Automation is moving fast in the IT market, and the ones feeling the impact first are professionals still in the early stages of their careers.
Since 2021, entry-level positions have dropped 10% across all industries, according to a recent SAP report. But when you zoom in on the 10 most common entry-level roles — like software engineer, data analyst, and customer support — the decline is way more dramatic: 35% between 2024 and 2025.
And that raises a question a lot of companies still haven’t truly answered: if the front doors are closing, who’s going to fill the IT leadership seats a few years from now? 🤔
It’s no exaggeration to say the traditional growth model in IT has always depended on these entry-level roles to function. That’s where professionals built their technical foundation, learned the business, and developed their first leadership skills — right there in the most operational, repetitive tasks. With artificial intelligence automating exactly that kind of work, this path is being cut off before it even begins. 🚨
The talent pipeline is broken
For decades, the IT sector worked like a well-oiled conveyor belt: professionals entered through operational roles, learned by doing, made mistakes in low-risk environments, and over time accumulated the experience they needed to step into more strategic positions. That cycle wasn’t perfect, but it worked. It created a steady pool of talent ready to grow within organizations. Today, that conveyor belt is jammed, and the problem goes way beyond a simple reduction in job openings.
Autumn Krauss, chief scientist at SAP SuccessFactors’ Future of Work Research Lab, puts it well. According to her, organizations are creating a gap in the future leadership pipeline when they keep reducing entry-level hires without rethinking how early-career talent develops. Historically, people built business acumen, technical expertise, and leadership skills precisely during their first few years on the job. With AI embedded into workflows, companies need to be intentional about creating new pathways for these professionals to develop those competencies.
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What’s actually happening is a compression of the IT job market that hits the most vulnerable layers first. Companies, under pressure to boost efficiency and cut costs, found in automation a quick fix to replace repetitive tasks that used to be handed off to junior professionals. The immediate result looks great on financial reports, but it creates a quiet, long-term problem: no entry, no progression. And no progression means no leadership pipeline.
When a company eliminates its entry-level positions, it’s not just cutting operational costs. Without realizing it, it’s cutting investment in its own future. The senior IT leaders who hold strategic roles today went through — almost without exception — the very functions that AI is now taking over. A junior engineer, for example, used to start in QA and testing, gaining hands-on experience with how systems actually work. As they moved up, that knowledge compounded, shaping leaders who deeply understood both the technology and the business.
Being honest about what AI removed
Maruf Ahmed, CEO of IT staffing and consulting firm Dexian, has a pretty straightforward take on this. For him, maintaining a strong succession path starts with honesty about what AI has removed from someone’s development — and then deliberately filling that gap. Senior leaders need to spend more time actively teaching, and people need to be exposed to complex decisions earlier in their careers. The day-to-day work used to build that foundation on its own, but that just doesn’t happen anymore.
AI as a partner, not a replacement
It’s important to be clear that AI itself isn’t the villain of this story. The technology is doing exactly what it was designed to do: execute repetitive tasks with more speed, accuracy, and scale than any human could. The problem isn’t the tool — it’s how organizations are responding to it. Instead of using automation to free up junior professionals for more complex, higher-value work, a lot of companies are simply removing those positions from the org chart. It’s a short-term mindset that solves one problem today and creates three for tomorrow.
There’s also a technical detail that can’t be overlooked: it’s not always easy to evaluate the quality of responses generated by AI, especially since the technology has become notorious for hallucinating results. IT leaders — current and future — need a solid knowledge base to feel confident evaluating those outputs, particularly if they expect their teams to use these tools day in and day out. The most valuable leaders are exactly the ones who are comfortable making decisions with incomplete information and capable of judging automated processes the moment something unexpected comes up.
As Ahmed points out, AI rarely stays contained in a single area for long. The moment an organization automates one department, there’s an expectation to extend it across the rest of the company. And then leaders who built their entire career within a specific function suddenly find themselves being asked to weigh in on AI use in areas they never directly managed.
On the other hand, there are companies that already get this and are building smarter models for integrating humans and AI. In those environments, junior professionals work alongside automated tools, using them as accelerators for their own learning. An entry-level software engineer, for example, can use AI to generate boilerplate code and, instead of spending hours on that mechanical task, focus their energy on understanding the logic behind architectural decisions, reviewing what was generated with a critical eye, and developing the ability to communicate technical choices to non-technical stakeholders. That’s the kind of skill no language model is going to replace anytime soon. 🤝
Companies still haven’t done their homework
A recent Deloitte survey shows just how big the gap is between expectations and preparation. According to the study, 84% of companies still haven’t redesigned their roles around AI, even with high expectations for automation. On top of that, 36% expect at least 10% of their positions to be fully automated within a year, and 82% believe that will happen within three years.
Even with those numbers staring them in the face, fewer than half of organizations are making meaningful adjustments to their talent strategies. About 53% say they’re simply focused on educating employees to boost AI fluency. That leaves entry-level workers and those in more operational roles on pretty shaky ground as automation replaces time-consuming tasks and managers start overseeing teams made up of both humans and machines.
Deloitte also points to a potential shift toward flatter organizational structures. More than half of companies are considering pod-based or non-hierarchical models, while 16% have already started making that transition. In practice, IT leaders will increasingly depend on others within the company to make judgment calls about AI, opening the door for more communication and transparency as roles evolve and hierarchies flatten out.
What companies need to rethink right now
The question at the center of this discussion is straightforward: how do you make sure the IT sector keeps producing new leaders in an environment where traditional entry-level roles are disappearing? There’s no single answer, but there are clear directions organizations can follow so they don’t wake up five years from now with nobody ready to step into the positions their current leaders will eventually leave. The first step is acknowledging the problem exists and that it won’t resolve itself over time.
Ahmed warns about a common trap: treating AI adoption as a simple technology rollout rather than a workforce development project. Leaders keep investing heavily in new tools, services, hardware, and technology — and then expect employees to figure out how to use all of it on their own time. Krauss reinforces that their own research showed many leaders don’t feel confident or prepared to lead a transformation of this scale. In the end, organizations fall back on the tactic of handing out AI tools and hoping everyone figures it out for themselves.
One of the most promising approaches is to redesign entry-level roles instead of eliminating them. If automation is taking over the more mechanical parts of a junior data analyst’s work, the answer shouldn’t be to close the position — it should be to redefine what that position means. That means creating space for these professionals to work on interpreting the data generated by AI, communicating insights to business teams, and critically questioning the results presented by models. These are competencies that require context, judgment, and accumulated experience — and they only develop with time and real exposure to the company’s problems.
Investing early makes a real difference
The numbers from the SAP report show just how much weight early talent development programs carry. A full 86% of employees say an early-career program helped prepare them for professional success. Despite that, only 32% report having participated in one, and 49% say their company doesn’t even offer the opportunity.
There’s also a transparency issue that carries serious weight. Only 35% of early-career professionals say they received enough information about which roles within their organization could be automated in the future. And one in three expresses concern that their job might cease to exist because of AI advances. As Krauss puts it, as roles change, companies need to give employees a clearer picture of where opportunities are emerging and which skills will matter most. People tend to invest in their own growth when they can see a path ahead, and that visibility is becoming increasingly decisive. 💡
Another critical point is rethinking internal development programs. Traditional upskilling strategies — the kind that offer an online course and call it done — just aren’t enough anymore. What actually works is a combination of structured learning, exposure to real challenges, and close mentorship from more experienced professionals. Companies that invest in this type of development tend to retain talent longer and build an internal culture of growth that becomes, in itself, a competitive edge in the market.
The IT sector is at a turning point. Automation and AI have already proven they’re here to stay, and resisting that reality isn’t a viable option. But ignoring the side effects of this transformation on the talent and leadership pipeline isn’t either. The organizations that will come out ahead are the ones that manage to leverage technology without giving up on investing in the people who, at the end of the day, are the only ones capable of deciding what to do with everything AI produces.
