The aviation industry is undergoing rapid transformation, and using AI to improve operational efficiency is at the core of this evolution. Cirium’s OTP Improvement AI has set a new benchmark for how airlines and airports can analyse on-time performance (OTP) and operational disruptions.
To gain a deeper understanding, we sat down with Niha Shaikh, VP of Product at Cirium Journey, to discuss how this innovation is reshaping decision-making and driving operational excellence.
As you talk to airports and airlines globally, what are the most prevalent challenges facing operations teams?
As the demand for travel rises, operational teams are under constant pressure to maintain on-time performance while managing increasingly complex networks. Disruptions cascade quickly, and the data needed to understand causes is often fragmented across multiple systems. That makes it hard to move from reactive firefighting to proactive planning.
How does AI for OTP help—and what role does generative AI play?
Generative AI helps make sense of operational complexity. Traditional analytics can crunch numbers, but they often miss the “why” behind patterns. Generative methods interpret data, highlight relationships, and present insights in ways that are easier to act on—for example, identifying the stations that contribute most to network delays, pinpointing aircraft that consistently cause downstream disruptions, or analyzing peer buffer and performance on common routes.
Cirium’s OTP AI is an example of this approach. It applies generative techniques to translate fragmented data into clear narratives. An overnight capability (e.g., NightVisionAI) can detect events of interest and synthesize where delays propagated, which schedules are most vulnerable, and how peers responded—so teams begin the day with a focused view of what matters most.
What problems does this solve in practical terms?
Three areas stand out:
- Network awareness with context. Understand how disruptions ripple across the operation, not just as isolated events.
- Comparable external benchmarks. Assess peers with similar analytical depth—historically time-consuming or surface-level.
- Reduced cognitive load. Turn fragmented data into clear, actionable insights and suggest logical follow-up paths so teams can explore options without starting from scratch.
Ultimately, the aim is decision-quality insights—not just more data.
What outcomes are airports and airlines seeing so far?
Airports and airlines consistently highlight the value of narrative-style insights. Instead of spreadsheets, teams get clear explanations and visuals they can share with stakeholders. Time savings are significant: analyses that once took days now take minutes. In one beta, an airline reduced a 30–40-minute delay analysis to under five minutes. Another used the tool to surface additional context around turn times that internal metrics did not capture.
How does this align with industry priorities—and Cirium’s mission?
A core industry priority is accelerating digital transformation with trusted data and responsible AI so teams can navigate complexity and make better decisions, faster. This aligns with Cirium’s mission: to accelerate the digital transformation in aviation, empowering the industry with trusted data and innovative solutions. We harness the power of that strong data foundation using Generative AI with OTP AI — helping navigate the complexity efficiently and support better decisions. It’s about enabling agility in an industry where every minute counts.
What is the bigger picture?
This isn’t just about OTP. It signals next-generation analysis capabilities—where AI doesn’t only calculate but explains, personalizes, and supports operational goals. That shift opens doors to smarter decisions across fuel efficiency, schedule optimization, asset management, and risk forecasting. For airports and airlines, the focus is on augmenting expertise—giving teams clarity and speed to elevate performance in complex environments.
See the assistant in action or to learn how it can elevate your operations: cirium.com/OTPAI
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