Think back to the start of your career. Maybe you interned for minimum wage, or did menial work like media lists or slide deck updates. It wasn’t glamorous, but it helped you learn the business and get to your current position.

Now, AI can do all that entry-level work. For example:

• Banks are already looking to cut their incoming analyst classes
• Private equity firms are using automated financial modeling tools to do what junior associates typically do
• 44% of legal work is expected to be automated

So what does that mean for college grads, interns, and anyone looking to switch industries?

Last week, we sat down with labor economist Sania Khan to talk about the rebuilding of the career ladder with AI.

She told us this shift isn’t necessarily about jobs disappearing, it's about roles evolving – and faster than most organizations can handle. Here's what she’s seeing on the ground, and what you can do about it.

The 3-year reset

Across industries, 64% of leaders expect entry-level roles to shift from “creation” to “review” within three years. That means fewer blog posts and original code, more copy-editing and test scripts.

Sania said there are 4 categories of work that are already beginning to shift toward automation:

• Repetitive, rule-based tasks (data entry, basic analysis)
• Data-intensive work (financial forecasting, market research)
• Predictable decision-making (standard approvals, basic QA)
• Low-creativity tasks (content tagging, report generation)

Within five years, we should start to see enough new jobs created by AI that the job loss balances out. But in the meantime, the key is for junior employees to “move up the value chain”.

In other words, junior team members need to go from creating outputs to interpreting them, from processing information to thinking strategically about its implications.

How to thrive in a world AI can automate

For employees

1. Map out your role as it exists now: Document everything you actually do in a typical week. Pay special attention to tasks that fall into AI's sweet spot: repetitive work, data analysis, predictable decisions, and low-creativity requirements. This inventory becomes your blueprint.

2. Move up the value chain: For every task you currently do that AI could handle, what higher-value work can you be doing instead? (E.g. When AI writes first drafts, you can focus on refining strategy and positioning.) This isn't about doing less, it's about elevating your contribution.

3. Double down on uniquely human capabilities: List out the skills that will differentiate you in an AI-powered workplace: relationship building, personalization, and perhaps most importantly, the ability to judge AI outputs. Focus your development efforts here and get really good and what you uniquely bring to the table.

For leaders

Leaders face a different challenge – if they keep a vast swath of entry-level workers on staff, they’ll soon be behind competitors in terms of staffing costs and overhead. Here’s what Sania says leaders need to be thinking about:

1. Analyze AI's impact across your entire organization: This isn't just about identifying which processes can be automated – it's about understanding how roles will evolve and what new capabilities you'll need.

2. Feed this analysis directly into skills forecasting: What expertise will your organization need six months from now? A year? Five years? Look at this through the lens of the human capabilities that will complement AI, then use this forecast to guide both your upskilling investments and your hiring strategies.

3. Redesign roles proactively: Sania stresses that workforce transformation cannot just be about automating tasks and eliminating positions. You need to create clear career progression paths that factor in how the nature of work is changing in your org.

Your path up the ladder

Sania outlines two clear mandates for the workforce:

• For ICs: Where you work matters as much as how you work. Choose companies that are actively investing in AI adoption and employee development. Staying with one that isn't is a risk to your career.
  
  
• For leaders: Invest in your people before, during, and after AI deployment. Create clear paths for career evolution, build a culture around both human values and AI efficiency. This is a strategy outside of just maintaining morale.

And for those just joining the workforce, the game has changed quite a bit which kind of makes you the guinea pig. But it’s important to emphasize that this shift doesn’t make the last 10-15 years of a mid-career folk’s experience null and void.

Everyone now has the same opportunity before them: To climb an unfamiliar ladder and rethink how work can get done for jobs that might not even exist yet.