Build better with AI: Lessons from real-world GenAI projects

From one-person startups to enterprise use cases, learn how to move from idea to implementation with generative AI.

Ben Heller, a consultant for transformational generative AI projects at Google Cloud, sat down with the All Things Open team to talk about how AI is reshaping what individuals and teams can accomplish. From building the NBC Olympics chatbot to guiding enterprise clients, Ben’s work sits at the intersection of technology, business strategy, and hands-on problem solving. As we enter the “agentic era,” he believes AI agents could realistically take on C-level roles inside startups, one-person start-ups.

Ben’s own journey into AI didn’t start with code, it started with curiosity and a push to get closer to technology. Coming from a business and sales background, he transitioned through roles at SAS, picked up certifications in Microsoft Azure, and eventually earned a master’s degree in applied analytics from Columbia. A piece of advice from a 30-year SAS veteran stuck with him: If you want to stay valuable in tech, keep moving closer to the technology. That guidance shaped his trajectory, and now it’s the advice he shares with others. There’s no single path into AI, but every step that builds your technical fluency opens new doors.

Read more: Going solo in tech: What this developer learned from starting a business

A big part of Ben’s current work is focused on use case decisioning, a structured process to prioritize which AI ideas are actually worth building. With pressure coming both from executives and grassroots efforts within organizations, teams often find themselves overwhelmed with possibilities. Ben helps narrow that down by evaluating business value, data feasibility, and ethical considerations. He emphasizes that the up-front planning work enables teams to move faster later by building repeatable, value-driven systems.

When it comes to tools, Ben uses Google’s Gemini platform regularly for both work and home projects. Whether identifying hardware issues or brainstorming AI use cases, Gemini’s multi-modal capabilities have become a go-to. He’s also keeping a close eye on video generation models like Veo, which he says are producing results that are “mind-blowing.” Outside of his work, Ben also supports local startups as an advisor through Raleigh Founded.

Editor’s note: Of course, at the pace of technology growth, the Veo 3 model has been released since this recording.

Key takeaways

1. There’s no one path into AI. Ben moved from sales to consulting by learning tools, earning certifications, and staying close to the tech.
2. Use case decisioning is essential. Success with AI starts by choosing the right problems to solve, and being strategic about what to build.
3. Start small, plan well, move fast. Discovery work up front helps teams build repeatable systems and avoid chasing hype.

Read more: How I use AI agents to automate my workflow and save hours

Conclusion

Ben’s story is a reminder that meaningful work in AI doesn’t require a traditional background, it requires curiosity, intention, and a willingness to stay close to change. Whether you’re starting out or scaling up, his advice applies: Get closer to the tech, make smart choices about what to build, and don’t be afraid to learn as you go.

More from We Love Open Source

• Going solo in tech: What this developer learned from starting a business
• Build a successful dev career with curiosity, community, and consistent practice
• How I use AI agents to automate my workflow and save hours
• Skip the crowded job hunt: Find your tribe instead
• Why AI won’t replace developers