Can AI Predict When People Are Home? What It Means for Process Servers.
- Steve Navarrete
- Nov 3
- 3 min read
There is a long-standing belief in the process service industry that the best time to catch someone at home is in the evening. On paper, it makes sense because most people work during the day, so they should be home after six, right?
That logic might have held up twenty years ago, but things have changed. Between remote work, shifting lifestyles, and how people respond to strangers at their door, evening service is not always the golden window it used to be.
Some technology companies use the term predictive insights to describe how artificial intelligence can process massive amounts of data to identify likely addresses or connections between people. In practice, these tools help pinpoint where someone might be found, not when. They focus on location analytics and behavioral patterns drawn from public records, not on predicting what time a person will actually be home. That part still depends on the skill, experience, and intuition of the process server in the field.
The truth is, process servers have been making these kinds of predictions long before AI existed. We read neighborhoods. We notice which cars move and which ones do not. We pay attention to details like trash cans, packages, curtains, lights, barking dogs, and interviewing neighbors. We do not need algorithms to tell us that people who leave for work at 7:30 a.m. probably get home around six.
But what data models often miss is human behavior. People’s routines are not as predictable as they used to be. They work different shifts, travel more, and spend more time at home during the day than ever before.
One of the biggest shifts since the pandemic is how many people now work from home, either full-time or hybrid. This has completely changed the game. Midday attempts are often more productive than evening ones. I have seen it firsthand: people answering the door at one in the afternoon wearing a headset and pajama bottoms. They are not office commuters; they are home workers. And if they will not open the door in the middle of the day, chances are they are not going to answer it at night either.
Early mornings are also a great place to start. Many people are still home before leaving for work or school, and you often catch them off guard before the day’s distractions kick in. Making early morning attempts are one of those simple habits that can make a big difference. This does not mean to make all service attempts in the morning. It just means it's a good place to start and then move to an afternoon or evening attempt if the morning attempt is unsuccessful. Point of emphasis - DO NOT MAKE ALL YOUR ATTEMPTS AROUND THE SAME TIME. THIS IS POINTLESS.
The idea that evening is always the best time to serve someone just does not hold up anymore. What matters now is not the clock; it is understanding the situation, reading the environment, and adjusting your approach.
Let’s be honest: a lot of process servers avoid night attempts because they are inconvenient or feel unsafe. And those are valid reasons. Process servers have families too. They also want to be home for dinner. Walking up to a dark property at ten o’clock with poor lighting and a barking dog is not anyone’s idea of a good time. Safety has to come first.
That said, skipping evening attempts altogether can limit success. The key is to make them strategic, not routine. Save them for when the facts justify the risk or when daytime patterns clearly fail.
The future of process serving will not be built on software predicting people’s habits. It will be built on servers who combine technology, field sense, and good judgment, and who stay alert to how people’s lifestyles and work patterns are changing, because those shifts determine when it is the right time to knock on someone's door.
AI may eventually be able to predict not only where someone lives but when they are likely to be home. That level of accuracy is still far in the future, and even if it became possible sooner, privacy laws would likely keep it out of reach. It would require access to protected data such as real-time tracking, historical movement, and daily routines to make meaningful, high-probability predictions of when someone will be home. Until then, the best predictive insight in this business still comes from experience, persistence, and the human ability to read a situation in real time.
Technology will keep evolving, and so will the laws around service. But no matter how advanced things get, successful process service will always depend on awareness, adaptability, and the willingness to go when other process servers will not. Process servers who understand those changes, and know how to work with them, will continue to deliver results that software alone never could.
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