Where should we start with AI in operations?

To begin with AI in operations, organizations should focus on targeted, well-defined workflows that utilize existing data to deliver "needle-moving" results [6, 9]. Successful implementation often involves prioritizing projects that can be tested with small, defined user groups and have clear training and onboarding paths [6]. Common operational areas for experimentation include process automation, quality inspection, predictive maintenance, logistics, and energy forecasting [2, 10]. Before scaling, it is essential to establish a strong data fabric to ensure the AI can deliver actual business value [1]. Organizations should also develop a solid roadmap that balances technology, human resources, and processes, while keeping a focus on economic sustainability and ROI [9, 11]. As AI moves from experimentation into embedded operational infrastructure, leadership must prioritize governance, security, and the creation of clear strategies to manage these systems effectively [4, 8].