The FAA has begun testing SMART, an AI-supported traffic management tool that synthesizes hundreds of data feeds to spot delay risks and routing conflicts before takeoff.
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The Federal Aviation Administration has begun testing a new AI-supported system designed to predict delay risks before flights leave the gate, aiming to help traffic managers spot schedule conflicts, weather impacts, and emerging congestion early enough to adjust plans across the national airspace.
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Published coverage and FAA materials describe the new capability as SMART, short for Strategic Management of Airspace, Routes and Trajectories. It is positioned as a decision-support layer for the FAA’s traffic flow management workforce, not a passenger-facing prediction feature and not an automated replacement for air traffic control.
SMART is described as an enhancement within the broader Flow Management Data and Services (FMDS) program, which is intended to modernize how the FAA’s Air Traffic Control System Command Center aggregates data and coordinates traffic management initiatives. In practical terms, it is meant to give traffic managers a more unified, forward-looking picture of where demand is building and where capacity is likely to shrink.
According to publicly available FAA descriptions, the platform brings together a large set of live inputs, including weather, flight trajectories, traffic flow indicators, airport capacity constraints, and controller staffing-related metrics. The aim is to convert that stream into forecasts and visualizations that highlight where trouble is likely to appear before aircraft depart.
The most direct promise of a pre-departure delay predictor is earlier intervention. When the system indicates that certain routes, fixes, sectors, or airports are likely to become constrained, the FAA and airline operations centers can evaluate options earlier in the day, rather than waiting until airborne holding, last-minute ground stops, or missed connections cascade.
In FAA materials, SMART’s role is framed around preventing congestion and reducing delay by coordinating schedules and trajectories before aircraft depart. That emphasis matters because many operational levers work best before pushback: strategic reroutes can be filed, departure times can be metered more predictably, and airlines can make more informed choices about swaps, fuel planning, and turn times.
The FAA already uses structured programs to manage excess demand or reduced capacity, including initiatives that assign controlled departure times for specific flights during ground delay programs. What changes with an AI-supported forecasting approach is the expectation that potential imbalances can be identified sooner and modeled more continuously as conditions evolve, particularly around convective weather and rapidly changing capacity.
Reports indicate the FAA started testing the AI-supported system this week, with coverage noting the timing alongside operational strain in the Northeast. For travelers, that context is significant: it is precisely in high-density corridors, where summer storms and busy banks of departures collide, that small forecasting improvements can translate into fewer missed slots and less gate congestion.
At the same time, the FAA’s public messaging around SMART stresses that the tool is designed to support human decision-making, not replace it. That distinction is important for public confidence and for safety-critical operations, where new automation typically enters as advisory output that humans can accept, reject, or modify.
Implementation is also occurring against a longer modernization backdrop. FMDS is intended as a successor to legacy traffic flow management infrastructure, and multiple FAA programs, including surface and terminal management efforts, have been working toward more integrated data sharing and better demand-capacity balancing across the system. The new AI-supported capability is being presented as part of that stepwise shift rather than a single switch-flip change across every facility overnight.
Travelers should not expect an FAA app that tells them their individual flight will be 37 minutes late before boarding. The FAA’s system is built for system-level traffic management: it is designed to help traffic managers anticipate where the network is likely to jam and to shape flows through reroutes, metering, and other initiatives that can reduce knock-on effects.
What travelers may notice, if the tool performs as intended and is adopted widely in operations, is a subtle improvement in predictability: fewer sudden departure holds after boarding, fewer last-minute reroutes that add surprise time, and a better chance that airlines can adjust rotations before delays propagate through the day.
Even with better forecasting, delays will not disappear, particularly when weather reduces usable airspace or airports lose arrival and departure capacity. The practical value of an AI-driven predictor is not perfect accuracy; it is earlier detection of risk, clearer options, and more time for coordinated decisions among the FAA and aviation stakeholders.
SMART arrives as the U.S. transportation sector expands its use of AI for forecasting and decision support, while also grappling with how to assure safety and reliability in systems that must work under stress. FAA publications in recent years have also outlined broader efforts related to AI safety assurance and the use of data-driven tools across aviation operations.
For airlines and airports, the most meaningful next step will be how well the new system integrates into existing collaborative decision-making routines, and how consistently it performs across different weather regimes and traffic patterns. Predictive tools can be strong in stable conditions but are tested hardest during fast-moving thunderstorms, complex flow restrictions, and irregular operations.
For travelers, the near-term takeaway is that the FAA is putting more emphasis on preventing congestion rather than only reacting to it. If the AI-supported forecasts can help traffic managers act earlier, the result could be fewer compounding delays that begin before takeoff and ripple across the network for the rest of the day.
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