Women, Leadership, and AI: A Few Takeaways from Target Markets

By: Amy Cooper, RISE Founder & CEO

Ten years ago, about 40 women attended the women’s gathering at Target Markets. This week in Scottsdale? 220 seats filled, and they had to add more tables.

I love seeing that kind of growth.

The Women’s Leadership Group Lunch, sponsored by Allianz, was part of Target Markets’ 26th Annual Summit. This year’s conversation focused on something we’re all trying to figure out right now: AI.

Dawn D’Onofrio of RSUM, Laura Brown of ReSource Pro, and Rachael Hudson of Arrowhead Intermediaries took the stage to talk about what AI means for women in insurance, how it’s changing the way we work, and what we can do to stay ahead of it.

New to this corner of insurance? Target Markets is the trade association for program administrators. These are specialists, often operating as MGAs (managing general agents), who design, underwrite and manage insurance programs for specific niches on behalf of carriers. Think coverage built for a particular industry, profession or type of risk. It’s a part of the market that runs on deep expertise and close relationships, which made this conversation about AI and human judgment especially relevant.

There were some great points made, especially when the conversation turned to confidence, leadership, and the next generation of insurance professionals. Here are the ones that stuck with me.

The AI adoption gap

Laura shared that women are adopting AI at a rate about 25% lower than men.

Why? A few things came up: lack of encouragement from leadership to experiment, limited access to the tools, concerns about accuracy, ethics and security, and simply not having enough time to learn another tool.

That last one is interesting because saving time is one of the biggest benefits of AI. But you have to put in a little time upfront to figure out how to use it, and Laura was honest that most of that early stretch is trial and error.

The panel also pushed back on the idea that women are more cautious. In their view, women are often less risk-averse than they get credit for. When it comes to trying AI, that willingness to jump in is an advantage.

And that’s really where the panel encouraged everyone to start. Try something. See what works. Share it with someone else.

You don’t have to become the AI expert in your office overnight.

A better question to ask

Instead of asking “Will AI take my job?” the panel encouraged everyone to ask “How can I be better at my job because of AI?”

That’s a much more useful way to approach it.

Rachael made a great point about understanding your company’s business strategy before diving into AI. What are you actually trying to accomplish? What problems need solving? And can you explain that strategy clearly to someone else?

The panel’s view was that there’s no such thing as “AI transformation” on its own. AI should help you get to your business goals, not become another thing you’re doing just because everyone else is.

The good news is that getting started takes less time than it used to. Rachael noted that time spent “playing in the sandbox” has dropped dramatically. What once took around six hours of experimenting can now take about 20 minutes.

And you don’t have to start with some huge work project.

Dawn suggested taking a picture of your fridge and asking AI what to make for dinner. Seriously. Sometimes the easiest way to get comfortable with something new is to play around with it when the stakes are low.

The harder part, the panel admitted, is personal. Getting better with AI sometimes means letting go of habits that people have built part of their identity around. That takes intention, not just a new tool.

Don’t forget how to do things yourself

This was another part of the conversation I appreciated.

We’ve probably all received an email or read something lately that was very clearly written by AI, with little to no human editing. And it shows. The panel was blunt: when you see that kind of “AI slop,” you immediately lose some confidence in the person who sent it. It also creates more work, because someone has to go back and fix it.

There’s no way to fully rely on AI. If you use it for everything, how will you know when it’s wrong?

The panel also talked about how overusing AI can slowly chip away at skills you spent years developing. Laura called writing “a lost art” and encouraged everyone to keep spending time writing on their own. Rachael admitted she used to be meticulously organized, carefully naming her emails and attachments. Now she relies on AI to find things for her, and she’s watching for that kind of “skill erosion.”

There’s nothing wrong with making your work easier. That’s the point!

But you still need to understand what you’re looking at, recognize when something isn’t right, and bring your own experience and judgment to the work.

Especially in an industry like insurance, where relationships and trust matter so much. As the panel put it, that’s how we stay human: we’re here for relationships.

What about the next generation?

This might have been my favorite part of the discussion.

Kristen Swanson of AXA XL asked how we should be mentoring young women when AI is changing, and in some cases eliminating, the entry-level work that traditionally helped people learn the industry.

It’s a really good question.

Think about how much you learned early in your career just by doing the work. Researching, reviewing documents, organizing information, working through problems, and occasionally getting things wrong.

If AI handles more of those tasks, where does that learning happen?

The panel acknowledged that companies may hire fewer people because work moves faster with AI, but there are always steps that need a human. Those are exactly the steps where experience matters most.

Rachael’s advice was to encourage younger professionals to think through problems themselves rather than simply taking whatever answer AI gives them. A large language model needs you to supply the business context: which parts of a policy matter most, and why.

And for those of us who’ve been in the industry for a while, there’s an opportunity here too.

We have years of experience, context, and lessons learned that aren’t necessarily written down anywhere. We know why certain decisions get made, not just how to make them. Learning to explain that context, often through storytelling, may be one of the most valuable things we can offer.

That’s the stuff we need to be passing along.

It also reinforces something we talk about all the time at RISE: mentorship isn’t just about career advice or making connections. It’s about giving the next generation access to knowledge and experiences they wouldn’t otherwise have.

So, what’s next?

The panel left everyone with three simple challenges:

1. Try one new thing with AI. Pick a task and see what you can learn.
2. Show another woman what you learned. Help someone else get more comfortable using it.
3. Raise your hand. Look for an opportunity to lead an AI effort at work, even a small one.

Nothing complicated. Just start somewhere.

I think that’s a fitting message for a room that has grown from 40 women to more than 220 over the last decade.

Progress happens when people are willing to try new things, share what they’ve learned, and bring others along.

As we think about the future of insurance, especially how we develop emerging talent, those last two pieces matter just as much as the technology itself.

Our next RISE Mentorship Program cohort opens again in January, and conversations like this are a great reminder of why connecting experienced leaders with rising professionals is so valuable. Learn more at riseprofessionals.com/mentorship.

Were you at the Women’s Leadership Group Lunch? I’d love to hear what stood out to you.