You can learn a lot about a dealership by listening to service reps before 9 a.m.
It is the mood. The tension. The service manager is locked in, the support manager is tapping her finger on the desk, one customer is down, another ticket just came in, and a technician is heading out with half the story. By lunch, one call has turned into two, somebody is irritated, and your service manager is staring at costs that did not need to pile up this way.
So where does AI for customer service fit in for dealers?
By now, you have heard enough AI talk to choke an ox. There was the guy at the last show telling you using AI for customer service is going to solve all your problems, and if you do not get on board right now, it is going to take all the jobs.
Meanwhile, there is a bad ADF (automatic document feeder) at one of your enterprise accounts, the church down the road is out of cyan, and it is Friday.
Here is the part worth paying attention to. Yes, AI is going to make businesses better. Yes, it is going to reduce some labor costs. But in a copier dealership, the department with the most to gain from a solid AI rollout is service, not sales and marketing. Fancy emails and sharp-looking graphics are nice. Cutting cost and confusion inside the department that protects your customer relationships and soaks up real operating expense is worth a lot more.
Look at what happened with ADT. They are in the security business; their headache is the same as yours: a technician in a van is the most expensive way to solve a problem. By using AI for customer service to “look” at the issue before a human ever grabbed their keys, ADT managed to handle 40% of their calls virtually. Of those virtual calls, 80% never needed to dispatch a truck roll at all.
Think about your own log. Imagine if 30% of your complaints about “toner light is still on” or “the scan-to-email broke again” were killed at the dispatch desk because the system gave the coordinator the right question to ask. That is not just a statistic; that saves a lot of dollars in fuel, wear and tear, and technician frustration that can be avoided.
The same logic applies to the “big iron” guys like Siemens. They worked with a huge steel firm that used AI to spot “process interruptions.” The system saw that a part was going to fail before the machine actually failed — and saved nearly 2,000 hours of downtime.
I know you are not making steel, but you are managing a fleet of little paper-generating “factories” sitting in your customers’ hallways. When a machine throws an error code three times in a week, a human might miss it. AI does not. It flags the “alarm” before the machine dies on a Tuesday morning, right before the executive committee meeting. Catching a faulty pump or a fuser unit before it ruins the customer’s day is how you turn service from a “firefighting” department into a “fire prevention” department.
Using AI for customer service even changes how you train the newbie. We have all seen it: the new hire who spends three hours on a 30-minute call because they are scrolling through a 400-page PDF on their phone. Axis Water Technologies used AI tools to cut that “clueless” phase down from two months to three weeks. The company’s techs got out of the shop 35 minutes faster every morning because they were not guessing what parts to put in the trunk.
That is 35 minutes of extra billable time or, better yet, 35 minutes of that tech not feeling like they are drowning in a job they do not understand.
How using AI for customer service can prevent expensive problems
No, you are not a global steel company in Australia or a German dairy producer. That is not the point. The point is that they used AI to prevent expensive problems from getting worse. Dealership service departments live with that same problem every day, just on a different scale.
There is also a labor angle here. AI is not going to replace half your bench. It is more likely to help new technicians become useful faster. Salesforce cited Axis Water Technologies, saying its field-service tools cut new-hire training time from two months to about three weeks; reduced service dispatch by 20%; and helped technicians get out the door 35 minutes faster each day because they were better prepared.
That matters.
A new tech who can get to the likely answer faster is more useful sooner. A senior tech who spends less time cleaning up avoidable situations has more time for the work only he or she can do.
Then there is the hiring problem, which is really a shop-floor reality problem.
Very few people wake up and say, “Today I want to learn how to service photocopiers. Today I want to read service manuals. Today I want to walk into offices and hear complaints about slow printing and bad copies.”
That is the world you are hiring into. So, what makes that job more attractive?
Technical workers are not excited to join a department where all the knowledge lives in three veterans, the ticket notes are a mess, and every tough call starts with guesswork. A service department that can surface machine history, narrow likely causes, and guide diagnosis is easier to step into. It feels more modern, more teachable, and less chaotic. That does not solve the labor shortage, but using AI for customer service gives you a better shot at bringing younger people into your workforce and keeping them there.
So yes, you should be looking at AI in the service department. The real question is how to do it without turning it into another project that looks good in month one and ends up in the toner-waste bottle of hopes and dreams by month seven.
Start by deciding who is going to own the work.
Bring in outside help when the hard part is connecting systems and cleaning up the data. If nobody on your team can tie together service history, CRM records, dispatch rules, and device data into something usable, do not pretend otherwise. That is where specialists earn their keep.
Value in-house expertise
Keep ownership inside when the hard part is judgment. Your team knows your bad habits, your customers, your shortcuts, and your version of chaos better than any consultant ever will.
A hybrid model usually makes the most sense. Let outside specialists help stand up the tools and connect the data, but keep your service manager or operations lead in charge of the rules, the exceptions, and what good looks like in the field.
If you want to build this in-house, do not launch some grand AI initiative. Pick one service problem that already costs you money: repeat callbacks; weak triage; or missing parts. Maybe it is slow ramp time for new techs. Put one service owner on the problem and one system-minded person on the data. Then use your own history to help dispatch and technicians make better, faster decisions every day.
There is more to say here, especially around model choice, data quality, and where these systems can go off the rails. That can wait for another column.
Start where it already hurts.
Because when you make that 9 a.m. service desk less tense and keep dispatch in a good mood. You are fixing the heartbeat of the dealership, not chasing AI hype.
AI Columnist Greg Walters
About the columnist: Greg Walters is a writer, analyst, speaker, and longtime technology operator who has led managed print and IT initiatives for several organizations. He is a founding member (and past president) of the Managed Print Services Association (MPSA) and creator of The Death of the Copier blog. Walters also co-founded the Cricket Continuum, which works to convert office technology dealers into robotics resellers and service partners. Future columns will continue to focus on artificial intelligence in the copier dealer community. If you have curiosity about the subject, drop us a line, and we’ll see if we can come up with something.
