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

Cybersecurity is one of the hardest markets to hire in, and a lot of the difficulty comes from how companies approach the process. Candidates are still being filtered on degrees, previous employers, and years of experience- signals that are particularly weak in a field where the best work is confidential, and skills need updating constantly. Good people are getting screened out, and what looks like a talent shortage can often be a matching problem.

So, what changes when companies stop filtering on the usual proxies and start hiring on what candidates can actually do?

My guest this week is Laurent Halimi, CEO and founder of Cyberr.ai, a professional network for cybersecurity professionals. In our conversation, Laurent explains why the skills shortage in cybersecurity is only half the truth, where the bias in hiring really comes from, and what it takes to judge candidates on skills that you can actually verify.

In the interview, we discuss:

How AI is changing what cybersecurity professionals do and the new roles it is creating

A real skill gap at the senior level and a matching problem everywhere else

The certification race and why it is losing relevance

Why hiring in cybersecurity is different from other technical fields

Trust, stale skills, and work that stays invisible

Where the bias in hiring algorithms really comes from

Practical steps for judging candidates on verifiable skills

Anonymous applications and giving candidates control of their profiles

Will skills finally beat pedigree, and what does the future look like?

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There is growing consensus that AI is eroding the experience pathways organizations depend on to develop human judgment. Entry-level roles are shrinking, hands-on work is being automated, and fewer people are building the experience their businesses need. This is a well-documented challenge. What remains far less clear is what the talent strategy response should look like. Most skills systems today are built to track whether someone can execute a task, not how well they make decisions under ambiguity. If judgment is becoming the most important capability in the organization, recruiting, learning, and assessment all need recalibrating around it.

So what does that recalibration look like in practice?

My guest this week is Craig Friedman, author of Enterprise Skills Unlocked and Talent Transformation Leader at St. Charles Consulting Group. In our conversation, he explains why current skills infrastructure misses the capability that matters most, what work looks like when execution is automated, and how organizations can start building judgment into their talent strategy.

In the interview, we discuss:

What does enterprise AI adoption currently look like

The long-term consequences of cutting entry-level jobs

What does work look like when the automatable tasks are taken away?

Why judgment is more important as a skill than ever before

The difference between judgment and critical thinking

Where does judgment fit into the skills infrastructure?

How can TA teams hire for judgment?

What does the future look like?

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AI adoption is accelerating, but for most organisations it is still ad hoc. Tools are bought reactively, budgets keep growing, and few leaders can say with confidence what is being used, what is working, or what return they are getting. Some companies are already going further, cutting roles on the assumption that machines can simply take over tasks from people. What rarely gets examined is what those decisions do to the people who remain, the culture they work in, and the customers they serve.

So what do organisations need to understand about how work happens before they let AI reshape it?

My guest this week is Sam Naficy, Chairman and CEO of Prodoscore, a data analytics company that studies employee engagement, productivity, and collaboration in large enterprises. In our conversation, Sam shares what his data reveals about AI use and productivity, the cultural consequences of automating roles, and why visibility into work has to come before automation.

In the interview, we discuss:

Why most organisations are still in the early stages of AI adoption

The growing gap between AI spending and visibility into what is being used

What the data shows about heavy AI users and productivity

Why some employees adopt AI faster than others

The unforeseen impact of automation on workplace culture

What happens to the teams that remain when their AI replaces their colleagues

Stress-testing workforce changes before making them

The line between visibility and surveillance

What does the future of work look like?

Storytelling might be the most overused word in employer branding. Every company claims to tell stories, yet most of the content being produced sounds the same from one company to the next, and AI is about to make the problem significantly worse by flooding every channel with even more identikit messaging.

At the same time, the science behind how stories work on the human brain is well established, comprehensive, and much harder to apply than most people realise. The employers who understand it are creating content that connects on an emotional level, while everyone else is just adding to the noise.

So what separates real storytelling from content that just claims to be a story?

My guest this week is Susanna Rantanen, founder of Employer Branding Agency Emine and author of the book Story-Driven Employer Branding. In our conversation, she shares why so much storytelling isn't storytelling at all, what the science tells us about how stories persuade, and how employers can use them to attract and retain the right people.

In the interview, we discuss:

The biggest current challenges in Employer Branding

Why branding is a long-term play, not a short-term campaign

The internal audience employers overlook

Attracting the right people via meaning, relevance, and emotion

Why most Employer Brand storytelling isn't storytelling at all

The science of how stories work on the human brain

How does proper storytelling work in practice

The current and future impact of AI

What does the future of employer branding look like?

AI has disrupted the dynamics of hiring. Candidates can now apply to hundreds of roles in a single click, while legal and regulatory constraints limit how far recruiters can use AI to review what comes in. The result is a flood of applications that keeps growing because so little of it gets properly reviewed. With the inbound recruiting channel under this much pressure, sourcing offers a different equation; it's a channel where employers set the quality bar themselves, and AI has fundamentally changed what it can deliver. The implications reach beyond pipeline quality. New approaches to sourcing could transform the candidate experience and open career paths that traditional hiring would never have surfaced.

So what does it take to make sourcing a genuine strategic priority?

My guest this week is David Paffenholz, Co-Founder and CEO of Juicebox. In our conversation David explains what is driving the inbound challenges, how AI is transforming what sourcing can do, why every role could get the executive search treatment, and what this could all mean for candidates and their careers.

In the interview, we discuss:

The paradox of record application volumes and persistent skill shortages

Why candidates are using AI more effectively than employers

How bulk applying is creating a self-reinforcing loop that is breaking inbound

The business case for making sourcing a strategic priority

Exponentially improving the candidate experience

Treating every role like an executive search

Human judgment and the changing role of the recruiter

How skills-based matching could unlock career mobility and evidence that it is already happening

What does the future look like?

Juicebox

https://juicebox.ai/

David on LinkedIn

https://www.linkedin.com/in/david-paffenholz/