Bridging the Gap: Leaders & AWS AI/ML for Success

AI and machine learning are appearing in nearly every customer conversation I have today. Whether the focus is personalization, automation, or optimization, the expectation is that we, as AWS professionals, not only understand the landscape but can guide our customers through it. As a Senior Engagement Manager, I’m not expected to build models. However, I am expected to understand how these services align with customer outcomes. Staying relevant in this space means learning continuously — and intentionally.

The AI/ML world on AWS moves super fast. Every few weeks there's something new—a new model, a better service, or a new tool. It's easy to feel like you're behind. I've felt that way too. All the new words and ideas can be a lot, especially if your job isn't hands-on tech. But what I've learned is this: you don't have to know every small detail. You just need to be curious, ask good questions, and focus on the parts that help you serve your customers better.

I started by being honest about what I didn't know. Then I just started asking questions—in meetings, in online groups, in direct messages. I didn't wait for formal training. I got hands-on when I could. I played with PartyRock, making simple AI demos to see how these new models could be used without writing code. I looked into SageMaker JumpStart to see how AWS makes the hard parts easier. I used Amazon Q and Perplexity to research faster, get quick summaries, and compare different ways of doing things. These tools weren't just helpful; they really changed how I learned in real-time.

I also leaned into the people side. I followed smart people like Andrew Ng on LinkedIn, connected with AWS Community Builders, and joined talks there. I didn't just read—I asked questions, challenged ideas, and shared what I was trying. I found that talking with others helped me learn much faster than doing it alone.

Certifications helped too. Not because I needed another badge, but because they gave me a clear path. They helped me connect AWS services to real AI/ML problems in the world. That clear understanding led to better talks with customers, not about features, but about what they could achieve.

If you're in a leadership role or you talk to customers, and you're not sure where to start, I'll say this: you don't need to become a data scientist. But you do need to get involved. You need to be able to talk about AI/ML in a way that sounds believable and makes sense. You can do that by trying out the tools AWS already gives you—especially the ones made for business and product leaders, not just for developers.

Start small. Build something in PartyRock. Watch a re:Invent session on Bedrock. Follow a few AWS leaders and community builders online. Get curious about what customers are trying to fix. Ask questions—even the really basic ones. This space moves fast, but the best way to keep up is to stay involved, not just watch.

We're in a time where AI/ML is changing how our customers work—and they're looking to us for leadership. Not just deep technical knowledge, but clear answers, direction, and results. If you're not sure how to help, start by learning out loud. Try the tools. Share what you're learning. Talk to people who are doing the work. This isn't just for engineers. It's for all of us who care about helping customers move forward, with the right tech and the right conversations.

Let's stop thinking of AI/ML as someone else’s job. Let’s make it part of how we lead.