AI trust – New consumer survey data shows women aren’t less interested in AI than men, but they are more cautious about its risks and less confident in AI-powered tools—especially in healthcare and financial services. A business leader argues the gap isn’t about educatio
When she was offered the CEO role at Smart Communications, the instinct was to refuse.
Not because the job looked unimportant. Because the foundation felt wrong. She believed a CEO needed depth across every department—product, engineering, strategy—and that she didn’t have enough of it.
What changed her mind wasn’t a pitch. It was trust—people she relied on insisting that her perspective wasn’t a weakness.
That’s the moment she says she has returned to again and again, especially as she began looking closely at one question: how women experience AI.
Across thousands of surveyed consumers, the pattern is hard to miss.
Over the past two years. her company surveyed thousands of consumers across healthcare. financial services. and insurance about their experiences and expectations. In every industry and geography covered, women weren’t less interested in AI than men. They were simply more cautious about its risks, and they showed lower levels of confidence in AI-powered tools.
The difference widened most in healthcare and financial services—sectors moving fastest to roll out AI in customer interactions that can carry real consequences.
The common takeaway from data like this is to close the confidence gap with more education, reassurance, and better onboarding. But she reads the results differently—especially as a female CEO.
Women’s caution, she argues, isn’t a misunderstanding. It’s a considered response to questions that go directly to accountability and transparency—questions about what happens to the person on the receiving end when something goes wrong. She says those are the same questions she spent her career learning to ask.
In her view, the industries deploying AI aren’t just making technical choices. They’re shaping consequential moments: how much of the claims process gets automated, how benefits changes get communicated, and how a financial decision that affects someone’s family is delivered.
Those interactions carry emotional and financial weight. Yet she says the people shaping these decisions often optimize for speed and cost. The question of how the technology lands for the person being served gets less attention. So does the question of who is accountable when it fails.
Women, she says, are precisely the ones equipped to ask these questions—particularly those whose careers have taught them to hold relational thinking and business needs at the same time. Still, she argues, those women are rarely in the rooms where AI strategy is built.
Here the data takes on a sharper edge. The World Economic Forum reports that women hold just 15% of executive AI roles worldwide. She calls it a strategic blind spot, not merely a pipeline issue.
Her point is not about innate differences. It’s about what happens when relational thinking is treated as “soft” for years—and what that does to perspective.
When relational thinking is undervalued. she says. people become trained to do something difficult but crucial: hold two requirements at once. The business requirement and the human one. They learn to ask not only whether something is efficient, but whether it’s right. They also develop a sense for the gap between how a decision looks on a slide and how it feels to the person it affects.
She describes that instinct as the foundation of every AI strategy.
Trust, she says, is the obstacle that often isn’t measured.
Consumers, she notes, are genuinely open to AI playing a bigger role in their lives. But trust keeps blocking adoption. People want to know someone is accountable for decisions made about them. They want to feel known rather than processed.
Measuring savings has become easier—time, cost, and headcount reductions. What gets measured less often, she argues, is what it costs when a communication lands wrong, when a customer feels like a case number, and when trust erodes quietly across dozens of small interactions.
Those costs show up anyway—in churn, complaints, and regulatory scrutiny. They may be harder to fit into a slide deck, but they are real. And they are the parts of AI strategy that can be missed when the people building it aren’t oriented toward the human on the receiving end.
Then comes the through-line she wants decision-makers to confront.
She accepted the CEO role at Smart Communications because the people she trusted told her her skills and experience were enough—and that her perspective had been quietly undervalued.
She recognizes that she is fortunate. Too many women, she says, don’t have someone in their corner making that case.
In her description, the problem isn’t personal. It’s structural. She says companies are investing heavily in AI tools while underinvesting in the talent mix needed to deploy them well. Women may be brought in to manage AI outputs, she argues, but rarely to shape the strategy behind them.
That becomes both a career pipeline issue and a business issue. And in most organizations, she says, AI strategies don’t include someone asking the questions that have to be asked—especially the questions that start with accountability.
The two problems—women’s greater caution as consumers, and women’s absence from strategic decision-making—are connected in her telling. The instinct that makes women more cautious consumers of AI also makes them valuable architects of it.
And solving the second problem, she says, begins with a simple reframe: treating women’s caution not as a gap in understanding, but as the signal it already is.
AI trust women in AI executive AI roles accountability transparency healthcare AI financial services AI consumer confidence Smart Communications World Economic Forum