Women in the Age of Artificial Intelligence: If We Do Not Use It, We Will Not Only Fall Behind — the System Will Be Shaped Without Us

One of the most interesting contradictions around artificial intelligence in Turkey is that individual curiosity and adaptation are high. People are trying tools such as ChatGPT, Gemini, Copilot, and similar systems; they are beginning to use them in daily life, asking them to write texts, summarize information, generate ideas, and help with planning. On the institutional side, however, it is difficult to speak of the same speed. Especially for SMEs, lack of expertise, cost, not knowing where to begin, and the tendency to see artificial intelligence merely as a “technological investment” create significant barriers.

When we look at this picture from the perspective of women, the issue becomes even more thought-provoking because artificial intelligence is not merely a new technology. It is a transformation that is reshaping ways of working, productivity standards, decision-making processes, visibility, and the very perception of professional competence. If women do not take sufficient part in this transformation, they will not simply be using one tool less. The work systems, production models, and even the artificial intelligence tools of the future will be fed less by women’s experiences, needs, and questions.

This is why, for me, the issue of women and artificial intelligence is not a simple call for “women to learn technology too.” As artificial intelligence settles into work and everyday life, I have a deeper question in mind: Will women be only the users of these new systems, or will they become actors who shape, question, manage, and transform them according to their own needs?

What does the world tell us?

The 2026 study titled “Global Evidence on Gender Gaps and Generative AI Over Time” is one of the most important current sources examining the gap between women and men in the use of generative artificial intelligence at a global scale. The study reviews 76 sources from more than one hundred countries. In the sources that allow comparison between women’s and men’s usage rates, it looks at a data pool is 318,924 people.

The result is clear enough to show that the gender gap in the use of generative artificial intelligence is almost universal and persistent. Men’s adoption rate of generative artificial intelligence is calculated at 47.8 percent, while women’s is 39.3 percent. This means that men use generative artificial intelligence about 22 percent more than women.

At first glance, one encouraging development is that this gap has narrowed over time. In 2023, when ChatGPT had only recently entered our lives, the gap was larger. After 2025, it narrowed. But it did not disappear. According to the study, the gap appears to have stabilized at around 16 percent. In other words, we do not have strong enough reasons to say, “Everyone will get used to it over time, and the issue will resolve itself.”

More importantly, the gap is not simply the result of women being less represented in technical professions. There is evidence showing that even when women are in the same profession, in the same institution, and have access to the same tools, they still use artificial intelligence less than men. For example, among software engineers at a global technology company, although everyone had access to the same artificial intelligence coding tool, male engineers used the tool at a significantly higher rate than female engineers. A study of occupations requiring artificial intelligence use in Denmark similarly shows that women use these tools less than men in the same professions.

This finding takes the discussion beyond the question of “access.” The barriers to women’s use of artificial intelligence are not only technical; they are also cultural, psychological, and institutional. Even when women do the same job, have access to the same tools, and work in the same environment, they may feel more hesitation when using artificial intelligence.

Lean In’s 2026 “Women and AI” research makes the workplace dimension of this hesitation clearer. According to the research, men use these tools more than women in work settings. While 33 percent of men use generative artificial intelligence daily or constantly at work, the rate among women is 27 percent. Moreover, the gap is not only about frequency of use; men also appear to be ahead of women in having tried these tools at least once in their professional lives.

But the real issue is not merely the numbers. Women approach this technology more cautiously. When they use it, they worry more that it may be perceived as “cheating,” question the accuracy of the output more, and carry more ethical reservations. They are also more likely than men to believe that women may face job loss because of artificial intelligence.

It would be unfair to read these reservations as “women being distant from technology.” Women often work under the pressure of not making mistakes, appearing flawless, proving themselves more, and not allowing their competence to be questioned. In such an environment, approaching a tool like artificial intelligence —one that requires trial and error, sometimes produces wrong results, and sometimes forces the user to ask the question again and again— may not feel as comfortable for women as it does for men.

Working well with artificial intelligence does not develop by writing the perfect first prompt. It develops by trying, making mistakes, asking again, criticizing the output, and placing one’s own expertise on top of what the machine produces. But for someone who feels they have less room to make mistakes, this process can naturally become more anxiety-inducing.

Of course, women’s relationship with artificial intelligence is not uniform. Education level, age, income, language skills, country of residence, whether one lives in an urban or rural area, care responsibilities, and digital literacy all shape this relationship in significant ways. A well-educated, urban, English-speaking woman who already works with digital tools does not have the same access to artificial intelligence as a woman with low digital literacy, a heavy care burden, or limited job security. This is why, when we talk about women’s participation in artificial intelligence, we need to look not only at the gap between women and men, but also at which women experience this gap more deeply.

The situation in Turkey: curiosity is high, but systematization is weak

Interest in artificial intelligence is also rising rapidly in Turkey. According to TurkStat’s 2025 Artificial Intelligence Statistics, 19.2 percent of individuals in Turkey state that they use generative artificial intelligence. When we look at gender, the rate is 19.4 percent among men and 18.8 percent among women. This data suggests that, at the level of individual use, the gap between women and men is not very large.

Yet this picture should not be read too optimistically because trying a tool as an individual is not the same thing as integrating it into work systems. A person asking a few questions to generative artificial intelligence tools does not necessarily mean that they are using them as systems that truly increase professional productivity, support decision-making processes, and transform workflows.

TurkStat’s enterprise data makes this distinction clearer. The share of enterprises using at least one artificial intelligence technology rose from 2.7 percent in 2021 to 7.5 percent in 2025. While the usage rate is much higher among large companies, it remains more limited among smaller businesses. This shows that individual curiosity in Turkey is not turning into institutional transformation at the same pace.

Kariyer.net’s 2025 research, “Artificial Intelligence in Turkey: Opportunities, Challenges, Expectations,” paints a similar picture. According to the research, two out of three employees say they use artificial intelligence in work processes, while 40 percent of companies state that they use artificial intelligence in human resources processes. Nevertheless, lack of knowledge and experience, concern over inaccurate results, data security, and resistance within company culture remain important issues.

SMEs are especially critical for Turkey because small and medium-sized enterprises make up a large part of the country’s economy. If used correctly, artificial intelligence can offer significant productivity and competitiveness advantages for these businesses. But lack of expertise, fear of cost, and the inability to answer the question “How can this be applied to our work?” slow down the transformation.

For women, this slowness matters even more because women’s participation in the labor force, their access to the entrepreneurial ecosystem, their representation in technology, and their visibility in decision-making mechanisms already proceed on unequal ground in Turkey. When artificial intelligence arrives on this ground, it does not distribute itself neutrally. It settles on top of existing inequalities.

Turkey’s artificial intelligence startup ecosystem is growing. According to TRAI’s 2025 data, the number of artificial intelligence startups in Turkey has reached 419. The 2025 Turkish Artificial Intelligence Ecosystem and Global Impact Report states that there are 1,188 active artificial intelligence startups in Turkey and that around 70 percent of these startups were founded after 2020. This picture shows that Turkey is moving toward becoming not only a user but also a producer in the field of artificial intelligence.

But the real critical question begins here: Where do women stand within this production ecosystem? Are women visible only as individual users of these tools, or are they sufficiently represented among startup founders, in product development processes, data science teams, strategy tables, ethical debates, language and user experience fields, and decision-making positions?

The fact that these questions are still not tracked in Turkey with clear and regular data is significant in itself because what remains invisible cannot be managed. We need to monitor women’s place in the artificial intelligence ecosystem, not only within general technology statistics, but separately and regularly.

Learned hesitation: the invisible threshold in women’s relationship with artificial intelligence

Women’s cautious approach to artificial intelligence is not rooted only in a lack of technical knowledge. In fact, many women believe in the benefits of the technology, see that it saves time when they use it, and know that it can make their work easier. Yet they may still experience an inner tension while using it.

Part of this tension comes from sensitivity to the risk of making mistakes. In professional life, women often have to prove themselves more. In work cultures where the same performance is not evaluated in the same way, where women’s contributions are less visible, and where mistakes are judged more quickly, it is not easy to experiment freely with a new technology.

Another layer of tension is the discomfort of “having the machine do it.” Women may worry more that their own labor, intelligence, and competence will become invisible. Getting support from these tools may sometimes feel not like a helpful opportunity, but like something that overshadows one’s own competence.

Yet using generative artificial intelligence effectively is not the same as handing the work over to a machine. On the contrary, it means organizing one’s expertise better, asking more precise questions, developing alternatives faster, and improving the quality of decisions. When used well, these systems do not remove the human from the process; they make the human’s knowledge, choices, and values more visible.

But for this to feel true, a cultural transformation is needed. Women’s use of artificial intelligence should not be seen as “taking the easy way out” or “compensating for a lack.” It should be recognized as a strategic competence. Workplaces also need to be clear on this matter: In which tasks can artificial intelligence be used? What boundaries should be preserved? How should the output be checked? How should work supported by artificial intelligence be evaluated? As long as these questions remain unclear, hesitation may become a stronger brake, especially for women.

If women fall behind, the system will be shaped without them

This is perhaps the most critical point of this article. Women falling behind in the use of artificial intelligence not only creates risks for their individual careers, but it also affects how these systems develop.

Artificial intelligence systems are not developed only in laboratories. They are also shaped by users’ questions, habits, feedback, and recurring needs. Whoever uses them more, experiments more, and directs the system more today will make the tools of the future more responsive to their way of using them.

If the early and intensive users of artificial intelligence are predominantly men, these systems will naturally draw more from men’s ways of working, problem definitions, priorities, and needs. Even if this is not a conscious exclusion, the result may be a sexist technology environment because women’s experiences, language, everyday burdens, ways of working, ethical sensitivities, and needs will not be sufficiently embedded in the system.

At this point, the question “Isn’t artificial intelligence already neutral?” is deeply misleading. No technology is completely neutral. Every technology carries the world of those who develop it, use it, provide data to it, and give feedback on it. If women are not sufficiently present in that world, the default human model of these tools is built incompletely.

We are already seeing everyday, very concrete examples of this bias. In its explainer on artificial intelligence and gender equality, UN Women cites the experience of Beyza Doğuç, an artist from Ankara, as a striking example. While researching for a novel, Doğuç asked artificial intelligence to write a story about a doctor and a nurse. The system made the doctor male and the nurse female. When she continued prompting it, the system repeatedly placed the characters into gender-stereotypical roles. This may seem like a small narrative choice, but it is in fact an important sign of which professions, roles, and qualities the system associates with which gender. The same UN Women article also cites an analysis by the Berkeley Haas Center for Equity, Gender and Leadership of 133 artificial intelligence systems, reporting that about 44 percent showed gender bias and 25 percent showed both gender and racial bias. This shows that the issue is not a single mistake, but a broader problem rooted in the data on which artificial intelligence is trained and in the social patterns carried by that data.

For this reason, women’s limited presence in the artificial intelligence ecosystem not only creates a lack of representation; it also narrows the system’s default perspective. If women’s questions, ways of organizing everyday life, invisible barriers at work, relationship to care labor, communication styles, ethical sensitivities, and production experiences across different sectors are not sufficiently carried into the system, artificial intelligence may appear to be designed for everyone while failing to grasp everyone’s life to the same extent.

This absence may also affect which areas of artificial intelligence develop more rapidly. Technical, coding, finance, automation, and productivity-focused uses may come to the forefront, while care, education, emotional labor, small business management, everyday organization, language, culture, ethical evaluation, and human relationships — areas where women often hold deep experience — may be treated as secondary. Yet artificial intelligence needs all of these areas if it is truly to become human-centered.

What women bring to the use of artificial intelligence is not merely numbers. Women bring different questions, different forms of attention, and different priorities. Questions such as “Whose work does this system make easier?”, “Whose labor does it make invisible?” “Which language does it treat as default?”, “Which user does it center?” and “Should this decision still remain with a human?” are not luxuries in the age of artificial intelligence; they are necessities.

Women’s greater presence in the artificial intelligence ecosystem matters not only for their careers but also for the quality of artificial intelligence itself. More diverse users and producers allow systems to see more forms of life experience. This contributes to the development of tools that are less biased, more useful, more reliable, and more human-centered. In other words, women’s presence in this field is not a “gesture of equality”; it is one of the conditions for producing better technology.

This is why women’s more ethical, cautious, and questioning approach to artificial intelligence should not be seen as a disadvantage. The real issue is preventing this caution from turning into non-use. Women need to use artificial intelligence critically but actively, because we cannot make a system more ethical by staying outside it. We can transform it only by entering it, testing it, seeing its limits, noticing its errors, and carrying our own needs into it.

The new gap in work life: usage gaps may turn into opportunity gaps

Artificial intelligence is creating a new layer of skill in professional life. This skill is not merely about “which tool you know.” The real issue is how you integrate artificial intelligence into your workflow, what questions you ask, how you evaluate the output, how you combine your expertise with the machine’s output, and how you make better decisions through this process.

These skills will turn into career capital over time. An employee who uses artificial intelligence effectively will be able to research faster, prepare better, offer more comprehensive suggestions, and become more visible. If two people doing the same job differ in that one uses artificial intelligence strategically while the other remains hesitant, the difference between them over time will not be merely a difference in speed; it will become a difference in thinking systems, production capacity, and access to opportunities.

If women fall behind at this point, a new digital skills gap may be added on top of existing wage and promotion gaps. Usage differences that seem small today may affect who is selected for greater responsibilities tomorrow, who becomes visible in projects, who is prepared for leadership positions, and who receives salary increases.

Moreover, a significant share of the occupations affected by artificial intelligence include fields where women are heavily represented, such as administrative work, communication, education, customer relations, office support roles, human resources, and content production. Therefore, women falling behind in the use of artificial intelligence does not only mean accessing new opportunities later; it may also mean having to watch from the outside while the technology transforms their own work. If transformation is applied to women-heavy fields from the outside and from above, while women do not actively participate in its design and use, artificial intelligence may be experienced not as an empowering tool, but as a new pressure and obligation to adapt.

Lean In’s findings are important for this reason. Women are not only using these tools less; when they do use them, they receive less recognition, less encouragement from managers, and feel more social risk. If this three-part structure does not change, telling women to “use artificial intelligence” will not be enough because use is not only about individual motivation; it is also about the permission, support, and recognition provided by the institution.

Companies also carry significant responsibility here. If artificial intelligence policies remain unclear, employees who already carry more anxiety about making mistakes will behave more cautiously. If the use of artificial intelligence is left only to technical teams or to the individual initiative of more confident employees, inequalities within the organization will grow. For women to participate equally in this transformation, clear rules, practical training, safe spaces for experimentation, and visible support from managers are needed.

Everyday life is also part of this transformation

The importance of artificial intelligence for women is not limited to the workplace. Everyday life is also part of this transformation. In many societies, women still carry a large share of household organization, children’s school processes, care labor, family planning, and an invisible mental load.

Artificial intelligence will not eliminate this burden on its own. If used poorly, it may even create a new performance pressure that expects women to do more work faster. But when used consciously, it can ease women’s mental load in areas such as planning, research, comparison, writing, organization, and decision preparation.

For a mother, artificial intelligence is not only a tool that answers a child’s homework question; it can also support the creation of learning plans, simplify resources, and offer different forms of explanation. For a woman entrepreneur, it is not merely an application that writes social media copy, but also a strategic partner that can interpret customer data, prepare proposals, improve product language, and build workflows. For an editor, translator, consultant, educator, or small business owner, it is not simply a machine that produces ready-made text; it can be a working space that organizes thought, multiplies options, and improves the quality of decisions.

This is why women’s use of artificial intelligence in everyday life also matters. But the purpose here should not be to make women carry even more burden; it should be to help them reclaim their time, attention, and decision-making space.

Especially for self-employed women, those working in creative industries, consultants, educators, translators, editors, and small business owners, artificial intelligence is becoming more than a productivity tool; it can also be a tool for visibility, business model creation, and economic independence. Some processes that once required larger teams, technical support, or higher budgets — creating website language, designing service packages, organizing customer communication, building a content strategy, preparing proposal texts, developing educational materials, or conducting market research — are becoming more accessible with the support of artificial intelligence. For this reason, artificial intelligence skills may become decisive not only in women’s current jobs, but also in the process of building their own professional fields.

What should be done? How can women’s effective participation in artificial intelligence be supported globally and in Turkey?

Preventing women from falling behind in the use of artificial intelligence cannot be solved through individual effort alone. Of course, it is important for women to try these tools, learn them, and adapt them to their own work. But research shows that the usage gap is not only about curiosity or lack of skills. Lack of knowledge and familiarity matters, but so do institutional support, encouragement from managers, social legitimacy, space to make mistakes, ethical uncertainty, and the question “How will I be perceived if I use artificial intelligence?” The solution therefore needs to be considered at the individual, institutional, and ecosystem levels.

Globally, the first step is to make the use of artificial intelligence a visible and legitimate professional skill area for women. It is not enough to tell women, “Learn artificial intelligence.” How these tools will be evaluated in the workplace, in which situations their use is considered ethical and appropriate, how outputs should be checked, and where human expertise stands in the process all need to be discussed openly. As uncertainty increases, women who already feel more pressure around making mistakes and being judged may act more cautiously. This is why institutions need clear artificial intelligence policies, practical training, and safe spaces for experimentation.

Second, the role of managers is critical. Lean In’s 2026 research shows that men are more likely than women to be encouraged by their managers to use artificial intelligence and to receive more recognition when they use these tools. This difference may seem small, but over time it can turn into gaps in skills, confidence, and visibility. For this reason, the use of artificial intelligence in companies should not be left only to technical teams or to the individual initiative of the boldest employees. Women should also be clearly supported in incorporating these tools into their workflows, good examples should be made visible, and the use of artificial intelligence tools should be evaluated not as “taking the easy way out,” but as a strategic way of working.

Third, the format of training needs to change. Instead of general and abstract artificial intelligence training, practical training that touches women’s own professional contexts and daily work would be more effective. Artificial intelligence means something different for a teacher, something different for an editor, something different for a human resources specialist, something different for an SME owner, a lawyer, a doctor, a designer, an academic, or a consultant. For women to truly adopt artificial intelligence, concrete answers need to be given to the question: “What does this tool change in my work?”

Fourth, women need to take part in the artificial intelligence ecosystem not only as end users, but also as designers, developers, product managers, data scientists, ethics experts, language specialists, process designers, and decision-makers because making artificial intelligence tools more inclusive is not only about diversity among end users; it is also about diversity in the production process. Women need to be more strongly present in the areas that develop, test, criticize, and improve these systems, and in the spaces where decisions are made about which problems are worth solving.

For Turkey, a few specific issues stand out within this broader framework. TurkStat’s 2025 data shows that the use of generative artificial intelligence has become visible at the individual level, while the rate of artificial intelligence use among enterprises remains limited. For this reason, the issue in Turkey is not only that individuals try these tools, but also how these experiments are carried into institutional transformation and work systems. Especially for SMEs, artificial intelligence is still often perceived as an expensive, technical investment that must be brought in from outside. Yet, when positioned correctly, artificial intelligence can become an applicable support system for small businesses in areas such as customer communication, content production, data analysis, proposal preparation, process tracking, training, and marketing.

For women in Turkey, one of the most important needs is that artificial intelligence training should not remain limited to the technology sector. For women entrepreneurs, self-employed professionals, translators, editors, educators, consultants, small business owners, human resources specialists, communication professionals, and women working in creative industries, artificial intelligence can be a direct tool for building and strengthening business models. For this reason, artificial intelligence programs for women in Turkey should expand not only under the headings of coding or technical expertise, but also in areas such as workflow building, personal brand development, customer management, content strategy, financial planning, educational design, and process improvement.

Finally, we need to measure women’s place in Turkey’s artificial intelligence ecosystem more regularly. Individual usage rates matter, but they are not enough. We need more detailed data showing where women stand as founders in artificial intelligence startups, in technical teams, product development, data science, language technologies, ethics and regulation, consulting, and SME transformation, because what remains invisible cannot become stronger. If women’s place in this transformation is measured, made visible, and supported, artificial intelligence in Turkey can become not only a technological transformation but also an opportunity for a more inclusive work and production culture.

Women should not wait for artificial intelligence; they should build their place with it

Artificial intelligence transformation is no longer a technology agenda that can be watched from the outside. It is entering work, production, decision-making, communication, and everyday life. How this transformation takes shape will depend on who uses it, who questions it, and whose needs are embedded into the system.

If women remain hesitant in their use of artificial intelligence, they will not simply be using a technology less; they may face much deeper consequences such as becoming less visible in the work systems of the future, falling behind in new skill standards, seeing wage and promotion gaps widen and encountering artificial intelligence tools shaped more around male experience.

But the opposite is also possible. If women use artificial intelligence actively, consciously, and critically, this technology will not merely become a tool that increases men’s productivity advantage. It can turn into a field of transformation that strengthens women’s work, helps them build their own economic spaces, eases their everyday burdens, and contributes to the creation of more inclusive systems.

This is why the question today is not whether women will use artificial intelligence. The question is with what confidence, what ethical attention, and what system-building ability women will integrate this technology into their lives because if artificial intelligence is writing the language of the future, it is not enough for women to be represented in that language. They must be among those who build it.

References

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Lean In. “Women and AI: The Gender Gap in AI Adoption and Recognition.” 2026.

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UN Women. “Artificial Intelligence and Gender Equality.” Explainer, 2024.

Berkeley Haas Center for Equity, Gender and Leadership. Analysis of 133 AI systems, as cited by UN Women.