Ask the Analyst: What must HR leaders prioritize when building an early career pipeline ready for the AI reality?

Each week for The Briefing, UNLEASH’s weekly intelligence newsletter for senior decision-makers, we put an important question to the true HR experts: Our community of analysts.

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This week we asked them: What must HR leaders prioritize when building an early career pipeline ready for the AI reality?

Here are their perspectives.

Cheney Hamilton, Research Director, Bloor Research International

Boards are making a short-sighted mistake and early careers leaders know it.

AI, falling birth rates and nearly one million young people NEET represent a triple threat to the future workforce. Yet most boards are treating AI as a cost-saving tool rather than a reason to increase investment in early careers.

Cut the bottom rung today, lose your middle management layer in five years. The pipeline doesn’t grow back quickly. No matter how capable AI becomes, it will always need humans in the loop.

Our research is clear on what those humans need: creative thinking, critical thinking, emotional intelligence and leadership, the four capabilities that become more valuable as AI absorbs routine work. These can't be credentialled into existence. They have to be developed through experience, over time, from the ground up.

The organizations already responding are taking two routes: cross-organisational ecosystems that expose early talent to different systems and constraints across employers, or deep multi-functional pathways within a single organisation for those not yet ready to specialise. Both build something AI cannot replicate: adaptable humans who understand how work actually connects.

The organizations that win won't be the ones who cut fastest. They'll be the ones who kept humans growing alongside AI and had the courage to tell their boards why that mattered.

Jean-Baptiste Audrerie, HR Tech Industry Analyst, NexaRH

Basic repetitive tasks are disappearing, consumed by AI agents. Entry-level jobs are eroding faster with every new model release. Meanwhile, Lightcast data reveals that only 6% of AI workers hold AI-related degrees. The pipeline will not be fed by credentials alone.

HR leaders must prioritize four shifts:

1. Implement skill-based hiring. Move beyond degrees to identify emergent AI skills in external and internal candidates. Scrutinize what people have actually experienced and built recently, on their own. Many have crafted agentic workflows and automated report generators from their living room, not the office. They followed YouTube tutorials, OpenAI documentation, and Microsoft Copilot micro-learning, not an MBA. Shadow AI is a talent signal, not just a compliance risk.

2. Identify and activate internal AI-ready talent. Find the geeks willing to contribute to AI projects and gigs. They know far more than their resume reveals. These are the people who will update documents, redesign workflows, and maintain knowledge systems. Roles like AI Agent Orchestrator have no school; they emerge from practice.

3. Reinforce social pairing and mentoring. Early career employees are over-focusing on screens, relying on AI assistants for instant, complaisant Q&A. Knowledge may be immense and personalized, but assimilation happens through social experience and validation, not isolated prompting.

4. Clarify the human-AI boundary in every role. Some tasks run on autopilot, others are irreducibly human: judgment, context reading, emotional alignment, political navigation. In job descriptions, learning objectives, and check-in conversations, make explicit what AI cannot do. This demystifies how work is far more intertwined with tacit relational skills than with explicit, programmable tasks.

Kaelyn Lowmaster, Director, Research, Gartner

HR leaders must redesign early career pipelines around skills-based advancement rather than experience-based progression.

Gartner research predicts AI will create more jobs that it eliminates starting in 2028 while fundamentally transforming millions more. This will disrupt existing pathways for building experience. This is especially true for early career talent.

As AI augments traditional “entry-level work,” newer employees will be starting with more complex tasks, and will face a steeper learning curve with AI-enabled work. Right now, only 20% of executives believe their workforce is AI-ready and just 27% have a comprehensive AI strategy. As a result, early talent development has become a critical lever for closing capability gaps.

Gen Z brings strong digital fluency: They use AI more than any other generation at work. But they also bring skepticism and anxiety: 34% of Gen Z fear that AI will make their skills obsolete. HR leaders have an opportunity now to both build their future talent pipelines and address the concerns of their most AI-savvy employees.

By prioritizing accelerated, structured learning, psychological safety, and transparent AI communication, HR leaders can ensure early-career employees build adaptable, AI-relevant skills.

Tami Nutt, Systems, People & Workforce Strategist

When building an early-career pipeline ready for the AI reality, HR leaders must continue investing in people. That may seem obvious to some and counterintuitive to others, but it's the conclusion I keep coming back to.

It's also the piece missing from many of the AI briefings, announcements, and strategies I've heard over the past two years. A lot of organizations are looking at AI and seeing opportunities to automate work, improve efficiency, and reduce headcount. Some of those opportunities are real. But if the entry-level work goes away, where do future experts come from? Where do future managers come from? Where do future leaders come from? And who will be prepared to solve the challenges we haven't even encountered yet?

For decades, people learned by doing. They started with smaller tasks, worked alongside more experienced colleagues, made mistakes, asked questions, and gradually developed judgment. AI can help with the work, but it cannot replace the development.

IBM is one organization that seems to understand this. After more than a century of navigating technological and industrial change, the company continues to invest in early-career talent and AI skill development. IBM Chief Human Resources Officer Nickle LaMoreaux put it this way: "If we don't continue to invest in entry-level hires, what happens in 3–5 years? There's no pipeline; the well simply dries up."

That idea extends beyond hiring. IBM is also investing in programs that help university students and emerging developers build AI skills before they enter the workforce. That's a different mindset. It's easy to view people as a cost, and costs should be minimized. Investments are different, because you expect them to grow in value over time.

The organizations that continue investing in early-career talent are building the people who will lead teams, solve problems, understand customers, and drive innovation five and ten years from now. Those organizations will be far better positioned for whatever comes next.

David Green, Co-Founder & Managing Partner, Insight222

It seems that many organizations are at risk of automating away the very roles that create their future capability. Early-career jobs are often treated as low-cost execution: research, drafting, coding fixes, data cleaning, scheduling. Yet these tasks are also where people learn judgement, context, resilience and how work really works.

As Amy Edmondson and Tomas Chamorro-Premuzic warn in their HBR article, The Perils of Using AI to Replace Entry-Level Jobs, “stripping out entry-level jobs severs this pipeline.”

HR leaders must therefore resist the temptation to define AI readiness as simply teaching graduates to use tools. The priority is to redesign early-career work so AI removes unnecessary friction while preserving the developmental stretch that builds expertise.

That means giving young talent exposure to real problems, human relationships, ambiguity, feedback and responsible experimentation. AI can draft the report; the graduate still needs to understand whether the argument is sound. AI can surface candidates; the recruiter still needs to build trust.

As Edmondson and Chamorro-Premuzic put it, “AI is only useful when paired with critical thinking.” The winning early-career pipeline will not be AI-first or human-first. It will be learning-first.