The electrical system warning light had gone on in my Kona EV over the weekend, and all the manual said was to take it to the dealer for evaluation. I first tried scheduling an appointment via the website, and it reminded me how the web, once a marvel, is looking awfully clunky these days. There were lots of options for services to schedule, but it wasn’t at all clear which of them I might want.

Not only that, I’d only reached this page after clicking through various promotions and testimonials about how great the dealership is—in short, content designed to serve the interests of the dealer rather than the interests of the customer. Eventually, I did find a free-form text field where I could describe the problem I actually wanted the appointment for. But then it pushed me to a scheduling page on which the first available appointment was six weeks away.

So I tried calling the service department directly, to see if I could get some indication of how urgent the problem might be. The phone was busy, and a pleasant chatbot came on offering to see if it might help. It was quite a wonderful experience. First, it had already identified my vehicle by its association with my phone number, and then asked what the problem was. I briefly explained, and it said, “Got it. Your EV service light is on, and you need to have it checked out.” Bingo! Then it asked me when I wanted to schedule the service, and I said, “I’m not sure. I don’t know how urgent the problem is.” Once again. “Got it. You don’t know how urgent the problem is. I’ll have a service advisor call you back.”

That was nearly a perfect customer service interaction! I was very pleased. And someone did indeed call me back shortly. Unfortunately, it wasn’t a service advisor, it was a poorly trained receptionist, who apparently hadn’t received the information collected by the chatbot, since she gathered all the same information, only far less efficiently. She had to ask for my phone number to look up the vehicle. Half the time she didn’t understand what I said and I had to repeat it, or I didn’t understand what she said, and had to ask her to repeat it. But eventually, we did get through to the point where I was offered an appointment this week.

This was not the only challenging customer service experience I’ve had recently. I’ve had a problem for months with my gas bill. I moved, and somehow they set up my new account wrong. My online account would only show my former address and gas bill. So I deleted the existing online account and tried to set up a new one, only to be told by the web interface that either the account number or the associated phone number did not exist.

Calling customer service was no help. They would look up the account number and verify both it and the phone number, and tell me that it should all be OK. But when I tried again, and it still didn’t work, they’d tell me that someone would look into it, fix the problem, and call me back when it was done. No one ever called. Not only that, I even got a plaintive letter from the gas company addressed to “Resident” asking that I contact them, because someone was clearly using gas at this address, but there was no account associated with it. But when I called back yet again and told them this, they could find no record of any such letter.

Finally, after calling multiple times, each time having to repeat the whole story (with no record apparently ever being kept of the multiple interactions on the gas company end), I wrote an email that said, essentially, “I’m going to stop trying to solve this problem. The ball is in your court. In the meantime, I will just assume that you are planning to provide me gas services for free.” At that point someone did call me back, and this time assured me that they had found and fixed the problem. We’ll see.

Both of these stories emphasize what a huge opportunity there is in customer service agents. But they also illustrate why, in the end, AI is a “normal technology.” No matter how intelligent the AI powering the chatbot might be, it has to be integrated with the systems and the workflow of the organization that deploys it. And if that system or workflow is bad, it needs to be reengineered to make use of the new AI capabilities. You can’t build a new skyscraper on a crumbling foundation.

There was no chatbot at the gas company. I wish there had been. But it would only have made a difference if the information it collected was stored into records that were accessible to other AIs or humans working on the problem, if those assigned to the problem had the expertise to debug it, and if there were workflows in place to follow up. It is possible to imagine a future where an AI customer service assistant could have actually fixed the problem, but I suspect that it will be a long time before edge cases like corrupted records are solved automatically.

And even with the great chatbot at the Hyundai dealer, it didn’t do much to change my overall customer experience, because it wasn’t properly integrated with the workflow at the dealership. The information the chatbot had collected wasn’t passed on to the appropriate human, so most of the value was lost.

That suggests that the problems that face us in advancing AI are not just making the machines smarter but figuring out how to integrate them with existing systems. We may eventually get to the point where AI-enabled workflows are the norm, and companies have figured out how to retool themselves, but it’s not going to be an easy process or a quick one.

And that leads me to the title of this piece. What is the competitive moat if intelligence becomes a commodity? There are many moats waiting to be discovered, but I am sure that one of them will be integration into human systems and workflows. The company that gets this right for a given industry will have an advantage for a surprisingly long time to come.