As AI continues to automate routine tasks, some progressive organizations are shifting early-career roles to handle complex work that requires judgment. As a result, junior employees need to navigate ambiguity and deliver higher-impact outputs much earlier in their careers.
But most organizations haven’t tailored their development approaches to match these new expectations. According to a September 2025 survey by Gartner, learning and development leaders say that investment in early-career development is static or slightly declining. This leaves a gap between evolving expectations and the support early-career employees receive.
While AI tools are powerful accelerators that can enable employees to gather and synthesize information faster, they can’t replace one critical capability: human judgment.
Early-career employees often lack the experience necessary to assess the accuracy, bias, and relevance of AI-generated outputs. This kind of judgment is especially crucial in roles that require nuanced decision-making or subject matter expertise.
At the same time, limited subject matter expertise and institutional knowledge can weaken how early-career employees use AI in the first place. Without strong foundational knowledge or context, it can lead to vague or misdirected prompts, resulting in lower-quality starting points.
The risk of outdated development support
Early-career employees without the right development support may produce poor-quality outputs, which many refer to as “AI slop.” As a result, senior staff will need to get involved. In the long term, organizations that fail to develop early- career talent risk weakening their internal talent pipelines and will have to rely more heavily on external hiring for mid- and senior-level roles.
For HR leaders, that means early-career development needs to evolve to support the new work that this talent is performing now, rather than aligning to traditional stepping stones and relying on the business to provide low-risk learning opportunities. You need to treat the development act as a launchpad, rather than a slow on-ramp. In practice, that means using development touchpoints to build safety nets for more complex work. Those safety nets are the tools, guidance, and connections that help early-career employees navigate more complex decisions and tasks despite their limited experience.
Below are three development-based safety nets that can help better equip employees for more complex work:
1. Teach value-driving behaviors through formal learning
Formal learning provides a unique opportunity for explicit instruction. It helps employees understand exactly how their work creates value for the organization, what the company expects of them, and why. According to a July 2025 Gartner survey of 2,986 employees, performance improves by up to 30% when employees understand how their work contributes to business outcomes. When employees can connect their role to a broader business strategy, they are in a better position to make decisions in uncertain situations.
To support this, organizations must adapt early career training to focus on building business acumen and how employees’ actions can deliver business value. That means setting clear, explicit goals and giving examples of what they expect their performance to be. When they do this, employees are in a better position to make the right decisions in uncertain situations, despite their limited experience.
2. Build social networks to support real-time problem solving
As employees encounter novel challenges, they have to apply more judgment. In this situation, access to people who understand their work and can offer relevant, experience-based guidance becomes increasingly important.
Employees with diverse networks are better positioned to build these capabilities than those who rely primarily on very senior connections or deep technical expertise.
While early-career employees still need to build technical expertise over time, AI’s ability to augment many technical tasks means that they need to master discernment and adaptability in ambiguous situations. HR leaders can strengthen this kind of learning by broadening how employees build their networks. That means more project-based coaching and mentoring, cohort- or rotation-based learning. It’s also about providing intentional opportunities for employees to connect with peers and slightly more experienced colleagues who understand their day-to-day work.
3. Create safe spaces for mistakes
Employees are twice as likely to demonstrate high skills-preparedness when organizations treat mistakes and delays as an acceptable part of learning, per Gartner. As early-career employees take on more complex work, making room for experimentation becomes increasingly important.
In the short term, organizations can use structured practice environments such as GenAI simulators to give employees space to learn without immediate consequences. But simulations aren’t a substitute for real work. They can’t replace the learning that comes from being accountable for actual outputs, and they hold limited value if they simply reproduce tasks that are no longer relevant.
Ultimately, building skills-preparedness requires embedding learning directly into the work itself. When you structure roles so that employees can contribute while building new capabilities, performance and learning can happen at the same time.
As AI continues to reshape entry-level work, early career development carries more weight than it once did. HR leaders can respond by using development support to build safety nets into work. Those safety nets can help employees take on more complex work earlier in their careers, improving customer and client outcomes in the short term and strengthening the talent pipelines that organizations will need over time.
No comments