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If you think you need a traditional engineering résumé to lead in AI, Crystal Collins’ story will make you think again. A first‑gen high school and college graduate from rural Virginia, Crystal built her career in service, sustainability, and systems change long before she ever worked with machine learning models.

Today, she’s a Senior Director of Strategic Programs & AI Enablement in Texas, helping teams adopt AI in ways that actually align with their values.

For International Women’s Day, BetterEngineer asked Crystal how she found her way into tech, how she handles feeling “not technical enough,” and why more women in AI isn’t just about representation, it’s about the kind of future we build.

1. Origin story: From rural roots to systems thinking

Q: Let’s start with your story. How did you get from where you began to working in AI and tech?

I didn’t start in tech at all. I grew up in rural Virginia, studied anthropology in college, and then moved into the nonprofit world.

For about 10 years, I worked in education and community-oriented nonprofits. A lot of my early work was running after-school programs for high school students, including bringing teenagers to soup kitchens, cleaning up cemeteries, visiting nursing homes, and cooking meals for people awaiting organ donations. Service was a big part of my early career and my identity.

There were also some really unique opportunities. I helped take students to build schools in places like Malawi and Haiti. Those experiences were formative. They grounded me in impact, sustainability, and this question of: “Are we actually making a real difference?”

Over time, I found myself gravitating toward systems and operations. I supported our first implementation of Salesforce at one organization, then led another implementation at a smaller nonprofit. I was putting new systems and processes in place and realized I really liked that systems-level work, seeing how everything connects, and how better structure can unlock more impact.

That mindset (systems, impact, sustainability) translated naturally into tech later on.

2. Pivoting into sustainability, business, and tech

Q: What was the turning point that moved you from nonprofit work into tech and AI?

After about a decade in nonprofits and education, I decided to get my MBA. I went to business school full-time, focused on sustainability and entrepreneurship, and did an internship with a consulting firm that specialized in ESG for private equity firms.

When I started recruiting, I was looking at consulting roles and came across a technology company that, interestingly, had sustainability at the heart of its mission. That really appealed to me. Even though I didn’t have a classic “tech background,” I saw an opportunity to combine sustainability, systems thinking, and technology.

In that role, I helped build a sustainability consulting group for manufacturing clients and led an internal sustainability resource group, supporting employees who cared about impact and wanted to contribute, even if it wasn’t in their formal job description.

I also worked on an R&D project using digital twins to improve bus route planning for public transportation. That was a great example of where tech and impact meet: using advanced tools to solve real-world problems in more sustainable, efficient ways.

Across all of this, I was mapping customers’ sustainability goals, understanding where the market was struggling, and then connecting that to the technologies and expertise available. A lot of my work was storytelling: “Here are your goals. Here’s where the industry is stuck. Here’s how our capabilities can actually help you move the needle.”

3. What “impact” feels like in day-to-day work

Q: You’ve mentioned being very driven by impact. What does making a “measurable impact” mean to you now?

There’s the quantitative side: being able to measure things like volunteer hours, reduced emissions, or improved efficiency. That matters.

But there’s also this gut-level feeling you get when you know what you did that day mattered. Earlier in my career, I’d literally get butterflies riding the elevator after a good day, those moments where you know you did the right thing and made a difference.

In my role now, everything around AI is so new. I work on change management and enablement for AI, which means being very thoughtful about how we introduce it: understanding how different people feel about it, what they’re afraid of, what excites them, and how to align AI adoption with the company’s values.

Our experience at work takes up most of our day. It matters whether people feel discardable or empowered. Helping create an environment where people feel they can do their best work, and that the work aligns with their values, that’s impact, too.

On the sustainability side, I’m still very much involved. For example, I’ve been working with our AI “garage” to pilot sustainable software development standards and embed them into coding and code review processes. The idea is to reduce the carbon emissions associated with the applications we build. That’s a more technical, behind-the-scenes kind of impact, but it’s real.

4. Helping non-technical teams move from fear to curiosity 

Q: Many non-technical teams feel intimidated by AI. What helps people move from fear to curiosity and participation?

One of the first projects I had in my current role was launching communities of practice within our technology group. Those communities have been a powerful, ground-up way to demystify AI.

Leaders in those groups are very open about how they use AI. They’ll start meetings with an icebreaker like, “Use this model to do a fun prompt and share what you got.” It creates a low-stakes, playful entry point.

Two things really help:

  1. Transparency: In one of my previous roles, nobody talked about using AI. It almost felt like a secret you didn’t want anyone to “catch” you using it. That creates fear and stigma. Being explicit about when it’s okay to use AI, and having leaders openly say, “I used AI for this part of my process,” normalizes it. People realize it’s not taboo.
  2. Clear guardrails: A lot of people are afraid they’ll do something wrong. Governance is critical, not just for risk management but for psychological safety. When people understand the guardrails—where AI is appropriate, where it isn’t (for example, in certain types of client work), and what the rules are—they can work more freely instead of being paralyzed by “what if I mess up?”

So we talk about it openly, model healthy use, show fun and useful examples, and make it clear: “Here’s where AI fits. Here’s where it doesn’t. Here’s how we keep it safe and aligned with our values.” That combination tends to move people from fear to curiosity.

5. Being a woman in tech and AI: leadership, allies, and representation

Q: What has your experience been like as a woman working on AI and strategic programs in large, tech-driven organizations?

One of the things that drew me strongly to my current company was seeing a woman in the CEO role and a leadership team with a significant number of women. That kind of top‑down representation matters. You feel it in the culture and in who gets heard.

I’ve also been fortunate to have strong male leaders who recognized my potential, advocated for me, and made sure I had access to opportunities. That’s not something everyone experiences, and I don’t take it for granted.

We talk a lot about women lifting up other women, which is important. But in male‑dominated fields like tech, we also need men who are intentional allies—who look for female mentees, who can see beyond gender to potential, and who actively create room for that potential to grow.

Q: Why is it especially important, right now, to have more women in tech and AI?

This isn’t a scientific claim, but I’ve seen that women often carry and execute on values in a very tangible way. Right now, in the era we’re in with AI, we need people who are community‑minded and willing to think beyond the immediate ROI.

We need people asking, “What does this mean for our communities? For inclusion? For the long‑term?” More women in tech and AI increases the odds that these questions actually get asked and acted on.

6. The “permission” mindset: advice for women entering AI

Q: If you could give one mindset shift to women working in or entering the AI space, what would it be?

Give yourself permission.

It sounds like something out of a book, but it’s true: the people who move fastest and furthest are often the ones who don’t wait for permission. They see something they want to do, and they just start.

I still have to catch myself. My brain will go to, “Should I check with someone? Do I need approval?” and I have to remind myself: “You don’t need permission. You just need to do this.”

If you notice yourself waiting for someone else to validate your idea, or say “yes” before you act, that’s the moment to write your own permission slip. That’s where you accelerate.

7. “Not technical enough”? Rethinking who belongs in tech

Q: You didn’t come from a traditional tech background. Have you struggled with feeling “not technical enough” in tech spaces?

Definitely. Early on, it was intimidating to be in rooms full of very technical people. I didn’t speak up much at first because I didn’t feel like the most technical person there.

Eventually, I realized two things:

  1. You don’t have to be “the technical one” to belong in tech. There are so many roles that are essential and not primarily technical, like understanding business users, mapping processes, working across stakeholders, and articulating a clear vision. Those are all things I bring to the table.
  2. New fields level the playing field. I saw this first with sustainability. It was evolving so fast that no one really “knew everything.” Because I focused on it deeply for a couple of years, I quickly became one of the internal experts.

The same thing is happening with AI. A lot of people still don’t truly understand it. If you’re willing to put in the time and curiosity, you can know more than 50% of the room pretty quickly.

So yes, the intimidation is real, but it doesn’t mean you don’t belong. It just means you’re in a space that’s still defining itself, and there’s room for many kinds of expertise.

8. Beyond job postings: advocating for yourself differently

Q: Many women and underrepresented groups self‑select out of roles if they don’t meet 100% of the requirements. What have you learned about navigating that?

I’ve read the same research...that women and other underrepresented candidates often won’t apply if they’re missing one requirement.

What I’ve learned is that you sometimes have to bypass that system altogether.

It starts with getting very clear on what you do well and what you want to be doing. Then you advocate for yourself outside the application portal: building relationships, finding sponsors, and having real conversations with people who can champion you.

That was a hard mental leap for me at first. It felt more natural to just follow the formal process and hope the system would see me. But a lot of growth has come from stepping outside that and saying, “Here’s what I can do. Here’s where I want to contribute,” and inviting people to meet me there.

It’s still a work in progress (for me and for many of us), but it’s powerful.

9. What it means to lead in AI as a first‑gen woman

Q: Personally and professionally, what has it meant to you to be a woman leading strategic programs and AI enablement in large tech-driven organizations?

I think about this a lot less in abstract terms and more through the lens of my own story.

I was the first in my family to graduate high school and the first to go to college. So when I look at where I am now, it feels like a deeply personal journey.

It has taken a lot, mentally and emotionally, to get here. There’s the identity shift, the constant mindset work, the need to challenge your own limiting beliefs over and over again.

I try to stay grateful and thoughtful about it. I try to support other women because the mindset challenges are so common. We all have those inner narratives that tell us to shrink back or stay quiet, and we need to gently but consistently push against them, both for ourselves and for each other.

That’s what it means to me: holding the weight of my own story, staying grateful for the opportunities, and using my position to make it a little easier for the next woman who comes through.

Why Stories Like Crystal’s Should Rewrite Who We Think “Belongs” in AI

Crystal’s journey is a reminder that AI doesn’t just need more code; it needs more courage, more values, and more voices that didn’t grow up thinking they belonged in the room. Her story echoes what so many women in tech quietly live every day: navigating intimidation, rewriting internal scripts, and learning to stop waiting for permission.

As we celebrate International Women’s Day, BetterEngineer is proud to amplify leaders like Crystal who prove that inclusive, impact‑driven engineering cultures aren’t a nice‑to‑have. They’re the only way we build AI that truly serves people.