A note on method: before publishing, I ran this piece through a peer review using the free plan of Elicit, an AI research assistant. It checked the claims against sources, flagged where the argument outran the evidence and suggested what to soften. I kept what held up and pushed back where it was wrong. An article about who reads our words, reviewed in part by a machine, felt like the right test.
Scenario under analysis
A customer is preparing for a winter walk in the hills and needs the right equipment. A few years ago that meant one conversation, between them and someone in a shop.
Now the conversation is spread across several. They start with a site assistant, typing questions late at night. In the store, they talk to a sales associate, who asks a colleague for help over a headset. That exchange, about the customer, is turned from voice into text for a manager to read. The associate answers partly from a suggestion on a screen. A week later, the customer’s own shopping agent places the follow-up order with the retailer’s system. Behind the scenes, the associate’s shift ends and the next colleague reads a summary of what happened. And when the jacket leaks a month later, the complaint and the photo that follow may have been written and made with AI too, and so may the company’s reply.
Every step is a conversation. Few of them are between the same two parties, and nobody involved can see the whole chain. The journey is a composite, built from patterns rather than from one observed case.
The chain also runs in two directions. Some conversations go from the customer to the company: a question, an order, a complaint, a claim. Others come back from the company to the customer: an answer, a decision, a call, a label on a shelf. This note follows both, and stays with service: customer service, staff on the shop floor, the conversations people have when they need something. Marketing is a different conversation, and it is left out.
Two audiences
At each step there are really two questions. Who am I talking to now? And who will read this later?
At a shop counter, the two answers were almost the same: the person in front of you, and whatever they remembered. In this chain they come apart. The listener you can see is often not the audience that matters most. That audience is whoever reads or processes the record next, and nobody in the conversation can see them. Two audiences is a lens rather than a complete map: there are often more, from a colleague within earshot to the company that runs the system. But the split between now and later is the one that changes what people say.
The roles in the conversation
The customer
We shape what we say for whoever we think is listening. Linguists call this audience design: we speak one way to the person in front of us, another when we know someone else can hear, and another again when we don’t know they are there. The customer speaks differently depending on who they think is listening, and in this chain that listener keeps changing. So does the company.
The associate (customer to staff, and back)
The baseline. A person reads the pause before the answer, the hand that goes back to the jacket the customer just said they didn’t like, the gap between what is said and what is meant. Nothing is kept word for word. But this baseline was never neutral. Reading the room works best for people whose signals match what the reader expects, and less well for a blind customer, a colleague who communicates differently, or anyone whose face and voice don’t follow the usual patterns.
Customer to company
Half the chain runs from the customer towards the company. More and more often, the customer brings their own AI to it.
The site assistant (customer to company’s AI)
If nothing here seems to judge them, the customer can be franker: the real budget, the real fitness level, the doubt they wouldn’t admit in front of someone who knows more than they do. But that openness depends on what they think happens to their words. Research on service AI finds people share more sensitive information when they believe the system can’t judge them, and that the preference weakens when they want support or expect what they say to be passed on. A machine is not automatically a safe listener. It only feels like one when both the judgement and the record seem to be absent.
The shopping agent (customer’s AI to company’s AI to staff)
The customer’s intent is compressed into a request, interpreted by the retailer’s system and handed on to staff. Umberto Eco (the Italian semiotician and novelist, author of The Name of the Rose) argued that every text is written for a Model Reader: the reader it expects, and quietly teaches how to read it. Here the idea applies directly: the retailer now writes its product data for a new reader, the customer’s agent, which reads structured fields rather than shop windows. Two translations later, it is hard to say what the customer actually authorised, what the agent inferred and what the retailer carried out.
The agent on the phone (customer’s AI to staff)
The phone rings, and a pleasant voice asks whether the waterproof version is in stock. The associate answers as they would answer anyone, without knowing they are talking to a machine. The rule that an AI must say it is an AI was written to protect the people it talks to. Here, the person who needs to know is the member of staff.
The complaint (customer with AI to staff)
The jacket leaks, and the customer writes to complain, with help. The email is long, confident and full of references, some of them wrong. For a customer who struggles to write, the AI is what makes the complaint possible at all. For the person reading it, the problem is buried somewhere in the length. So the company asks to speak by phone: not to hear more, but to hear the customer without the machine. The call is a check on who is speaking. It also excludes the customer who chose writing because speaking is harder for them.
The photo (customer with AI to company’s AI to staff)
The refund request arrives with a photo of the damage. A photo used to be evidence; now it may be generated. The retailer’s system checks the customer’s image for signs of AI, and the customer is quietly treated as a suspect. Honest customers pay for the dishonest ones: another photo, a video, a trip back to the shop.
The review (customer’s AI to company and the next customer)
The customer asks an AI to write the review. The company reads it, and customer service may reply, often with a draft written by AI. Months later, another customer asks an AI to summarise the reviews before buying. Machines write for machines, and the people at either end may never read a word of it. Reviews belong here because the reply is a service conversation: it is often the only answer a dissatisfied customer gets in public. Fake reviews have been banned in the UK since April 2025, and a review drafted by AI about a genuine experience is probably not fake in law. But nobody reading it can tell whose words they are.
Company to customer
The other half of the chain runs back. Some of what the company says to the customer is first said about them, among colleagues. Some of it reaches the customer through machines, the company’s or their own.
The transcript (staff to staff, about the customer)
The associate turns away and asks a colleague over the headset: is the waterproof version in stock, and can we refund a jacket bought last week? The conversation is about the customer, but the customer isn’t in it. It is turned from voice into text, and a manager can read it.
Eco built his idea of the Model Reader for written texts: a writer can’t see the reader’s face, so the text has to imagine one. When we talk, we check each other constantly, through a nod, a pause, a change of tone. A transcript turns talk into text, and the speakers lose that check without noticing. The two colleagues are now, in effect, writing for a reader they can’t see: the manager, and whoever opens the record later.
The talk also changes medium. Most of the words survive, though not all, and not equally for every voice. What doesn’t survive is everything that was never a word: tone, hesitation, the half-sentence abandoned midway. Knowing the transcript exists changes the talk itself. In one study of meetings, hearing participants changed how they spoke once their words were being captioned automatically for Deaf colleagues. That study measured how people spoke, not what they chose to say. Research on workplace monitoring points further: people under monitoring shared less. Whether colleagues on a transcribed channel stop asking the questions that make them sound unsure is a question for fieldwork, and an important one.
The customer, meanwhile, has become the subject of a record they never spoke into, and nothing in the shop tells them it exists.
The handover note (staff to AI to staff)
The next colleague reads the summary, not the conversation. They are not overhearing the original talk. They are the record’s future audience, reading something already transformed. Whatever the summary dropped never reaches them, and nothing in it shows that anything was there. In one evaluation of AI note-taking tools in primary care, omissions were by far the most common error. And whatever the next colleague says to the customer starts from that summary.
The prompted answer (staff with AI to customer)
The customer hears a person but receives the machine’s answer. In Erving Goffman’s terms, the associate is the animator who speaks the words, the system is the author who wrote them, and the retailer is the principal who answers for them, though often nobody makes that clear. The trust the associate has earned is lent to the system, and when the answer is wrong, it is the associate who loses it.
The reply (customer service with AI to customer)
The complaint gets an answer, and the answer was drafted by AI too. It is polite, complete and slightly too smooth: it apologises for things the customer didn’t say and misses the one thing they did. Two machines have now exchanged letters on behalf of two people. If each side’s AI is summarising the other’s, a complaint can be closed on paper without anyone having properly read it.
The decision (company’s AI to customer)
The photo is checked, and the refund is refused. The customer receives the outcome, often without knowing whether a person ever looked at it. A refusal with no one behind it is hard to argue with and harder to accept. What the customer needs is not only the answer, but who gave it and how to ask a person to look again.
The call (company’s AI to customer)
The phone rings about the delivery, and a voice asks the customer to confirm a time slot. It sounds like someone from the shop. In the EU, it now has to say that it isn’t. The customer has a few seconds to decide how much to tell it, and whether anyone will hear it later.
The customer who records (staff to customer’s AI)
Here the roles reverse. A customer wears glasses with a camera, or holds up a phone that transcribes the conversation. Now the associate is the one speaking to a listener they can’t see, and the second audience belongs to someone else: a video, a feed, a summary in an app. The staff member gets none of the notices this note asks retailers to give customers.
The customer’s AI reading the shop (shop to customer’s AI to customer)
A blind customer points their phone at the shelf, and an app reads the labels aloud. This is the role where AI most clearly opens a door. It also gives the shop a new reader. Shelf labels, packaging and price tickets were designed for a glance from a person; now they are also read by a machine on someone’s behalf. A label a camera can’t read is a label that customer can’t read either.
The translated counter (staff to AI to customer, and back)
A customer and an associate who don’t share a language talk through a translation app. Each hears a fluent version of the other. Neither can tell what was smoothed, softened or lost: the joke, the hesitation, the “I think” before a promise. The fluency hides the gap, which may be exactly why both of them trust it.
The interface is the listener’s face
Nobody sees the listener directly. They see an interface: a chat window with a name and a friendly avatar, a screen the customer can’t see, a microphone with no light, a summary card with no source. Each one is a sign that tells people who is listening, and people believe it.
Clarisse de Souza (a Brazilian computer scientist who founded semiotic engineering, a theory of interface design) describes every interface as a message from its designers: here is who we think you are, and here is how to talk to us. In this chain, that message is often silent about the one thing people most need to know, which is the second audience.
It helps to separate three things. The signal: the light is on. The inference: it is listening to us. The ground truth: what is actually stored, for how long, and who can open it. Most of the design problems in this chain sit in the gaps between the three. A friendly chat window can suggest a private conversation while the log is kept for months. A recording light can be on while nobody says who reads the transcript.
The law has started to catch up with the first audience. In the EU, from August 2026, an AI system that talks to people must tell them it is an AI, unless that is obvious, and the notice must meet accessibility requirements. The law says much less about an AI that is only listening in on a conversation between two people.
Tensions
Openness versus memory
The same quality that makes a machine easy to talk to can make it hard to trust. Feeling unjudged invites disclosure, including things like a disability someone would rather not explain to a stranger. Expecting a record invites caution. Customers may open up to the assistant while colleagues go quiet once their words become a transcript. It is the same technology, imagined as two different listeners: one that hears without judging, one that keeps everything.
Authority versus accountability
Fluent output arrives with more authority than it has earned. The colleague who raised a problem months ago loses to the answer that sounded finished. Trust here comes in three kinds that rarely move together: it hears me, its answer is right, and my words will be handled properly. A system can win the first and fail the other two. When it is wrong, nobody quite owns what it said, except the person who happened to relay it.
Authorship versus authenticity
Once the customer’s words, photo or voice may be made by a machine, the retailer starts to doubt the customer’s own signs. The complaint may not be in their words, the damage may not be real, the caller may not be a person. The usual response is a more demanding channel: a phone call, a video, a visit. The Financial Ombudsman Service has said it is phoning more complainants because AI-written submissions bury the real problem, and public bodies face the same problem with AI-written information requests. But the more demanding channel costs most for the people who relied on AI to get in touch in the first place.
The doubt runs the other way too. A customer who receives a smooth, complete reply, or a refusal with no name on it, starts to wonder whether anyone at the company read their complaint at all. Each side now asks the same question of the other: is there a person behind this?
Compression versus context
Each pass through AI compresses. The customer’s situation becomes a query, the conversation becomes a transcript, the transcript becomes a summary. Each step is meant to save time, and the context lost along the way is cumulative and invisible.
Eco would say that meaning depends on an encyclopedia: the shared knowledge a community draws on to make sense of what is said. There is a general one, which everyone shares, and smaller local ones, which only insiders have. A machine trained on the world carries much of the general encyclopedia. What it mostly lacks is the local one: how far you go for a customer when the policy says no, which exception is fine, what a regular means by “the usual”. Where it lacks the local, it fills in with the general, and it sounds just as sure.
Access versus accuracy
For some people the transcript isn’t a loss; it is the conversation. A Deaf customer reading live captions, a colleague who takes in written information more easily than speech, someone who prefers typing to speaking: for them, text is the way in. But speech recognition doesn’t hear everyone equally. It makes more mistakes with some accents, with stammers and with speech affected by disability, and it rarely says where it struggled. The people who most need the text are often the ones it gets wrong.
Speed versus formation
The informal talk a transcript discourages, and the conversation a handover note replaces, are where colleagues used to learn from each other: the aside, the honest “I’m not sure”, the workaround passed on at the till. Faster communication can mean slower judgement. That is the same debt I wrote about in The abstraction debt, seen from the side of conversation rather than work.
Design opportunities
Each of these separates what the interface shows from what the system actually does. One without the other is decoration.
1. Make both audiences visible when they matter
Label the assistant as AI before the first message. Beside the place where the customer types, show whether the conversation is saved, until when, and who can read it. Do this before asking for anything sensitive, and offer a route to a person at the same point.
2. Decide what the transcript is for, and tell both audiences
Tell staff plainly what is transcribed, who reads it, for how long and why. Give them a channel, or a moment, that isn’t kept, for the questions they would rather not have on record. Agree what can be said about customers on a transcribed channel: the jacket and the refund, not the customer’s name or circumstances. Where a customer’s own voice could be captured, there is no screen to tell them, so tell them in layers: a sign at the entrance and at the counter, a word from the colleague, a visible cue on the headset and a link to the full notice. Each layer reaches someone the others miss. And tell staff what they can do when the customer is the one recording.
3. Show where the answer came from
Mark AI-drafted answers on the associate’s screen before they are read out, with the policy or source beside each claim. Ask the associate to check, edit or reject the draft, and record who approved what was finally said. Do the same for written replies and automated decisions: say when a reply was drafted with AI or a refusal was made by a system, and tell the customer how to reach a person who can look again.
4. Confirm what the agent is about to do
Before the customer’s agent places an order, show the customer exactly what will be submitted and what data will be passed on. Record what they authorised separately from what the agent inferred. This stage has the least evidence behind it, so pilot it before rolling it out.
5. Let the summary carry its doubt
Label handover summaries as AI-generated, link each claim to its source, and separate what was observed from what the model inferred. Let the outgoing colleague correct the summary before the next shift reads it.
6. Give every voice the same chance of being heard correctly
Test transcription and summaries with the people most likely to be misheard: Deaf and hard-of-hearing staff and customers, people with regional accents, people who stammer. Show where confidence is low, and let people check and correct the record of what they said. The same applies to what the shop says back: test labels, packaging and translated answers with the apps customers use to read and hear them.
7. Check who is speaking without closing the door
When a complaint, a photo or a call needs checking, offer more than one way to do it: a phone call, a captioned video call, a visit or a few written questions answered without AI. Ask the customer which suits them. A check that only works by phone is a barrier, not a safeguard.
Behind all seven sits the same groundwork: short, defined retention periods, limits on who can open a transcript or summary, and a log of who did. A visible indicator is a promise. That groundwork is what keeps it.
The listener we design for
Service design has always listened to what people say: the interview, the complaint, the question at the counter. That was never the whole of what they meant, but it was said to someone they could see, and that person was, more or less, the audience.
That is no longer true. People now speak to one listener and are read by another. We design for the listener people can see. The harder problem is the audience they can’t: whoever reads the record next.
Sources
- Bell, A. (1984) Language style as audience design. Language in Society, 13(2).
- de Souza, C. S. (2005) The Semiotic Engineering of Human-Computer Interaction. MIT Press.
- Digital Markets, Competition and Consumers Act 2024: ban on fake reviews, in force from 6 April 2025.
- Draper, T. C. et al. (2025) Clinical AI scribes in primary care: accuracy, error severity and implications for clinical practice. BMJ Digital Health & AI. doi:10.1136/bmjdhai-2025-000092
- Eco, U. (1979) The Role of the Reader; (1984) Semiotics and the Philosophy of Language.
- EU Artificial Intelligence Act, Article 50(1) and 50(5). Regulation (EU) 2024/1689
- Financial Ombudsman Service (2026) Embracing AI’s transformational impact on consumer complaints. financial-ombudsman.org.uk
- Financial Ombudsman Service, comments by chief ombudsman James Dipple-Johnstone, reported in Insurance Business UK, October 2026. insurancebusinessmag.com
- Goffman, E. (1981) Forms of Talk. University of Pennsylvania Press.
- Holton, C. F. and Fuller, R. M. (2006) The impact of electronic monitoring on hazard communications. HICSS ’06. doi:10.1109/HICSS.2006.467
- Kim, T. W. et al. (2022) Do you mind if I ask you a personal question? How AI service agents alter consumer self-disclosure. Journal of Service Research. doi:10.1177/10946705221120232
- Koenecke, A. et al. (2020) Racial disparities in automated speech recognition. PNAS, 117(14). doi:10.1073/pnas.1915768117
- Mujtaba, D. et al. (2024) Lost in transcription: identifying and quantifying the accuracy biases of automatic speech recognition systems against disfluent speech. NAACL 2024. aclanthology.org
- Seita, M. et al. (2018) Behavioral changes in speakers who are automatically captioned in meetings with Deaf or hard-of-hearing peers. ASSETS ’18. doi:10.1145/3234695.3236355
- Urqueta Alfaro, A. et al. (2026) Automatic speech recognition underperforms with diverse accents as measured by word error rate and a semantic similarity measure. Disability and Rehabilitation: Assistive Technology. doi:10.1080/17483107.2026.2716598