You are starting your career at a moment when the work that used to build your confidence, capability and judgment is being automated. That deserves your attention, because it changes what you need to ask for and how early you need to ask for it.
What the data is telling you
The World Economic Forum’s Global Gender Gap Report 2026 shows why. Women make up 46% of entry-level workers in the data examined, but only 23% of the C-suite. The same analysis reports that women account for 57% of workers in occupations considered most likely to be disrupted by generative AI. McKinsey and LeanIn’s most recent Women in the Workplace research adds the detail that matters at your level. Only 31% of entry-level women have a sponsor, compared with 45% of men. Four in ten have not received a promotion, a stretch assignment or access to leadership training in the past two years.
So you are entering a pipeline that was already leaking women, at a moment when AI removes the tasks that built readiness.
None of that is your fault. All of it is now yours to manage.
What you are actually losing?
Routine work was never the path to leadership, but it sets you up for the path ahead.
A careful piece of analysis gives you something to think about. Thinking about it gives you questions. Answering those questions in front of someone more senior shapes your judgment, which leads to owning a small decision, then a workstream, then a team. Each step earns the next.
Matt Beane, who studies how skill passes from experts to novices, calls this the apprenticeship bond, and his research on robotic surgery shows how it breaks. When the lead surgeon controls the robot, the trainee becomes an optional participant watching from a second console. Technology gets adopted faster than training gets redesigned, and the watching-and-doing that builds surgical skill quietly disappears. Your context might be different, but the mechanism is identical. The machine takes the first pass, the senior person reviews the output, and you are no longer in the loop where the learning happens.
When the first task disappears, the sequence does not rebuild itself. You have to rebuild it deliberately.
Here are five moves worth making in your first two years:
1. Ask for the judgment, not the task
Position yourself where the decisions remain after AI has completed the first pass.
· Ask to review an AI-generated recommendation before it goes forward. Identify what the model has missed, name the assumption it made that your business would not, explain the trade-offs and propose the next action.
· Ask for structured stretch assignments: a contained client discussion, a low-risk operational decision, one piece of a cross-functional initiative.
· Ask for the boundaries to be made explicit, ask who you can go to when you are unsure, then own the result.
Confidence does not come from reading, and it does not come from being told you have potential. It comes from carrying something, watching how it lands and thinking honestly about why.
2. Become visibly good at AI, without becoming the help desk
Learn the tools properly, understand where they fail in your function, and be the person who can explain why a particular output should not be trusted.
Junior people who are genuinely AI-native can do remarkable things regardless of experience. That is your entry ticket into rooms your grade would not normally reach. Trade your fluency for proximity. Offer to show the team how you are using the tool and how your function can benefit.
One caution. There is a version of this where you become the person who fixes everyone’s prompts and formats everyone’s decks and never touches the decision. That is the old office-housework trap wearing new clothing. If your AI work does not increase your exposure to judgment, it is not advancing your career.
3. Build your own evidence file, because nobody else will
Research by Shelley Correll and Caroline Simard found that 60% of men had their feedback tied to business outcomes, against only 40% of women. So, keep a running record: the decision, what you recommended, what happened, and what it saved or earned. Bring it to your review rather than waiting to see what your manager remembers.
A useful way to open that conversation: “Thank you for the opportunity. Here is the business outcome my work moved this quarter. What would need to be different for you to consider me ready for the next level?”
4. Get a mentor and a sponsor, and know which one you are asking for
A mentor helps you understand what you are learning and where you need to grow. A sponsor spends their own credibility putting your name into a room you are not in. They are not interchangeable. Development without visibility rarely converts into advancement. Visibility without preparation becomes an unfair test.
Two practical implications. First, seniority matters: a generous mentor who sits in no decision-making forum cannot sponsor you. Second, sponsors need material. They are staking their reputation, so give them something specific to say about your judgment, not your attitude.
A sponsor request sounds like this: “When the next piece of client-facing work comes up, I would like you to be considered for it. What would you need to have seen from me first?”
5. Watch where you are standing, not just how hard you are working
Effort does not compensate for position. If your role is concentrated in the tasks AI automates rather than the ones it augments, no amount of hard work changes the trajectory of that seat.
Understand what the work in your function will look like in three years, where the judgment is migrating to, and which adjacent skill would move you closer to it.
Stepping sideways early is far cheaper than discovering later that you have deep expertise in something nobody needs.
Plan your climb deliberately!
AI will change the first rung of your career. It does not remove the climb, but nobody will protect that climb on your behalf by default.
As your organization redesigns entry-level work, make sure you are the one deciding how you climb: how you become visible, trusted and ready to lead.
If you are not thinking and asking for what you want, someone else is.
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