Anyone who talks about AI today usually speaks in the language of acceleration. Faster. More output. Less friction. More scaling. That sounds like shiny dashboards, automated funnels and entrepreneurs who write a prompt with one hand and scroll over the chart of the next sales curve with the other.
But many women don’t use AI in this way. And female scanners certainly don’t.
You don’t sit down in front of ChatGPT or Claude to simply spit out “more content in less time”. You come with twenty open loops, three viable ideas, five half-finished thoughts and this one inner sentence: I know this could be important, but I can’t get it sorted properly.
This is where the real story of AI use by women in business begins. Not with the machine, but with the way a person uses it.
So the real question is not: Do women use AI less than men? The more exciting question is: Do they use it differently – and if so, is that perhaps precisely where its advantage lies?
What the data really shows
Research to date shows quite clearly that, on average, women use generative AI less frequently than men. In a Harvard Business School meta-analysis of 18 data sets with a total of 143,008 people, women were around 22 percent less likely to use generative AI. A BIS study found that 50 percent of men, but only 37 percent of women, had used generative AI in the past year.
This is not a marginal detail. But the mistake begins exactly where you want to turn it into a cheap character study: men courageous, women anxious. Men technical, women social. It’s not that simple.
The difference lies less in a core essence than in the conditions under which people use technology.
Women are more likely to report that they feel more insecure, that they need training before they can use the tool effectively, and that they think more about whether it is fair, legitimate or perhaps even a questionable shortcut to use.
That is not a weakness. It’s a different perspective.
Why female scanners have a special role in this debate
This is where scanners become interesting. The classic scanner doesn’t sit in front of AI like a calculator. She sits in front of it like a desk where someone is finally ready to sort the notes with her. Not to sort them. Not to standardize them. But to draw a recognizable line out of diversity.
While others ask: “How can I automate this?”, she often asks first: “What is the common thread here?“
That is no small difference. And it has more to do with business than many people think. Because a large proportion of female self-employment does not fail due to a lack of competence. It fails because there is too much skill in the room at the same time and no clear form emerges from it. Women know too much, can do too much, think in terms of connections rather than pigeonholes and still appear diffuse to the outside world.
AI then becomes an organizer rather than a production machine. Not as a substitute for thinking, but as an amplifier of structure.
What the difference means in practice
The most exciting dividing line is not between “male” and “female” in the biological sense, but between different logics of use.
The evaluation of real chatGPT usage of OpenAI shows that users with typically female first names write relatively more frequently on topics such as Writing and Practical Guidance, while users with typically male first names use Technical Help, Seeking Information and Multimedia relatively more frequently.
In business, it often translates like this: Many men treat AI more like a toolbox. Many women treat it more like a thinking space.
One asks: “Write me five sales emails for product X.” The other asks, “I have three offers that all feel right, but aren’t clear enough to the outside world. Help me find the intersection so that my target group immediately understands what I stand for.”
The first is output-oriented. The second is structure-oriented. Both can be effective. But for scanners in particular, the second type is often the more valuable.
1 AI as an organizer instead of an output machine
So the real point is not whether women are “nicer prompters” or men are “tougher”. Such caricatures are about as useful as a horoscope or lottery.
The more exciting difference is: What is AI even used for? As a recipient of orders? As a sparring partner? As a copywriter? As an analyst? As a mirror? As an organizer?
Scanners often use AI particularly powerfully where their own thinking produces not too little, but too much. For them, AI is not a band-aid for a lack of creativity. Rather, it is a channel through which an overflowing inner library finally becomes readable.
If you constantly see cross-connections, you don’t need more input. They need consolidation. Not another impulse. But a form.
That’s why the business value for scanners rarely lies first in “content in ten minutes”. It lies in completely different moments: when five ideas for an offer become a clear signature offer. When 42 notes become a sales narrative. When diffuse expert knowledge becomes a language that the market understands. When an avalanche of ideas finally becomes a sequence.
2. prompting: less command, more depth control
Let’s take two illustrative prompt examples:
- The first is: “Create a business plan for a coaching service for self-employed women.” That’s concise, direct and not wrong.
- Another is: “I support multi-talented, self-employed women who are very competent but don’t communicate clearly enough to the outside world what they are actually selling. Help me to formulate an offer that is immediately understandable without flattening my versatility. Show me three positioning options with advantages and disadvantages.”
The second prompt is longer, more contextual and more strategic. Not because women would biologically prompt in this way, but because many female scanners have learned that without context, all they get back is generic mush.
Another example:
- One prompt says: “Write me ten Instagram posts about productivity.”
- The other: “My target group is exhausted by general productivity content. They don’t need any more tips, they need clarity about why their business isn’t selling despite their expertise. Develop ten post ideas that make this tension visible without slipping into coaching platitudes.”
This is where the real difference becomes apparent: not hardness versus politeness, but surface application versus depth control.
3. why caution is not automatically weakness
Many scanners do not adopt AI blindly. They check. They turn the text over again. They ask questions. They want to know whether the suggestion is not only quick, but also coherent. It is precisely this attitude that is often misunderstood as hesitancy. In fact, it is often quality awareness.
And there are good reasons for this. Because AI is not neutral. A UNESCO analysis in 2024 showed clear gender stereotypes in large language models: Women were much more frequently associated with household and family, men with business, career and salary.
Those who know this use AI differently. I would say: soberly.
Entrepreneurs who work with language, perception and trust on a daily basis simply cannot afford to let a black box plow through their business unchecked.
There is also a second point that is talked about far too little: the social cost issue. Studies from 2025 show that people can be socially penalized for using AI. They are sometimes seen as less competent or less motivated if it becomes apparent that they have used AI. And it is precisely this penalty that seems to affect women more in some contexts.
Suddenly, AI is not just a productivity tool, but also a reputational risk.
4. the problem is not mindset, but environment
This is crucial for female business. If women use AI less often because they are less encouraged, receive less training or are judged more harshly, then this is not an individual mindset problem. It is a structural problem with a career effect.
In 2024, Deloitte not only found lower utilization rates among women, but also clear differences in perceived encouragement and further training on the job. Those who work under such conditions stumble not because they are incapable, but because stumbling blocks are placed in their way.
And that is precisely why it would be too short-sighted to read this text only as a gender comparison. For female scanners, the more exciting question is: what happens when they learn to use AI not as a foreign body, but as a strategic tool that finally supports their way of thinking?
How scanners can use AI more intelligently in business
Then it’s not just the output that changes. Then the relationship to one’s own versatility changes. Suddenly, not every idea has to be thought through immediately just because it’s there. They can be outsourced, sorted, clustered and tested. You can sit down with AI like a very fast, never offended assistant and say:
“Here are my twelve thoughts. Which three belong together? Which one has the clearest market connection? Where is the overlap between what I love and what people would buy immediately?” This is real self-management work.
In practical terms, this means
- Use AI not just for pretty formulation, but already during sorting.
- First collect the raw material, then build the structure, then sharpen it.
- Don’t accept every suggestion, but check: What is really mine? What sounds smooth, but not true? What is marketable but not coherent?
The most important AI competence for scanners in business is therefore not specialized technical knowledge. It is the ability to make judgments. Knowing when AI is allowed to structure and when it has to stop. When it should sort ideas and when it should dilute its own voice. When it can provide a framework and when the sentence must come from your own nervous system.
Conclusion: not more masculine use, but smarter
The future does not belong to the loudest AI users. It belongs to the cleanest. To those who know that speed is not automatically leadership. To those who have understood that trust is an economic factor. And those who neither demonize nor idolize AI, but lead it.
This is good news for scanners. Because their supposed weakness – not being one-dimensional, being constantly connected, having a fine sense for nuances – is not a flaw in the age of AI. It is an advantage. Provided they stop measuring themselves against linear user images.
You don’t have to use AI like a tech-loving early adopter to profit massively from it. They need to use it in such a way that their way of thinking finally becomes marketable, tangible and sellable.
The better question for the next few years is therefore not: Are women using enough AI? But rather: Under what conditions can women use AI in a way that serves them economically without stripping away their voice?
Those who find a good answer to this will not simply become more productive. They become clearer, more confident and harder to overlook.
About the author
Andreja Drozdan ist Business-Mentorin für vielbegabte Unternehmerinnen. Sie hilft Expertinnen dabei, ihr Angebot, ihre Botschaft und ihren Verkauf so zu schärfen, dass aus Können endlich ein klares, kaufbares Business wird. Sie schreibt über Positionierung, Angebotsklarheit, Verkaufspsychologie und die Frage, warum viele großartige Frauen nicht an mangelnder Kompetenz scheitern, sondern daran, dass ihr Business nicht klar genug verstanden wird. Sie arbeitet mit ambitionierten Frauen, die bereit sind, groß zu denken, klar zu handeln und ihre Vielbegabung endlich als Vorteil zu nutzen. Gemeinsam schärfen wir deine Positionierung, entwickeln dein Signature-Angebot und bauen ein Business, das sichtbar, strategisch – und vor allem profitabel ist. Meine Angebote sind keine Feel-Good-Spielwiese. Sie sind für Macherinnen, die Verantwortung übernehmen – für sich, ihre Wirkung und ihren Erfolg.
