Zero-touch Dispatch with Artificial Intelligence
As early as 1968, we’ve been warned of the potential dangers of intelligent computers and artificial intelligence (AI). In Stanley Kubrick’s 2001: A Space Odyssey, a spaceship’s sentient computer, HAL, evolves from helpful servant to obstinate hindrance and dangerous foe. Such scenarios may be more far-fetched than the fears that haunt most humans. But they do reveal concerns about humans losing control to technology.
The reality is that automation is already reshaping the service landscape, with many predicting that numerous tasks will be supplanted by software, robots, autonomous vehicles, and more. Rather than fear being replaced by automation, technicians and dispatchers should see this technology as liberating. While automation handles repetitive, time-consuming tasks, people are free to focus on work requiring a human touch, like interacting with customers and making non-routine decisions.
What is Zero-touch Dispatch?
First let’s define “zero-touch”: 100% automation without human intervention. When it comes to zero-touch dispatch, this means fully computerized AI software manages the schedules and dispatching of field technicians. The only human involvement needed would be the oversight of a select few dispatch professionals. These designated dispatchers could optimize service schedules by giving the software basic voice commands such as, “Implement the emergency weather dispatch model for all Topeka, Kansas facilities engineers.”
How Artificial Intelligence Powers Automation
Automation of this sort cannot happen without artificial intelligence, which processes massive volumes of data and makes predictions in milliseconds to automatically make optimal routine scheduling decisions. Behind the scenes, models, machine learning libraries, and predictive software all play a part:
- Models are the mathematical and algorithmic models that power the logic behind AI software.
- Machine learning is a type of AI that enables computers to learn from historic data and improve the quality of decision making without being explicitly programmed. Machine learning libraries are the code bases that teams “plug in” and customize to suit their needs.
- Predictive software encompasses a variety of statistical techniques—including predictive modeling, machine learning, and data mining—that analyze current and historical facts to make predictions about future or otherwise unknown events.
Examples of all this in action include:
- Scheduling software that accesses historical data about a location’s weather patterns and adjusts technician schedules to accommodate for typical patterns (e.g., hurricane season), while prioritizing highly urgent service jobs.
- A dispatch feature that determines—based on historical performance data—that technicians with less than three years of experience will require 20% more time on job sites. The software could then send more experienced technicians to priority jobs.
- Dispatch software that accounts for factors such as customer demographics, service history averages, and task type to predict the likelihood of appointment cancellation. It could then automatically send appointment reminders to customers at high risk of cancelling.
What is the Potential Field Service Payoff?
While all this may sound good in theory, at the end of the day, organizations want to know how much they stand to gain through more efficient scheduling.
Consider this scenario. A large telecommunications company operates a fleet of 200 vehicles. Each of these vehicles is dispatched to two jobs a day at $200 per job:
200 x 2 = 400 x $200 = $80,000 per day in service fees
If the company applied AI software to improve the efficiency of its dispatch by 5%, it would realize a significant amount in additional profits. A 5% improvement equates to 2.1 jobs a day per vehicle:
200 x 2.1 = 420 x $200 per job = $84,000 per day
Multiply this by a conservative 300 days, and the organization would generate an extra 1.2 million dollars in earnings.
And that’s the impact with just a 5% efficiency gain. It’s no wonder many executives are so excited about applying AI to dispatch software. It may be the technology that makes it possible to run a revenue-optimized services business.
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