There’s a gender gap in generative AI use. According to research published by Harvard Business School, women adopt artificial intelligence at lower rates than men. Many have interpreted that statistic as hesitation. But what if adoption rates are the wrong measure?
In my work teaching and coaching executives, I’ve noticed something different. Many senior-level women aren’t slower to adopt AI because they’re resistant to technology. They’re more deliberate about how they use it. They don’t begin with AI. They begin with themselves.
That distinction matters.
AI doesn’t understand what matters most in a particular situation. It doesn’t know an organization’s history, political realities, stakeholder relationships, or the human consequences surrounding a decision. Experienced leaders recognize that judgment is their responsibility. AI use that showcases this wisdom will expand, challenge, and strengthen their thinking without ever replacing it.
Across dozens of leaders, I’ve observed the following four habits in executive women’s AI use that consistently produce better outcomes:
1. Define the problem before asking AI to solve it
The most effective AI users don’t start by asking the tool what to do. They clarify the problem they’re solving, and then define what success would look like.
Recent research from both McKinsey and Boston Consulting Group reinforces this approach. While the research from each is independent of the other, both organizations conclude that AI initiatives are far more likely to create value when leaders begin with a clearly defined business problem and establish measurable outcomes before using the tools. Organizations that start with the AI tool often struggle to move beyond experimentation because they never determine how they will evaluate success.
Lisa, a senior risk executive at a private equity firm, faced a difficult conversation after reporting a colleague for violating compliance rules. Before opening her AI tool, she defined her objective: remain professional while setting firm boundaries. Only then did she ask AI to suggest approaches that were respectful but direct.
The difference wasn’t a better prompt. Lisa had already done the thinking AI couldn’t do. She knew what success looked like and could judge AI’s suggestions against her own objective.
So, before turning to AI, ask yourself two questions: What problem am I actually trying to solve? What would success look like?
2. Use AI to build relationship intelligence, not just documents
Many senior women leaders use AI for something far more sophisticated than drafting emails. They use it to gain a better understanding of the people around them. Over time, they built a living record of stakeholder priorities, communication preferences, concerns, and lessons that they learned from previous interactions. Rather than approaching each conversation as a blank slate, they used AI to identify patterns, anticipate reactions, and bring to the surface perspectives they might have overlooked.
Emerging research supports the underlying capabilities behind this approach. A recent study published in Science found that AI can help people synthesize competing viewpoints, identify common ground, and uncover perspectives that individuals may struggle to articulate independently. Microsoft researchers similarly found that AI-assisted reflection helped people prepare more thoughtfully for important meetings by encouraging them to reconsider assumptions, anticipate challenges, and adapt their communication.
Two important cautions remain. For security purposes, make sure to anonymize personal or confidential information. And because today’s AI memory systems are still imperfect, leaders should regularly review and update the information they store.
Anya, who leads process management at an international pharmaceutical company, maintained an evolving record of senior stakeholders, documenting their priorities, past concerns, and communication styles. Before introducing a company-wide technology initiative, she asked AI to help her think through how each executive was likely to experience the proposal and what competing priorities they might bring to the discussion.
Rather than asking AI what to say, she used it to tailor how she presented information. This way, her specific shareholders are more likely to respond favorably.
3. Let AI prepare while keeping judgment for yourself
Experienced leaders understand that gathering information and making a decision are different jobs. AI can organize research, identify patterns, summarize evidence, and generate alternatives. It can’t determine which trade-offs are worth making or which consequences are acceptable.
Research supports this division of labor. A study of more than 100 strategic business decisions found that decision quality was highest when analytical reasoning and intuition worked together, particularly in dynamic environments. Research published in Nature Reviews Psychology similarly concludes that AI is most useful as a decision-support tool and not as a substitute for human judgment. That’s because its outputs reflect distinct biases, reasoning failures, and other limitations.
Rolanda, a major gifts officer at a university, used AI to organize research, identify themes, and refine early drafts of an important donor report. But she deliberately stopped short of letting AI write the final version. The report wasn’t simply informational. It reflected years of trust, shared history, and personal connection. Only she could determine what deserved emphasis and what tone would strengthen the relationship.
AI prepared the work. Rolanda decided what she was willing to stand behind.
4. Don’t ask AI to agree with you. Ask it to challenge you
Perhaps the most sophisticated use of AI I’ve observed is asking it to disagree.
Decades of decision research have shown that deliberately exposing assumptions to challenge leads to better decisions, and now studies incorporating AI show that it makes that discipline available on demand. The tools offer counterarguments, question assumptions, and can introduce alternative viewpoints that unlock more critical thinking and make it less likely to conform to dominant opinions.
Rather than asking AI to validate an idea, experienced leaders ask it to identify overlooked risks, construct the strongest case against a proposal, or explain why a reasonable colleague might disagree.
Caitlin, a government official leading a proposal for a new water treatment plant, used AI exactly this way. She asked it to challenge her assumptions and identify concerns she’d failed to anticipate. The resulting counterarguments strengthened both the proposal and her communication strategy before objections surfaced publicly.
The real advantage isn’t better AI—it’s better human judgment
Business leaders often measure AI success by adoption rates, productivity gains, or speed. Those metrics matter, but they miss something more important. The leaders getting the greatest value from AI aren’t the ones handing over their judgment. They’re the ones using AI to sharpen it.
The future competitive advantage won’t belong to the people who ask AI the most questions. It will belong to the people who know which questions only they can answer.
