were automating.  These automated decisions are attracting real legal and regulatory attention. Actions previously seen as simple process steps are now potentially being viewed from a legal perspective as consequential decisions.

At the same time, there's a growing recognition that AI could be truly catalytic, forcing the kind of fundamental change that talent functions have needed for years. 

So how do leaders navigate the constraints while seizing that opportunity?

My guest this week is Kyle Lagunas, Founder of Kyle and Co. In our conversation, Kyle unpacks what defensibility really means in practice, why talent teams need to shift from risk avoidance to risk readiness, and how AI is catalyzing long-overdue transformation.

In the interview, we discuss:

Credibility under constraint

Risk averse or risk avoidance?

What does defensibility look like?

The AI balance between execution and judgement

Human-in-the-loop needs to be designed, not assumed.

Are we holding machines to a higher standard than we hold humans to?

The importance of rigour in pilot programs

Building AI literacy

What does the future look like?

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AI tools are changing the pace at which organizations filter and rank candidates. However, matching someone to a job description and actually predicting whether they'll perform well in the role are two very different things. Most hiring processes have never been validated against real performance outcomes, and organizations often don't have a clear, measurable definition of what success looks like in a role. Without that foundation, even the most sophisticated AI is just automating something that was never evidence-based in the first place. 

So what would it actually take to build hiring processes that genuinely predict performance?

My guest this week is Jennifer Yugo, Managing Director and owner of Corvirtus, and an organizational psychologist specializing in evidence-based hiring. In our conversation, she explains the science behind predicting job performance and why most hiring processes are far from where they need to be.

In the interview, we discuss:

Matching candidates vs predicting performance

Why most hiring lacks evidence

Defining what success really looks like and identifying performance indicators

Do some AI hiring tools stand up to scrutiny?

The risks of automating bad decisions

Questions TA leaders should ask vendors

Are we going to see a reckoning for hiring technology?

What might the future look like?

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