Industrial & Systems Engineering | Senior at The University of Texas at El Paso (UTEP) | Alpha Pi Mu (APM) Treasurer | IISE | HFES
I stumbled across this article that highlights how companies are moving beyond the initial excitement around generative AI (gen AI) to focus on generating real business value. The key takeaway is that organizations need a well-structured operating model to scale gen AI successfully, with data playing a central role. This involves creating a framework that aligns technology, people, and processes to ensure AI solutions are impactful, not just experimental. It’s crucial for companies to avoid common traps, like investing in AI without a clear purpose or running disjointed pilot projects. Instead, they should focus on building adaptable AI components, creating cohesive teams, and ensuring risk and compliance management is integrated into the process. Additionally, many organizations are shifting from centralized AI teams to more decentralized models as they mature. This need for structure and organization directly aligns with industrial engineering, where the goal is to optimize systems by streamlining processes and improving efficiency. In the future, I aim to contribute by integrating AI and data analytics into continuous improvement strategies. I want to help design systems that not only leverage AI for innovation but also ensure that the technology is scalable, compliant, and aligned with business goals. #FutureOfWork #DataAnalytics
