AI applied: From buzzword to actual impact in the turnaround process — an Amsterdam Airport Schiphol case study
Abstract
Airport operators face growing complexity due to larger aircraft and rising passenger volumes, putting pressure on on-time performance and exposing structural limitations in traditional turnaround management. At Amsterdam Airport Schiphol, these challenges converge in the ‘black box’ between actual in-block time and target off-block time, where over 100 interdependent processes occur with limited real-time visibility. This case study presents Schiphol’s seven-year journey to address these constraints through Deep Turnaround, an artificial intelligence-driven solution combining computer vision and predictive analytics to convert raw camera data into actionable insights. Currently this solution is live on 102 stands. Results show significant improvement in turnaround predictability, positioning AI as an enabler of systemic transformation rather than a standalone fix. Lessons learned underscore the importance of full rollout, trust in data, and embedding AI outputs into operational decision making. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
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Author's Biography
Jeffrey Schäfer serves as Process Owner for Aircraft Turnaround at Royal Schiphol Group, Amsterdam Airport. Jeffrey describes himself as an ‘aviation geek’ who has progressed from an operations analyst to a leader in process management over the past five years. Combining expertise in performance management and operational workflows, Jeffrey seeks fact-based decision making, driving Schiphol’s digital transformation and elevating its operational efficiency. His work exemplifies a commitment to embedding data-driven practices in airside operations, ensuring Schiphol remains at the forefront of innovation in European aviation.
