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 involves prioritizing projects that can be tested with small user groups and have clear training and onboarding paths [6]. Common operational use cases 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 delivers actual business value [1]. Organizations should also develop a solid roadmap that balances technology, human resources, and processes, while keeping a close watch on economic sustainability and ROI [9, 11]. Rather than treating AI as a series of isolated experiments, the goal is to embed AI agents into workflows to create an ongoing layer of organizational capability [4, 12].