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April is AI Awareness and Literacy Month, a time to reflect on how artificial intelligence is reshaping the way we work, learn, and solve problems. At CAI, our Data & AI Services Team is in the middle of that shift every day, helping clients move from AI curiosity to real, measurable adoption and understanding.

Whether you're exploring a pivot into the AI space or already on the path and wondering where you fit, this one's for you.

In this segment, we chat with Mariel Vargas, CAI's Lead AI Consultant about what a career in Data & AI looks like and advice she'd offer to anyone considering a career in this space.

Q). For someone just learning about CAI's Data & AI Services team, how would you describe the work your team does and the value it brings to clients?

The Data and AI Services team helps organizations figure out how to use data and AI in ways that work inside their business. Not just technically, but operationally.

The work covers four areas:

AI strategy and advisory: Where we help clients understand where they are today, assess their AI maturity across people, process, technology, data, and governance, and build a realistic roadmap.
GenAI and platform services: Where our architects and engineers deliver end-to-end solutions, from platform integration to scalable AI architectures.
Digital workforce enablement: Where we focus on training, change management, and adoption programs, helping people move from knowing a tool exists to actually using it in their daily work.
Intelligent data and insights: Where we help organizations modernize their data environments so they are trusted and ready to support AI.

What I find most meaningful about this work is that we do not just hand over a solution and leave. We stay involved when resistance shows up, when adoption is slower than expected, when the original plan needs to adjust. That is where the real work happens. And something I appreciate about CAI is that we do not only advise clients on AI adoption from a distance. We live it internally first. The methodologies we bring to clients are ones we have tested on ourselves.

Q). What does a typical day or week look like for someone on this team?

It depends on which project you are working on and where you are in it. No two weeks look the same!

Every project starts with a discovery phase. Before we build or recommend anything, we spend real time understanding what the client actually needs, what their current state looks like, and what is driving their interest in AI. From there, the work always involves connecting across different stakeholders, communicating with the client, and coordinating multiple workstreams at once.

For me, most of my work lives in change management and adoption. On any given week I might be building a change plan, designing training sessions, or crafting communications to help people understand what's coming and why it matters. Some days are very focused and heads-down. Others are back-to-back conversations across different levels of an organization.

Q). What's a recent project or win that your team is really proud of? What made it so impactful?

The most recent big project for me has been leading the internal rollout of Claude Enterprise at CAI. We had licenses to deploy across the organization, but the real work was never about the technology. It was about helping people understand why it mattered and how it connected to their specific roles.

We built the change management strategy from the ground up with stakeholder interviews, tailored training sessions, a Champions network, and a lot of conversations with people about how to get the most value out of the tool for their specific role. What made it feel like a genuine win was watching the shift in mindset. People started bringing their own use cases to us. That transition, from "how should I be using this?" to "here is what I want to do with it," is what real adoption looks like.

Over 90% of users reported daily or near-daily usage, and more than half were saving meaningful hours every week. Those numbers mattered, but the conversations behind them mattered more. This rollout also became a proof of concept for how we guide external clients through the same journey.

Q). How does collaboration, both within CAI and with clients, play a role in delivering Data & AI solutions?

Everything we do involves collaboration. There is no version of this work that succeeds without it.

Internally, we work closely with developers, architects, and delivery leads. The change management perspective has to be grounded in what's technically possible, and the technical team needs the human context to build something people will actually use.

On the client side, I am often working with executives and frontline employees at the same time, which requires a different kind of listening in each direction. Executives care about risk, return, and strategic fit. Frontline employees care about whether this is going to make their job harder or easier. Both perspectives have to be addressed for a project to succeed.

Q). What skills and backgrounds do you look for in someone joining the Data & AI Services team, and how do you help them grow once they're here?

Passion and curiosity matter more than people might expect. I’ve worked alongside people from very different backgrounds, including IT, software development, communications, and project management, and each one brought something that made the work sharper. That said, this is a consulting team, so regardless of your technical depth, you need to be comfortable working directly with clients. That means asking good questions, listening carefully, and being able to communicate complex ideas clearly to different types of audiences.

Technical fluency matters and it varies by role. On the architecture and platform side, you need solid hands-on experience with cloud environments and AI solutions. On the advisory and enablement side, the ability to sit with ambiguity and navigate organizational complexity is just as important as knowing the technology.

My best tips for getting acclimated and focused on growth is to:

Expand your network early: When I joined, I set a goal of meeting one on one with 20 people in the first month and asked my manager to recommend names. That ended up being one of the best things I did.
Connect with your colleages: CAI has ERGs and communities that are easy to plug into, and in my experience, people here are genuinely willing to extend a hand and help you find your footing.
Align with your manager on career goals from the start: I have felt extremely supported in that way, and I think that comes from being intentional about those conversations early on.

Q). How do you stay current in a field that's evolving as quickly as AI and data?

Honestly, this is one of the real challenges of working in this space. The field moves faster than any one person can keep up with alone, so how you approach it matters.

The first thing I do is align with my manager on which technologies are most relevant to my current projects and overall role, and that helps me be intentional about where I invest my time. From there, it's a mix of reading daily industry content, learning courses, and working toward certifications in the field.

But honestly, some of the most valuable learning happens within the team. We have a weekly coffee chat where we share learnings and updates with each other, and that informal exchange often surfaces things I would not have found on my own. Team collaboration is underrated when it comes to staying current. In a field this fast-moving, you cannot do it alone.

Q). What advice would you give to someone who wants to build a career in Data & AI services?

Do not wait until you feel ready. In this field, that moment may never come. The people who build strong careers here are the ones willing to learn in motion, adjust as they go, and be honest about what they do not know yet.

Find the intersection between your existing skills and AI. If you come from communications, think about what AI adoption communication looks like. If your background is in project management, think about how AI is changing how delivery works. You do not need to start from zero. The skills you already have are more relevant than you might think.

Show your passion because people notice when someone is genuinely curious and excited about this work, and that energy opens doors.

Seek out professionals in this space who inspire you and connect with them to build your network intentionally. There are also incredible free resources available, courses, communities, content, that can get you very far if you are willing to put in the time.

And probably most importantly, AI is open and available right now. Use it every day. Become a bit of a nerd about it and figure things out hands-on. That is genuinely the fastest way to learn. The people who will thrive in this field are not just the ones who understand AI theoretically. They are the ones who are in it, experimenting with it, and pushing its limits every day.

If Mariel's conversation has you thinking about what a career in Data & AI could look like for you, CAI is a great place to explore that path. Join our talent community to stay in the loop on company news, events, and open roles. Whether you're pivoting into AI or looking to grow deeper in the space, there's a place for you here.