Cuts through the hype to examine what AI is delivering in production environments right now — from predictive maintenance to passenger flow balancing to security image analysis. Participants share honest assessments of what has worked, what has underperformed, and what they wish they’d known before they started.
As AI moves from pilot to production, airports face questions they weren’t designed to answer: who owns the decision when an automated system errs? Day 2 examines accountability frameworks, explainability requirements, regulatory expectations, and the organisational structures needed to govern AI at scale.
The ambition to run a fully instrumented, data-driven airport operation requires unglamorous foundational work — interoperable systems, clean data pipelines, legacy integration, and organisational buy-in. This table examines what that groundwork looks like in practice, and why most airports are further from readiness than they admit.
Data infrastructure is only valuable if it changes behaviour. Day 2 examines the hardest part of digital transformation: getting operational teams to trust, interpret, and act on data-driven recommendations in real time — and the organisational and cultural barriers that prevent it.
From extreme weather to geopolitical shocks to cyber incidents, disruption is no longer exceptional. This table examines how airports are shifting from reactive crisis management to proactive resilience design — building redundancy, scenario planning, and operational flexibility into the fabric of how they run.
Day 2 takes the resilience conversation into more uncomfortable territory: what do airports actually owe each other and the wider system when things go wrong? This explores the case for deeper operational collaboration — shared protocols, mutual aid agreements, and industry-level response frameworks.
