Before your interview starts, someone has already made up part of their mind about you. They typed your name into LinkedIn. They skimmed what came up. In ninety seconds they formed a quiet opinion about how you think, how you communicate, and whether you can actually do the work, all before you said hello.

We were never prepared for that part of hiring. We were trained for the other part: earn the degree, collect the titles, build the CV, and let it speak for you. So we poured years into the document. And many women tell me the same thing anyway. The callback does not come. Or the role goes to someone with half the track record and twice the visibility.

For a long time the explanation on offer was that you were not doing enough. Here is a more accurate one, and the accurate one puts the power back in your hands. The thing employers are looking for has quietly changed. In the AI era, they increasingly hire people based on evidence of what those people can actually do, not simply their CV, degree, job titles, or years of experience.

Why this favours you, not the system that overlooked you

LinkedIn’s own workforce data is blunt about this: there is no longer a normal, linear career path. Careers move sideways, diagonally, and across industries far more than the neat ladder of junior role, manager, director, and vice president ever admitted. More importantly, the skills a role requires are changing quickly, and are expected to shift more sharply by the end of the decade as AI reshapes the work itself.

This retires a question we were all taught to answer well. Where do you see yourself in five years is a question built for a map that no longer describes the territory. The more useful question, the one that actually compounds, is smaller and nearer: what capability could I build in the next three to six months that would make me measurably more valuable. Five-year plans are guesses. A capability you can name and build is an asset.

For a woman in transition, this is a relief, not a threat. You do not have to reverse-engineer a decade. You have to choose the next capability and go and build the evidence that you have it.

What employers now mean by evidence

The instruction underneath all of this is simple to say and harder to practise. Do not tell employers you are capable. Let them see it. Telling is a claim, and claims now compete with a stack of identical claims. Showing is proof, and proof is rare, because most people never make their capability visible.

There are more ways to do this than a CV allows, and none of them require permission.

The explorer, not the map reader

If one shift separates the people who thrive now from the people who stall, it is this. The most valued professional is no longer the map reader who says, give me the process and I will follow it. It is the explorer who says, I am not sure the process exists yet, so let me investigate, test something, and build one. The map reader waits to be told. The explorer goes and finds out. Three habits mark the explorer, and each one is learnable.

The first is a scientific mindset. Instead of instruction and execution, the sequence becomes hypothesis, experiment, evidence, conclusion, and round again. You form a view about what might work, you test it small, you look honestly at what happened, and you adjust. Employers are no longer only asking what you did. They are asking what you tried, and what you learned when it did not go to plan. Failed experiments count here, because they prove you can think, not merely follow.

The second is getting close to the actual work, rather than talking about transformation from a distance. Grand phrases about AI transformation impress no one now. Specific observation does. So you go and look at the real workflow, and you ask plain questions.

The third is customer curiosity. Using AI is not the achievement. Solving a problem is. The value is not in the sentence I used AI. It is in the chain that connects a real problem to a workflow to a specific AI intervention to a measurable outcome. The person who can draw that line, from a customer’s pain to a number that moved, becomes very hard to replace.

“I use AI” is no longer the flex

Almost everyone can now say they use ChatGPT. That sentence has stopped separating you from anyone in the queue. What separates you is a process you rebuilt and can prove. Take one thing you already do well and redesign it.

The point is not the tool. The point is the four numbers, and the fact that you went and got them.

Go one level deeper than the tool

You do not need to become an engineer. You do need to understand what is happening underneath the tools you use, enough to know what these systems can and cannot do, and where the risks sit. That means a working grasp of the vocabulary now shaping the work: what an AI agent is, how tools and APIs let these systems act, what automation can and cannot be trusted with, and how the pieces connect. Fluency, not mastery. The goal is to know what is possible, what is not, and where the danger lives, so that when you propose a change you are proposing something real. For anyone whose work touches policy, risk, or governance, this matters twice over, because rules written without an understanding of the technology underneath them collapse into abstract language no one can apply.

Your winding path is the offer

Here is the part that should change how you see your own history. The most valuable professional in this era is not the pure AI specialist. It is the person who pairs deep domain knowledge with enough AI fluency to apply it. Generic AI knowledge is now common. Domain expertise plus AI is rare, because it takes years to learn a field’s real workflows, its stakeholders, its regulations, its risks, and the operational problems that never make it into a textbook. Someone who understands the domain can point AI at the right problem. Someone who only knows AI cannot.

My own path ran through the lab, then logistics, then medical devices, then publishing, before I built this company, and for years I was asked to explain the moves as if they were a lack of focus. They were the opposite. Every one of those worlds is now context I can apply, a real workflow I understand from the inside. Your range is not a liability to explain away. It is a library only you hold, and it is exactly what makes your AI fluency worth paying for.

The strongest move is to redesign the role

Read this one twice, because it is the most powerful move available to you. The strongest candidate does not say, I am good at this job. She says, here is how I would redesign this job, and then the team’s workflow around it, using AI. Weak and strong sit very close together in words, and very far apart in effect.

Applying for a role in marketing, the weak answer is, I use AI to write content. The strong answer is, I looked at how your campaigns run now. Here is where the bottleneck sits. Here is what I would automate, here is where I would keep human judgement, and here is the AI workflow I would introduce, with the time it would save and the quality checks that would protect it.

Do you hear the difference. The first is asking for a job. The second is showing the employer what they gain by giving you one. It turns the interview’s central question from why should we hire you, into how soon can we use you. That is a far stronger place to be standing, and it is available to you in any field, at any level, the moment you decide to prepare a redesign instead of a defence.

You were never short on capability. You were short on evidence in the places that count.

Sources: this issue draws on public remarks about hiring in the AI era by Sal Khan (founder and CEO, Khan Academy), Yamini Rangan (CEO, HubSpot), Aaron Levie (co-founder and CEO, Box), and Grant Lee (co-founder and CEO, Gamma), together with workforce data and observations from LinkedIn under Ryan Roslansky, who served as CEO from 2020 to 2026. The explorer and map reader framing is drawn from Yamini Rangan; the redesign the role idea from Grant Lee.