Using AI to decide, in real time, when a campaign should be running at full weight, easing off, or going dark entirely, replacing a flighting calendar set months in advance with one that adjusts itself against live performance signals.
Also known as Dynamic flighting, algorithmic flighting
Flighting itself is not new. It is the classic media scheduling pattern of running ads in concentrated bursts, called flights, separated by hiatuses where spend drops to zero, built to match budget against the weeks that actually move a buyer: a back to school push, a holiday spike, a seasonal category peak. What AI adds is the ability to stop guessing the calendar months ahead and instead let the flight pattern respond to what is actually happening, engagement dropping off faster than expected, a competitor going dark, inventory running low, a shift in demand nobody had on the calendar.
In practice this means a model watching frequency, response rate, and creative fatigue signals in near real time, then recommending or automatically triggering when a flight should ramp up, hold, or pause, instead of a planner locking those dates into a media plan for a campaign that runs all year. The scheduling logic still follows the same bursts and hiatuses shape flighting has always had. The difference is who, or what, is deciding the timing.
Flighting has always been one of the places media planning turns into a judgment call, and it is exactly the kind of judgment call agencies are now being asked to defend or improve with data instead of instinct.
It turns a quarterly guess into a weekly adjustment. A media plan built months ahead for a campaign running through the year used to lock in flight dates based on last year’s seasonality. An AI-assisted flighting model can pull a flight forward, extend it, or cut it short based on what this year’s data is actually showing, without waiting for a quarterly review to catch up.
It catches fatigue before a human would. Creative fatigue and audience burnout inside a flight used to show up in a monthly report, after the budget was already spent. A model tracking frequency and response decay in near real time can flag or trigger a pause days earlier, before performance actually craters.
It changes what a media plan deliverable even looks like. Instead of handing a client a fixed calendar of flight dates for the whole campaign, agencies are increasingly presenting a flighting logic, the rules and thresholds that will trigger a ramp up or a pause, which is a different kind of document to build and a different kind of thing to explain in a client meeting.
A seasonal outdoor gear client runs a media plan built around three flights: a spring hiking push, a summer camping push, and a fall hunting season push, with hiatuses in between to conserve budget. Historically the exact start and end dates for each flight were locked in during annual planning based on the prior year’s calendar. This year, your media team layers in a model that tracks weekly search interest, weather pattern data, and early engagement signals against each flight’s planned start date. Two weeks before the fall flight was scheduled to begin, the model flags an unusually early cold snap driving hunting-related search volume ahead of schedule, and recommends pulling the flight forward by ten days. The team makes the call, the client’s product sells into the actual demand curve instead of the calendar’s assumption of it, and the flighting logic, not just the flight dates, becomes something the team can point to in the next planning cycle.
The automations and agents module of the workshop teaches you how to build AI workflows that compress the busywork without taking the craft out of the studio.