Delek US

One of the most important lessons I’ve learned with my experience in data and AI is that innovation only delivers value when it is tightly aligned to real business needs. Early in my career, I saw initiatives fail not because the technology was weak, but because the connection to measurable impact was unclear. Today, I focus on starting with the problem–not the solution –and ensuring every initiative has a clear pathway to business value. Programs like scaling drone operations into a centralized command center reinforced that innovation succeeds when it’s operationalized, not just piloted.

Data provides clarity, but leadership requires judgment. I rely on data to frame decisions and reduce uncertainty, but I’ve found that intuition built through experience plays a critical role in timing, prioritization, and risk-taking. In realtime and fast-moving environments, especially in industrial operations, waiting for perfect data can slow progress. The balance is knowing when data is “good enough” and when to lean on experience to move forward decisively.

Innovation that Scales, Leads and Lasts

We’re seeing a clear shift from isolated AI use cases to integrated, enterprise-wide capabilities. In the energy sector, this means connecting data from field operations, control rooms, and enterprise systems into a unified decision environment. Technologies like M/L, computer vision, digital twins, and AI-driven optimization are becoming part of the core operating model. The real transformation isn’t just smarter algorithms, it’s how organizations redesign workflows and decision-making around them.

Yet realizing that enterprise-wide vision is where the real organizational test begins. Scaling is where most organizations struggle. It’s relatively easy to prove value in a pilot, but it’s much harder to embed that capability into daily operations. The biggest challenges are often organizational – like change management, skill gaps, and resistance to new ways of working. Successful organizations invest as much in adoption as they do in technology: they build cross-functional alignment, adjust their SOPs, train their teams, and design solutions that fit seamlessly into existing workflows. Starting small, proving impact, and then scaling methodically has proven to be the most effective approach.

To the professionals looking to build a career in data, AI and innovation, I would advise them to focus on understanding the business before mastering the technology. The most impactful professionals are those who can translate complex capabilities into real-world outcomes. Be curious, stay handson, and don’t be afraid to experiment. But also recognize that real impact comes from execution at scale. Finally, invest in communication and change leadership; the ability to bring people along the journey is what ultimately turns good ideas into meaningful, lasting innovation.