25 AI Agents Can Freeze a Debit Card. Not One of Them Can See Why the Machine Is Jammed.

Glia just shipped 25 AI agents for customer service at banks and credit unions. According to FinAi News, the new Glia CoPilot library handles frontline tasks like freezing a lost debit card, making a payment, and initiating a dispute. Real actions, executed on command, no human in the loop.

Good for them. Genuinely. That is useful automation.

But look at the list again. Freeze a card. Move money. Open a dispute. Every one of these tasks lives entirely inside a database. The problem arrives as structured text, the fix is an API call, and the whole interaction ends in seconds. These were already the cheapest tickets in the queue. Now they cost almost nothing.

Meanwhile, somewhere right now, a customer is standing in front of a jammed machine. A furnace that will not ignite. A router with a blinking light nobody can name. A commercial espresso machine leaking from a place the manual does not mention. And not one of those 25 agents, or the thousands like them shipping across the industry, can do anything about it.

We keep automating the cheap customer service tickets

The pattern is everywhere once you see it. Vendors race to automate the transactional layer of support because it is the layer software can reach. Text in, text out, action in a system of record. The demos look incredible. The metrics look incredible. Deflection rates climb.

Yet the cost curve of support does not bend the way the pitch decks promise, because the expensive cases were never the transactional ones. The expensive cases are physical. They involve hardware, installations, error states, and a customer who cannot describe what they are looking at. I wrote about this before: the customer who cannot describe what they are looking at is your most expensive case. That case ends in repeat contacts, escalations, returns, or a truck roll. Each truck roll costs hundreds of dollars. A frozen debit card costs a fraction of a cent in compute.

So the industry is optimizing the numerator that was already small and leaving the big one alone.

The ROI gap is showing up in the data

Salesforce’s new State of Field Service report, based on a survey of over 2,300 field service professionals across nine countries, makes the tension plain. Adoption is near total: 95% of field service organizations use AI, and 85% plan to increase investment over the next one to two years. Leaders are chasing customer satisfaction, productivity, and revenue.

The wins are real but narrow. Where AI touches scheduling and dispatch, 57% of organizations report higher revenue per job. In other words, AI is great at deciding which truck to send and when. It still has nothing to say about whether the truck needed to go at all.

Meanwhile, 40% of leaders admit they struggle to measure whether AI is working, 61% say mobile workers lack access to the customer data they need onsite, and 66% report rising turnover among mobile workers. Money is pouring in. Proof is lagging. The physical layer of service, where the actual margin leaks out, remains mostly untouched.

Why AI agents leave the jammed machine jammed

The reason is simple and uncomfortable. AI agents work in customer service when the problem can be fully expressed in text and the resolution can be fully executed in software. Banking tasks qualify. That is why financial services is first in line.

A jammed machine fails both tests. The customer cannot express the problem accurately, because they do not know what they are seeing. And no API call clears a paper jam, reseats a cable, or spots the hairline crack in a fitting. The bottleneck is not intelligence. The bottleneck is eyes.

Every dollar spent making the easy tickets easier widens the gap between what your AI agents can handle in customer service and what your customers actually bring you. Worse, it concentrates frustration. When the simple stuff resolves instantly, the customer with the physical problem feels the contrast. They just watched your AI freeze a card in four seconds. Now they are on hold, trying to describe a noise.

The strategic question nobody in the demo asks

If you run support or service operations, the question is not “which tasks can we automate next?” The vendors will answer that for you, loudly. The better question is: where does the money actually go? Pull your cost-per-resolution numbers by case type. In most hardware, appliance, telecom, medical device, and equipment businesses, a small share of cases, the physical ones, consume the majority of support spend.

Strategy means putting resources where the cost lives, not where the tooling is convenient. Right now the market is doing the opposite at scale.

Closing that gap does not require another chatbot. It requires getting visual context into the case at the first contact. When an agent, human or AI, can see the jammed machine through the customer’s phone camera, the diagnosis happens in minutes instead of days, and the truck only rolls when it must. That is the layer we build at Viewabo, and it is the layer the current AI wave keeps driving past.

Twenty-five agents that can freeze a debit card is progress. But nobody’s support budget is bleeding out through debit card freezes. It is bleeding out in front of a machine that nobody at the company can see. Until your stack can look at the problem, you have automated the receipt, not the repair.