Image credit: AI-generated using OpenAI (ChatGPT / DALL·E)
Ask what a career in AI looks like, and most answers involve writing code, training models, and tuning algorithms. Yet some of the most consequential work in AI has little to do with any of that.
It sits in what the industry now calls AI governance: the discipline that shapes how AI systems are deployed, assessed, and monitored. It has become a field where more women, and a wider range of voices generally, are urgently needed.
Consider why. AI governance is where human values, organizational priorities, and regulatory requirements are translated into decisions about technology at scale. It involves determining which datasets are appropriate, which risks require mitigation, and where human oversight is non-negotiable. These are not routine workflows but consequential judgment calls, and these decisions are stronger when informed by diverse professional and lived experiences. That is exactly why widening who sits at the governance table matters.
The field is also formalizing quickly. Regulations such as the EU AI Act, which entered into force in August 2024, are turning AI oversight from primarily an ethics conversation into an increasingly defined organizational responsibility. Structured methodologies have emerged as well. For example, one framework maps AI risk to ISO, NIST, and Shared Assessments SIG, giving organizations reference points for assessing third-party AI risks.
In practice, organizations need people who understand governance frameworks, apply them in day-to-day operations, and explain risks in plain language.
Demand reinforces that need. According to the IAPP AI Governance Profession Report 2025, 77% of surveyed organizations were already working on AI governance, rising to nearly 90% among organizations using AI. The report also identified access to appropriate AI governance talent and skills as a significant challenge: 23.5% of respondents cited finding qualified AI professionals as part of the challenge of delivering AI.
The direction is clear. AI governance is becoming an organizational discipline, and employers need professionals capable of connecting technology with risk, compliance, policy, and human impact.
Why Women Are Central to the Governance Conversation
AI governance spans ethics, technology, law, policy, security, and risk. It values cross-functional and translational work: simplifying technical details for a legal team, translating regulatory requirements for product leaders, or helping compliance and engineering teams reach agreement about acceptable risk.
Many women in technology and adjacent fields have developed these capabilities through roles in program management, policy, compliance, monitoring and evaluation, privacy, security, and organizational leadership. Those experiences can provide a strong foundation for governance work.
The case for broader representation is not about assuming that any group possesses a fixed trait or inherently makes better decisions. It rests on the importance of bringing varied perspectives and experiences into decisions that can affect large groups of people.
Women remain significantly underrepresented in AI. The World Economic Forum reported in 2025 that women represented approximately 25% to 30% of the global AI workforce, with women holding fewer than 15% of executive positions.
That representation gap matters in governance. AI systems can reproduce patterns found in historical data, while decisions about their design, deployment, evaluation, and oversight involve human judgment. Governance teams benefit when the people asking questions about risk, accountability, fairness, and impact bring a broad range of experiences to the discussion.
Women have also helped dismantle the outdated notion that meaningful work in AI belongs only to people who write code. Leaders across responsible AI, law, public policy, ethics, security, and governance demonstrate that shaping how AI evolves—and the products and decisions it powers—depends on more than technical skill.
It also depends on judgment, communication, regulatory understanding, risk awareness, and a clear grasp of societal impact.
Image credit: AI-generated using OpenAI (ChatGPT / DALL·E)
Building a Career in AI Governance
The growing professionalization of AI governance creates opportunities for people from a wide range of backgrounds. Building on a solid foundation makes that opportunity more durable.
1. Start From What You Already Know
Governance rarely means starting over.
A program manager who has designed accountability systems, for example, may already have experience relevant to an AI governance coordinator role. A compliance professional may understand regulatory interpretation and controls. Someone working in monitoring and evaluation may already know how to design assessment frameworks, evaluate outcomes, identify risks, and document accountability.
Start by mapping your existing responsibilities against the work performed in AI governance. A realistic transition path often becomes clearer once you identify the overlap.
2. Know Which Skills Transfer Most Readily
Not every skill transfers directly. Some areas require substantial retraining, while others map naturally onto governance work and can provide a useful starting point.
For professionals coming from monitoring and evaluation (M&E) and related fields, transferable capabilities can include:
Evaluation framework design → AI risk assessment frameworks
Data quality assurance → bias and data-integrity assessment
Theory of change development → AI system impact analysis
Compliance reporting → AI regulatory compliance reporting
Accountability systems → AI oversight and accountability mechanisms
Program risk management → AI risk assessment and mitigation
The terminology changes, but many underlying capabilities—evaluation, documentation, accountability, risk identification, and stakeholder communication—remain highly relevant.
3. Learn the Frameworks Employers Actually Use
Working familiarity with frameworks and regulations such as ISO/IEC 42001, the EU AI Act, and the NIST AI Risk Management Framework can help candidates understand the language organizations use when discussing AI governance.
The goal is not to memorize every provision. Start by understanding what each framework addresses, how organizations use it, and what kinds of evidence or processes governance teams may need to maintain.
Primary documents, professional webinars, and reputable educational resources provide accessible ways to develop that working knowledge.
4. Become Technically Conversant
You do not need to build AI models to participate meaningfully in governing them, but understanding how AI systems work—and how they can fail—makes governance work more effective.
Build familiarity with concepts such as training data, model outputs, hallucinations, bias, model drift, evaluation, human oversight, and third-party AI dependencies. Technical literacy helps governance professionals ask sharper questions and communicate more effectively with engineering and data teams.
The objective is not necessarily to become an engineer. It is to become technically conversant enough to understand the systems, risks, and trade-offs under discussion.
5. Reposition Your Resume Honestly
Your resume is often an employer's first impression, so orient it toward governance without misrepresenting your professional history.
Rather than relabeling a job title you never held, keep your actual titles and reframe the language around them. Strengthen your professional summary, describe accomplishments in governance-relevant terms, and foreground transferable skills that accurately reflect your experience.
For example:
Monitoring and evaluation professionals: Present M&E experience through a governance lens by emphasizing risk assessment, accountability, evaluation, data quality, and compliance reporting. A professional summary can identify AI governance as the intended career direction without implying experience you do not have.
Policy specialists: Highlight regulatory analysis, stakeholder engagement, policy development, technology accountability, and experience translating complex requirements into practical recommendations.
Risk and compliance professionals: Emphasize controls, assessments, regulatory interpretation, documentation, third-party risk, and cross-functional coordination.
Program and project managers: Highlight accountability structures, stakeholder management, risk registers, decision-making processes, and coordination between technical and non-technical teams.
The goal is accurate positioning that helps recruiters and applicant-tracking systems connect your real experience to governance roles—not an inflated title or exaggerated claim.
For professionals exploring this transition, the WomenTech Network AI Governance and Security career path provides an additional starting point for exploring roles, skills, and opportunities in the field.
A Practical Takeaway
The future of AI is being shaped now, and women in tech have an opportunity to influence it not only by building technology but also by determining how that technology is assessed, governed, and held accountable.
AI governance is still developing as a profession, which is precisely what makes this an important time to explore it.
A practical way to begin is to choose one governance framework to study this month. Then audit your existing skills against the transferable capabilities above, revise one section of your resume using accurate governance language, and connect with professional communities where responsible AI and governance are being discussed.
Small, concrete steps compound. You do not need to arrive with every credential or know every technical detail before beginning. What matters is developing the ability to connect your existing expertise with the emerging challenges AI governance is designed to address.
As organizations determine how AI systems affect employees, customers, communities, and society, the people around the governance table will help shape those decisions. Expanding who gets to participate in that work is therefore more than a career opportunity. It is part of building a more representative approach to the future of technology.