The most enthusiastic corporate adopters of AI are not building anything you would use for fun. They are quietly replacing their customer support. The pitch, internally, writes itself: support is expensive, staffing is hard, and a chatbot never sleeps, never needs training, and never asks for a raise. The pitch to you, the customer, is “faster, always-available help.” The gap between those two pitches is where the whole problem lives.
What support is actually for
Here is the thing the cost model misses. You do not contact support when everything is fine. You contact it when something has gone wrong — a charge you did not recognise, an order that vanished, an account you are locked out of, a problem the FAQ does not cover. By definition, the queries reaching a human were the ones the self-service layer already failed to answer. They are the hard cases, the edge cases, the emotionally charged cases. Those are precisely the cases a support bot is worst at.
A support bot handles the questions you could have answered yourself and struggles with the ones you actually needed help for. It automates the easy half and leaves you stranded on the hard half.
The loop that is designed to exhaust you
Anyone who has fought with a support bot on a genuinely difficult problem knows the particular despair of the loop. You explain the issue; it offers a generic suggestion you already tried. You explain again; it offers the same suggestion, rephrased. You ask for a human; it asks you to describe the problem, and offers the suggestion a third time. The conversation is not progressing toward a resolution because it was never architected to resolve your case — it was architected to contain you, cheaply, for as long as possible, in the hope that you give up or the system can mark the contact “handled.”
This is where the cost-saving genuinely lands, and it is worth being honest that it lands on you. The expense that the company removed from its books did not vanish; it was transferred to the customer in the currency of time and frustration. The hours a human agent used to spend resolving your problem are now hours you spend circling a bot that cannot, and the saving that looks so clean on the support department's budget is really just a cost quietly moved off the company's ledger and onto yours. A business that measures this arrangement as a success has decided that your time is free and its own is expensive — which is, when you put it that way, a fairly complete summary of how the customer's interests rank.
The escape hatch is being welded shut
For years the grudging compromise was the “talk to a human” option — buried, delayed, guarded by a maze, but there. The quiet shift with AI support is that this hatch is increasingly hard to find or simply absent. The bot is not a first line with a human behind it; it is designed to be the only line, engineered to “deflect” as many contacts as possible before they reach a person. Deflection is the actual metric, and deflection is a polite word for “persuaded the customer to give up.”
This is the part worth naming plainly. When a company measures its support AI by how few customers reach a human, it has defined success as your failure to get help. The incentive is not aligned with solving your problem. It is aligned with ending the conversation.
The confident wrong answer, now with consequences
Support is also a setting where the hallucination problem we keep returning to stops being funny. A chatbot that confidently states the wrong refund policy, invents a returns window, or misdescribes your rights is not producing an amusing screenshot; it is giving a customer official-sounding misinformation from the company itself. There have already been cases where businesses were held to promises their support bots made up. The bot speaks with the company's authority and none of the company's accountability, and the customer is left to sort out the difference.
When it is genuinely good
To be fair — and we always try to be — AI support done honestly is a real improvement. A bot that instantly handles the genuinely simple, common questions, at 3am, in the customer's language, is a better experience than a queue. The failure is not the technology. It is deploying the technology as a wall instead of a door: using it to answer the easy things and to hand off gracefully, quickly and without a fight the moment it is out of its depth.
What good support AI would do
- Offer a human early and visibly, not as a hidden last resort after three loops of the same suggestion.
- Know its limits and escalate on its own the instant it is uncertain, rather than confidently guessing.
- Never invent policy. On anything involving money, rights or entitlements, it should quote the real thing or fetch a person.
- Be measured on problems solved, not contacts deflected — a metric that would quietly realign the whole system with the customer's interests.
The accountability gap is the real innovation
The genuinely novel thing about AI support is not the automation — companies have been automating support for decades with phone trees and canned macros. It is the accountability gap. A human agent is a person the company employs and stands behind; what they promise, the company is generally bound by. A support bot speaks with the full authority of the brand — same logo, same confident tone, same “we” — while the company reserves the right to disown whatever it says the moment that becomes convenient. You are told to trust it as the company's voice right up until it tells you something the company would rather not honour, at which point it is suddenly just a flawed tool.
Customers should not accept that arrangement, and increasingly the law does not either: there have already been rulings that a business is bound by what its support bot told a customer, on the sensible principle that you cannot deploy something as your official voice and then disclaim it when it errs. That is exactly the right instinct. If a company puts a bot in front of you and lets it speak for them, they own what it says — the confident refund policy it invented included. Anything less lets firms capture the savings of automation while offloading its risks onto the customer, which is the precise arrangement this whole wave was quietly designed to achieve.
“Available 24/7” is not the same as “there when you need it”
The headline virtue of support bots — always on, instant, never a queue — is real but slippery. Availability is only valuable if the thing that is available can actually help. A bot that responds instantly, at any hour, in any language, and cannot resolve your problem has not given you support; it has given you a fast, tireless, multilingual way of not being helped. The metric the company celebrates (response time, coverage, contacts “handled”) measures the wrong thing, because it counts the answering, not the resolving.
For the simple, common question at 3am, instant availability is a genuine gift, and worth saying so. For the complicated, unusual, or emotionally loaded problem — the kind that made you seek help in the first place — instant availability of something that cannot help is just a faster route to frustration, followed by the hunt for the human that the system is designed to prevent you finding. Speed at the front door means little if the door only opens onto a corridor of the same three suggestions. What customers actually want is not a bot that answers quickly; it is a problem that gets solved, and the two have been allowed to drift very far apart.
Support is where a brand's real values show
There is a reason support has always been a truer measure of a company than its marketing: it is where you meet the business on your worst day, when something has gone wrong and you need help. How a company treats you at that moment — whether it makes reaching a competent human easy or hard, whether it owns its mistakes or routes you in circles — reveals what it actually thinks of the people paying it. The marketing is what a company says about itself; support is what it does when a customer is inconvenient. Deploying a bot explicitly optimised to stop you reaching a person is, in that light, a fairly frank statement of priorities.
That is what makes the “deflection” metric so revealing. A company measuring the success of its support by how few customers reach a human has, whether it means to or not, told you where you rank against its costs. It is not a neutral efficiency; it is a decision that your resolved problem is worth less than the salary of the person who could have resolved it. Customers register this, even when they cannot articulate it — the sense that a brand they trusted has quietly rearranged itself so that needing help is treated as a cost to be minimised rather than a promise to be kept. The technology did not force that choice. It just made it cheap enough to make at scale, and gave it a friendly interface to hide behind.
Until then, the honest summary of the current wave is this: your favourite companies are replacing the people who could help you with software optimised to stop you asking. Sometimes it works and everyone is better off. Often it works right up until your problem is the kind of problem you actually needed a human for — which, since you bothered to make contact, it usually is.
Originally published at theaidownside.com — evidence-first reporting on the costs and trade-offs behind AI products.
