As organizations adopt generative AI to boost efficiency, many are quietly eliminating entry-level positions that are gateways for new talent.

We recently debated the talent crunch during a Coffee With Digital Trailblazers, and at the last Spark Executive Forum. Eliminating entry-level roles or slowing hiring of younger professionals will likely create a future talent debt. Where will the next generation of Digital Trailblazers come from, when fewer people are coming up the ranks with hands-on experience?

Here is some alarming data:

• A Stanford study using ADP payroll data finds a 13% relative decline in employment for early‑career workers (ages 22–25) in occupations highly exposed to generative AI
• Postings for entry-level jobs in the U.S. have declined about 35% since January 2023, according to labor research firm Revelio Labs, with AI playing a big role.
• IDC reports that 66% of enterprises are reducing entry-level hiring due to AI, with 91% seeing jobs changed or eliminated by automation.

During the Coffee With Digital Trailblazers, Joe Puglisi, growth strategist and fractional CIO at 10xnewco, described the talent crunch as a fool’s paradise. “There is no training going on at the lower level, and this is a disaster waiting to happen,” said Puglisi. Martin Davis, managing director of DUNELM Associates, added, “Even though AI may not be driving the savings, executives are having to, basically, for the shareholders’ benefit, demonstrate that they are reducing headcount by using AI. If you’re not building the basic skills, then how do you get the more advanced competencies?”

Unfortunately, many executives will accept the talent debt in the short term to demonstrate ROI from AI-driven efficiencies. It’s one reason I’ve stated that AI is only reshaping business, not transforming it yet. Too many leaders want AI agents focused on task automation rather than growth opportunities.

So, I challenged several experts to weigh in on some options for boosting entry-level talent in the gen AI era. Here are four ways.

1. Redefine roles and promote AI skills

“Entry-level roles should evolve to focus more on AI-assisted diagnostics, system understanding, and judgment, with early career employees responsible for validating outputs, handling edge cases, and escalating complex issues, says Michael Ameling, President of the SAP Business Technology Platform. “Embedding AI fundamentals, data literacy, and human accountability into daily workflows ensures hands-on experience doesn’t disappear, but compounds. This approach builds a stronger talent pipeline while preparing teams to operate confidently in AI-native environments.”

My recommendations: (i) Consider redefining existing positions to these 25 emerging genAI roles. (ii) Develop your organization’s digital dexterity to accelerate the development of leadership skills. (iii) Accelerate AI adoption and ROI, but demonstrate returns in areas outside of headcount reduction or lowering level-1 talent pools.  

2. Develop AI literacy and scale data governance

“For roles where AI can handle much of the heavy lifting, like engineering or development, entry-level positions will require hands-on experience in problem-solving and strategic oversight, rather than manual execution,” says Dennis Bruder, chief product officer at Coupa. “They’ll need to understand what AI can and can’t do, know how to fill the gap, and ask the right questions to ensure its work meets standards. Without this “human-in-the-loop” guidance, AI won’t be as effective. Hiring managers should seek employees with a strong understanding of data governance, who will challenge AI’s assumptions to get to the root of the problem.”

My recommendations: (i) Develop an AI literacy program and tailor it for different roles and employee levels. (ii) Communicate and evolve an AI governance strategy that combines strategic goals with guardrails, policies, and data governance non-negotiables. (iii) Beat competitors by empowering a human-centric future of work, including how AI drives customer focus and improves talent retention.

3. Partner for on-the-job training programs and apprenticeships

Three experts weighed in on the importance of developing apprenticeships. They highlight entry-level opportunities in three stages

1. Shift from creating to validating and correcting. “The opportunity is to create structured, AI-augmented apprenticeships where hands-on experience comes from overseeing, testing, and correcting AI output rather than producing everything manually,” says Kate Shaw, senior product manager at SnapLogic. “Organizations that do this will preserve their talent pipeline while accelerating time-to-competence rather than hollowing it out, and will train the next group of entry-level engineers to produce meaningful assets they can troubleshoot in place of blackbox systems that remain opaque and unsupportable.”
2. Grow from AI validators to contextual problem solvers. “Enterprises need to intentionally redesign these jobs so early-career talent learns how work gets done with AI, not after it,” says  Lauren Tropeano, chief people officer at Docebo. “That means pairing automation with apprenticeship-style experiences that build judgment, context, and real problem-solving skills.”
3. Target problem solvers to evaluate tradeoffs and become tomorrow’s decision makers. “Early-career talent now learns by reviewing how AI reasons, using decision evidence and execution paths to understand logic, tradeoffs, and outcomes, says Pratyush Mulukutla, chief data officer at MediaMint. “Enterprises should reimagine entry-level roles as AI-augmented apprenticeships, as the fear that genAI will hollow out early-career positions assumes those roles existed only for manual execution.”

My recommendation: More organizations would benefit by partnering on centers of excellence programs that provide on-the-job experience.

4. Develop experts in AI’s most impacted disciplines

Three areas most impacted by AI are security, DevOps (especially developers), and customer support. Organizations will need to develop subject-matter experts and specialized training to fill gaps. Otherwise, today’s expertise may be reduced to a limited pool of tribal knowledge hoarders.

1. Security

Brooke Motta, CEO and co-founder of RAD Security, says that the best security teams are reshaping, not eliminating, junior-level roles. “Instead of manual triage or checklist compliance, new hires can focus on higher-order thinking, guided by systems that automate grunt work while still explaining their logic. Agentic AI that handles the “Tier 1” grind of investigating alerts, correlating context, and recommending next steps, which creates a faster path for people to build intuition and judgment without burning out. Smart companies will pair this automation with structured development paths that quickly turn junior hires into strategic contributors.”

My recommendations: Focus learning efforts on AI devsecops skill areas. I expect to grow in importance and risk: data security, modelops, and security test automation. Other focus areas should include data privacy and monitoring for rogue AI agents.   

2. DevOps

Brijesh Kohli, VP and head of education at Xebia, says that to future-proof entry-level talent, companies must shift from replacing to augmenting roles with GenAI. Kolhli recommends

• Equip junior developers, support engineers, and security analysts with AI tools like GitHub Copilot, Claude, and Amazon CodeWhisperer to accelerate productivity.
• Evaluate new hires not just on coding ability, but on prompt writing, responsible AI use, and understanding of output accuracy.
• Launch structured training programs or role-based enablement to integrate AI into workflows.
• Partner with universities to embed AI into core curricula and strengthen foundational coding skills.
• Model enterprise-wide adoption through internal programs to give early-career professionals hands-on experience with automation.

“Organizations that act now will build a more adaptive, AI-fluent workforce ready to deliver value from day one,” says Kohli.

My recommendations: Several new skills for developers include AI requirements, vibe coding, AI release-readiness, AI agent test automation, and AgenticOps. Also, leaders must revisit agile for the genAI era.

3. Customer Support

“In customer service and support, an effective approach is to redesign Level-1 roles around AI supervision and quality analysis,” says Niraj Rout, founder and CEO of Hiver. Entry-level employees review AI outputs, handle edge cases, validate decisions, and manage escalations. Hands-on experience increasingly comes from understanding where automation breaks down and learning how to intervene with human judgment and context. New hires learn faster because they engage with real consequences and work closer to complex downstream problems.”

My recommendations: Leaders don’t have to choose and can cut costs while improving customer support. But a fully autonomous customer support function is a “fool’s paradise” carried over from IVRs and rule-based chatbots. Haven’t companies learned?

Grow talent, preserve knowledge, avoid talent debt

“I would think long and hard before you lay people off, that the tribal knowledge or institutional knowledge that’s going to go out the door with them is not only invaluable but extremely hard to replace,” said Joanne Friedman, CEO of ReilAI, during the talent crunch episode of the Coffee With Digital Trailblazers.

There’s a lot of hype out there about how fast AI can replace people. I don’t know of too many businesses that slash costs and burn their future as a pathway to growth.