Can Skills-First Hiring Bring More Women Into AI?
Skills-first hiring is often proposed as a way to widen AI talent pools. Here is where it can help women enter AI roles and where it falls short.
By Amara Okafor, Women in AI Editorial Fellow ยท 30 August 2026
Artificial intelligence is changing faster than most job architectures. That makes rigid hiring criteria unusually risky.
A company that insists every candidate must already have held an identical AI job title may exclude people who can do the work but built their experience through software engineering, data, product, research, analytics, risk or another adjacent discipline.
For women, who are already underrepresented in AI talent pools, that matters.
What skills-first hiring means
Skills-first hiring shifts attention from proxies such as degree subject, prestige of employer or exact previous title toward evidence that a candidate can perform the work.
That evidence might include technical assessments, portfolios, shipped products, research, open-source contributions, domain expertise or practical case exercises.
LinkedIn's Skills-Based Hiring research estimated that adopting skills-first approaches could modestly increase women's representation in AI talent pools while also expanding the overall number of people employers can consider.
Why AI is well suited to this approach
Many current AI responsibilities did not map neatly onto established roles a few years ago.
A software engineer may now spend much of her time building retrieval pipelines. A compliance professional may become the internal lead for AI governance. A product manager may develop deep expertise in evaluating generative AI applications. None of those career histories necessarily contains the exact title an employer is searching for.
Skills-first hiring gives companies a better chance of recognising this adjacent experience.
It can also help career changers
Women returning after career breaks or moving from another sector can be disproportionately disadvantaged by filters based on continuous, linear experience.
AI offers opportunities to combine existing domain knowledge with new technical capability. A healthcare professional who understands clinical workflows and AI evaluation may be more useful to a health technology company than a generalist candidate with a more conventional CV.
But skills-first hiring is not enough
Changing the first screen does not automatically change who gets promoted, sponsored or assigned to high-impact work.
If a company hires a broader group but its senior leadership remains homogeneous, or if important AI projects consistently go to the same networks, representation may improve at entry level without changing the distribution of influence.
There is also a design question: assessments themselves can reproduce bias if they reward familiarity with a narrow style of interview rather than the skills needed for the job.
A better hiring process
For AI roles, employers can improve recruitment by:
defining the actual capabilities required for the first year of the role; removing degree requirements that are not necessary; accepting adjacent technical and domain experience; using work samples that resemble real tasks; separating must-have skills from skills that can be learned on the job; measuring who progresses through each stage of the process.
The result is not lower standards. Done well, it is a more precise definition of standards.
The wider lesson is that AI hiring needs to catch up with AI work. When roles evolve quickly, employers who recruit for yesterday's titles risk missing tomorrow's strongest candidates.