Women accounted for just 26% of US AI hires in 2025, compared with 50% of hires into non-AI occupations, according to an August 2026 LinkedIn analysis. US AI job postings have roughly doubled since 2023, making the representation gap especially significant as companies build out AI teams and seek workers who can develop, deploy, and work with the technology.
The pay can also be substantial. LinkedIn found that the typical US AI job posting lists about $177,000 in compensation, compared with about $80,000 for a typical non-AI role. Those figures describe postings across the market, however, not what an individual applicant should expect to earn.
Women face gaps across AI hiring and leadership
The representation gap extends beyond initial hiring. Women account for 20% of Head of AI roles and 18% of Member of Technical Staff roles in LinkedIn's analysis, while women hold only 13% of C-suite AI leadership positions at AI companies across the 27 countries studied.
Newer data show that women's representation also varies sharply by occupation. The World Economic Forum's Global Gender Gap Report 2026, published Sept. 16, found that women account for 19.3% of AI engineers and 20.6% of machine learning engineers across 19 economies with available data. Women represented 44.8% of data annotators, showing that participation is considerably higher in some parts of the AI workforce than in roles more closely tied to model development and deployment.
The gap can appear before employers make a hiring decision. March 2026 IMD research found that women submitted 16% of applications for technical positions requiring emerging-technology skills, compared with nearly 20% for positions using more established technologies. Women were also less likely than men to apply for emerging-technology jobs that required moving to another city, while the difference narrowed sharply when relocation wasn't required.
For workers who are already employed, an internal AI project may offer another route to relevant experience. Ask how AI pilots, workflow redesigns, or technology projects are staffed and whether employees can volunteer or train for them.
The stakes are not limited to access to new AI jobs. An April 2026 National Partnership for Women & Families analysis found that women make up 47% of the US workforce but 83% of workers in the 15 occupations it classified as most vulnerable to AI-related displacement.
A separate January 2026 Brookings analysis identified 6.1 million US workers whose jobs combine high AI exposure with low capacity to adapt to displacement; 86% of them are women. Many work in clerical and administrative occupations. Neither analysis predicts that those jobs will disappear on a specific timetable: AI exposure measures how work could be affected, not whether a particular worker will lose a job.
More recent LinkedIn data published Sept. 16 point in the same direction, finding that women account for 57% of workers in occupations considered most likely to be disrupted by generative AI, compared with 43% for men.
Three ways to break into AI work
If you're already employed, look for ways to build AI experience before making an external move. That could mean joining an AI pilot, helping redesign a workflow, testing outputs, documenting quality controls, or measuring whether a new tool improves a business process.
Professionals in marketing, HR, health care, finance, operations, and other fields can also target AI-adjacent work that combines subject-matter expertise with applied AI skills. Depending on the employer, that could involve implementation, evaluation, governance, workflow design, or adoption rather than model development. Requirements vary widely, so compare current job postings before paying for a course or credential.
People with a technical background can pursue engineering, machine learning, research, or implementation-heavy roles. LinkedIn says AI Engineer has overtaken Machine Learning Engineer as the most common AI occupation on its platform. Forward Deployed Engineer, a role focused on helping organizations implement AI systems, now ranks third among AI occupations in job postings.
Technical roles generally require a different preparation path. LinkedIn found that 91% of current AI workers have at least a bachelor's degree, with the share exceeding 95% in several highly paid occupations. That doesn't make a degree a universal requirement, but it is a useful reality check before assuming a short certificate alone will qualify someone for an engineering or research position.
Build proof of AI skills employers can evaluate
Before paying for a certificate or course, compare 8 to 10 current postings for the role you want. Track which qualifications repeatedly appear as required rather than preferred, including technical tools, data literacy, workflow implementation, model evaluation, or governance knowledge.
A work sample can show more than simply listing “AI skills” on a resume. Depending on the role, that might mean documenting an AI-assisted workflow, creating an evaluation or risk-review checklist, or showing how human review and quality controls were built into a process.
Internal projects can be especially useful for workers who want relevant experience without relocating or changing employers. On a resume or in an interview, describe the tool used, the task, the result, and the human oversight involved. That gives an employer evidence of applied skill instead of a vague claim of AI proficiency.
Check the role before you pay for training
Don't interpret the $177,000 figure as the value of adding one AI skill to your resume. IMD's research found that experience with emerging technologies was associated with about a 6% salary premium, while in-demand skills were associated with roughly 2% higher salary offers.
The $177,000 and $80,000 figures describe typical listed compensation across different groups of job postings; they do not isolate the effects of seniority, occupation, industry, education, or experience on an individual's salary. Use the salary range for the specific role, location, and experience level you're targeting as a more realistic benchmark.
For occupations covered by the Bureau of Labor Statistics, the Occupational Outlook Handbook can help you compare typical education requirements, pay, duties, and projected employment before committing to training.
Start with one target AI role
Start with one target role that fits your current skills and experience. Review 8 to 10 recent postings and track the qualifications that repeatedly appear as required rather than preferred.
Before paying for a course or certificate, check whether employers actually ask for it. Then speak with someone doing the job, if possible, and compare that person's experience with what current postings demand. The goal isn't to collect AI credentials; it's to build evidence that you can use the technology to solve the problems employers are hiring for.
