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

This is episode two of Decoding the C-Suite, a five part miniseries from Recruiting Future about how leadership really thinks about AI, recorded with the leadership team at SmartRecruiters seven months into their acquisition by SAP.

The headlines about AI and the workforce are relentlessly grim: surveillance, layoffs, roles disappearing. Lehua Stonebraker, who leads the people function at SmartRecruiters, starts from a different place: naming the fear rather than managing around it. In this episode she explains how to build psychological safety when employees are scared to admit they use AI, why she believes the org chart is gone, how engagement went up during an acquisition, and a vision of a connected employee lifecycle that finally uses everything an organisation learns about its people.

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:

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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:

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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:

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:

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/

For the last few years, most corporate conversations about AI have been about efficiency, automating tasks, and reducing headcount. That conversation is now starting to change, with more attention on what constant AI use is doing to the human capabilities organizations depend on.

Judgment sits at the center of this. It develops through years of hands-on experience, often in exactly the kind of junior work AI is now taking over. With many employers also hiring fewer entry-level people, developing the leaders of the future is becoming a serious challenge.

So how do organizations use AI to amplify human capability rather than erode it?

My guest this week is Johan Roos, Professor in Strategy at Hult International Business School and author of the new book Human Magic: Leading with Wisdom in an Age of Algorithms. In our conversation, Johan shares why the AI conversation is changing, the human capabilities that now matter most, and the practical choices employers face in developing judgment for the future.

In the interview, we discuss:

The dual mood of AI anxiety and excitement

• Moving the AI conversation beyond efficiency

• Amplification versus erosion of human capability

• The five human capabilities that matter most

• Why judgment comes from experience

• The risks of cutting entry-level hiring

• Thinking in tasks, not people

• Building an agentic workforce without cutting jobs

• Early warning indicators of talent risk

• Bionic collaboration and what the future looks like

https://itunes.apple.com/podcast/recruiting-future-podcast/id963756980?mt=2&ls=1

https://open.spotify.com/show/4u3Gl0l4pGBtIHOJZjLTrx?si=49641466567e44d6

https://www.linkedin.com/in/drjohanroos/

A full transcript will appear here shortly.

TA leaders are under real pressure to keep up with AI, new tools, and what they think other employers are doing. In that rush, a lot of functions are spending heavily on new technology without first understanding whether their existing processes are working, only to find the real problem was something no platform could have fixed. Getting this right means doing the internal work first, auditing what's already in place, and being honest about organizational readiness.

So what does it take to turn the lens inward before looking outward?

My guest this week is Jalpa Trivedi, an experienced Global Head of TA who has built and centralized TA functions across more than 30 countries. In our conversation, she shares why the internal audit matters more than the technology choice, how to build TA foundations that hold across different markets, and how to approach AI adoption with the governance it requires.

In the interview, we discuss:

-The 80/20 approach to centralizing TA across markets

-Building an EVP on evidence, not assumptions

-What persuades leadership to back a TA investment

-Why the biggest AI risk isn't falling behind

-Five data privacy questions to ask before signing with an AI vendor

-Where the dividing line falls between AI and human judgment

-What does the future look like?

A full transcript will appear here shortly.

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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/

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: