How to Make a Successful Career Pivot, According to a Global Leader

Jimmy Oribiana, Global Lead of Artificial Intelligence (AI) at Hitachi Energy, didn’t always work in AI. While he transitioned from Sales Operations, however, he says that the career change aligned with how he’s always operated as a systems-and-process thinker with an engineering mindset.

“What I had to learn was essentially a new language: the terminology, methodologies, and workflows that connect business needs to AI development,” he tells The Muse. “The biggest challenge wasn’t the technology; it was learning how to communicate its value across a large enterprise with established structures, processes, and legacy systems. AI moves incredibly fast, but organizations can take longer to adapt. Bridging that gap is both difficult and critical.”

What motivated Jimmy was the complexity of it all.

“I’ve always been driven by solving the toughest challenges, and throughout my career, each role has been a building block that led me to this point,” he explains. “The doubts were real, but so was the opportunity to shape something meaningful.”

Today, Jimmy develops the roadmap for deploying AI across Hitachi Energy’s Marketing and Sales function. His focus is on uncovering where AI can unlock value for the company’s customers, employees, and partners—and identifying the right use cases within each subfunction. From there, he works with the technical delivery team to break down requirements and determine appropriate solutions, some of which call for complex multi-agent systems.

“Leading AI globally means balancing innovation with practicality—ensuring that the solutions we develop are scalable, aligned with strategy, and rooted in real business needs,” Jimmy says. “It’s about building a roadmap that serves the organization today while preparing it for the future.”
Here, Jimmy shares more about his career pivot into the AI space, the valuable career lessons he’s learned along the journey, and his advice for other professionals who are actively exploring emerging fields like artificial intelligence.

I started my career in 2008 with Hitachi Energy as part of the Engineering Training Program—now known as Global Power+. Once graduated from the Development Program I rotated through three cities—Lake Mary, FL; Raleigh, NC; and Montreal, QC—which gave me the chance to see different parts of the business up close.

Coming out of university, it’s difficult to know exactly where you want to focus. The flexibility of the Power+ program was exactly what I needed because it exposed me to different roles, teams, and perspectives. Looking back, that experience laid the foundation for how I approach problems today: understanding systems, people, and processes holistically.

During my second rotation, I stepped into a market analyst role for eight months, and that’s where I really began developing my skills with data. I was tasked with building a linear regression prediction model for the global transformer market—an opportunity that connected me directly with Marketing & Sales leadership across multiple factories. This model provided an explanation of the market based on real data. It allowed me to apply what I had learned in statistics and see how data can influence real business decisions.

I didn’t realize it at the time, but that project was the first stepping stone toward the career I have today. It showed me the impact of combining analytical thinking with business understanding.

I’ve worked with data for most of my career, but about five years ago, I realized I was missing key tools in my toolbox. I was building increasingly complex data models for reports and dashboards because our standard reporting wasn’t capturing the true reality of what was happening in the business. These models helped, but they were still designed for human interpretation.

I wanted to take the next step—understanding how AI systems could automate insight, not just visualize it. I also knew I lacked the foundational skills—programming, software development, and the fundamentals of AI. So I went back to school to build that base from the ground up.

Every five years, I reassess my career and ask myself, “What am I missing?” Once I have the answer, I work with my managers to build those skills. That mindset has guided each major pivot in my journey, including this one.

There are many roles in AI—solution architects, engineers, data scientists—but one role has become especially important in large enterprises: the bridge between technical delivery teams and key decision-makers. Having spent 17 years in the organization, I understand how our processes, systems, and people operate. That experience helps me navigate complexity, align stakeholders, and ensure that AI initiatives actually solve the problems they’re intended to solve.

Being able to speak both “languages”—the technical details and the business context—is critical. Otherwise, AI solutions miss the mark, and that’s a big reason why so many projects fail to deliver value. My background in data, project management, and process leadership has made the transition into AI feel natural because AI ultimately needs to support people, not replace them. Bringing users into the development process has been key to driving impact.

The most rewarding part of my work is working with our people to deploy tangible impact that AI can have when it’s deployed thoughtfully and responsibly. What motivates me is to focus on use cases that truly solve a business problem and help teams feel the difference in their daily work. When processes flow better, when customers experience more value, it’s incredibly fulfilling.

AI is not just a technical capability; it’s a catalyst for transforming how people operate, collaborate, and make decisions. Being able to contribute to that transformation on a global scale is something I feel privileged to do.

The most challenging part is navigating the complexity of large enterprise environments. You’re dealing with legacy systems, established processes, diverse stakeholders, and competing priorities—all while the AI landscape shifts almost weekly. Translating fast‑moving technology into realistic, actionable strategies that work within those constraints requires constant alignment, communication, and patience. Balancing innovation with organizational readiness is both the hardest and most essential part of the job.

My favorite thing about working at Hitachi Energy is how much our company's mission aligns with my own ideals. We’re not just building technology; we’re helping power the world and accelerate the energy transition. Being part of a company that combines deep industrial expertise with a real commitment to innovation creates an environment where meaningful impact is possible.

I’ve also had the chance to reinvent myself multiple times here. Over 17 years, I’ve moved across functions, countries, and disciplines. That level of trust and mobility is rare in large organizations, and it’s something I value deeply. The company has always encouraged me to grow, explore new areas, and take on complex challenges—ultimately enabling me to step into the world of AI.

One of the most valuable lessons I learned early on is that expertise is built, not inherited. When I first transitioned from engineering into data, and later from sales operations into AI, I often worried that I wasn't “technical enough” or didn't have the perfect background. That doubt slowed me down at times. But I realized that the most important skill is the willingness to learn and the humility to admit what you don’t know.

Going back to school to rebuild my technical foundation was a major turning point. It taught me that stepping outside your comfort zone is often the prerequisite for the next stage of your career. What I once viewed as a gap actually became a strength. I understand the business, the people, and the systems—and that perspective allows me to connect AI to real-world value in a way that purely technical roles often can’t.

First, don’t be intimidated by the hype or the terminology. AI is a multidisciplinary field, and there’s a place for every type of skill—technical, analytical, strategic, or operational. Start by identifying what you’re already good at and how it connects to AI. You don’t need to become a full-stack developer to contribute meaningfully.

Second, invest in building a strong foundation. Learn the basics: programming concepts, data fundamentals, how models work, and how AI systems are built. You don’t need to become an expert overnight, but understanding the mechanics will give you confidence and credibility.

Third, focus on real problems. The most impactful AI professionals aren’t the ones who know every new technique; they’re the ones who understand how technology can solve real business challenges. Spend time learning processes, listening to users, and understanding pain points.

And finally, be proactive about your development. Every five years, I reassess my skills and ask myself what I’m missing. That mindset has guided my career through every reinvention. Emerging fields reward curiosity, resilience, and a willingness to learn. If you bring those qualities, you’ll find your place.