AI models are only as good as the data they can reach or were trained on. The difficulty is not a shortage of information. It is that the trusted information gets harder to find as the volume grows. In aviation, where a schedule change or a delay can move a route plan, a market entry case or a fleet decision, the quality of the underlying data decides whether an AI-assisted answer is usable.
OAG is one of the sources we consider when that question arises. Forward-looking and historical flight schedules from 900+ airlines are updated every 15 minutes, and the company tracks more than 4,000 airports. For strategy work, the historical layer matters as much as the live feed. OAG’s archive is positioned for benchmarking, strategy, and claims management, and it covers schedules and actual performance, not just plans.
Recent conversations with industry peers reinforce the same point. The opportunities for AI in aviation are real, but they are concentrated where the data is structured, current and comparable across carriers and airports. Building on that foundation is what makes the difference between a demonstration and a decision tool.
For aviation clients, this means three practical questions before any AI initiative: which datasets are authoritative for the question, how far back their history runs, and whether they reflect what actually happened or what was planned. Those questions shape the quality of the answer more than the choice of model.
At Threedot, we work on the strategic side of AI: defining the question, testing the evidence, and turning data into decisions an airline, airport or investor can act on. The right data makes AI useful. The right judgement makes it worth acting on.
