When AI Measures “Friendliness”: Who Decides What Good Service Sounds Like?

5 March 2026

Paul Francis

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Artificial intelligence is moving steadily from assisting workers to assessing them.

Cashier with robotic eyes, wearing a headset in a fast-food setting. Neon colors on screens in the background create a futuristic vibe.

Burger King has begun piloting an AI system in parts of the United States that listens to staff interactions through headsets and analyses speech patterns. The system, reportedly known as “Patty,” is designed to help managers track operational performance and, more controversially, measure staff “friendliness.” It does this by detecting politeness cues such as whether employees say “welcome,” “please,” or “thank you.”

From a corporate perspective, the logic is clear. Fast food is built on consistency. Brand standards matter. Customer experience scores influence revenue. If AI can help managers see patterns across shifts and locations, it promises efficiency, insight and improved service quality. On paper, it sounds like innovation.

In practice, it raises deeper questions about surveillance, culture, authenticity and who gets to define what “friendly” actually means, Because friendliness is not a checkbox, It is human.

The Promise Versus the Reality

The official line from companies testing this technology is that it is a coaching tool rather than a disciplinary one. It is presented as support for staff, helping identify trends rather than scoring individuals. It is framed as data-driven improvement rather than digital oversight, but the moment speech is analysed, quantified and turned into a metric, something changes.

Service work has always required emotional intelligence. It has also required emotional labour. Employees adjust tone, language and pace depending on the situation in front of them. A lunchtime rush feels different from a quiet mid-afternoon shift. A tired commuter is different from a group of teenagers. A frustrated parent is different from a regular parent who comes in every day.

Anyone who has worked in face-to-face customer service understands this instinctively. Your tone changes, your rhythm changes, your humour changes, and that is precisely where the friction with AI begins.

Culture Cannot Be Reduced to Keywords

One of the most immediate concerns is accent and cultural bias. Speech recognition systems are not neutral; they are trained on datasets. Those datasets may not equally represent every regional accent, dialect or speech pattern.

Hungry Jack's sign above a red canopy on a city street corner. Traffic light displays red pedestrian signal with trees and buildings in the background.

In a noisy fast food environment, with headsets, background clatter and rapid speech, even minor variations can affect recognition accuracy. If an AI system relies heavily on detecting specific words, then any difficulty interpreting accents could skew the data. That is not a theoretical concern. Studies have shown that automated speech systems often perform better on standardised forms of English and less well on regional or non-native accents. If politeness metrics depend on exact phrasing, workers with stronger regional accents or different speech rhythms could appear less compliant in the data, even when their service is perfectly warm and appropriate.

Beyond pronunciation, there is the question of cultural expression. In some regions, friendliness is relaxed and informal. In others, it is brisk and efficient. In some communities, humour and banter are part of service culture. In others, restraint and professionalism are valued. AI systems do not instinctively understand these nuances. They detect patterns.

But hospitality is not a pattern. It is a relationship.

Who Sets the Definition of Friendly?

This leads to a more fundamental question. Who decides what counts as friendly?

These systems do not calibrate themselves. Someone defines the threshold. Someone selects the keywords. Someone decides how often “thank you” should be said and in what context. Those decisions are typically made at the corporate level, often by operations teams and technology partners working from brand guidelines and idealised customer journeys.

There is nothing inherently wrong with brand standards, but there is often a distance between corporate design and frontline reality.

Business meeting with people at a wooden table, one reading a marketing plan. Laptops, coffee cups, and documents on the table.

Many workplace policies are written by people who have not worked a drive-thru shift in years, if ever. They may be excellent strategists. They may understand customer data deeply. But that does not always translate into lived experience on a busy Saturday afternoon when the fryer breaks and the queue is out the door.

In those moments, efficiency may matter more than repetition of scripted politeness.

If an algorithm expects a perfectly phrased greeting under all conditions, it risks becoming disconnected from the environment it is meant to improve.

Once those expectations are embedded in software, they become harder to question. The algorithm becomes policy.

The Authenticity Problem

Having worked in face-to-face customer service myself, I know that the best interactions were rarely scripted. Regular customers would come in, and you would adjust instantly. You might joke with them. You might take the piss in a friendly way. You might shorten the greeting entirely because familiarity made it unnecessary. That rapport is built over time and trust. Would an AI system recognise that as excellent service? Or would it mark down the interaction because the expected keywords were missing?

Hospitality is dynamic. It depends on reading the room, reading the person, and reading the moment. If workers begin focusing on hitting verbal benchmarks rather than engaging naturally, the interaction risks becoming mechanical. Customers can tell the difference between genuine warmth and box-ticking politeness. Ironically, quantifying friendliness may reduce the very authenticity companies are trying to protect.

Surveillance or Support?

This is where the tone of the debate shifts. Because even if the system is introduced as a supportive tool, the psychological reality of being monitored is not neutral.

Anyone who has worked in customer-facing roles knows that service environments are already performance spaces. You are representing the brand; you are expected to maintain composure and remain polite, even when customers are not. That emotional regulation is part of the job. Now imagine adding a layer where your tone and phrasing are being analysed in real time by software.

Hand holding a cassette recorder in focus, with blurred figures in business attire seated at a table in the background.

Even if managers insist it is not punitive, the awareness that your speech is being measured changes behaviour. You begin to think not just about the customer in front of you, but about whether the system has “heard” the right words. In high-pressure environments, that is another cognitive load. Another thing to get right. Over time, that kind of monitoring can subtly alter workplace culture. It can shift service from something relational to something performative in a more rigid way. Employees may begin speaking not to connect, but to comply, and when compliance becomes the goal, service risks losing its texture.

Supportive technology tends to feel like something that works with you. Surveillance, even when softly framed, feels like something that watches you. The distinction matters, particularly in lower-wage sectors where workers have limited influence over policy decisions.

The Broader Direction of Travel

What makes this story significant is that it does not exist in isolation. It is part of a wider pattern in which AI is moving steadily from automating tasks to evaluating behaviour.

First, algorithms helped optimise stock levels and predict demand. Then they began assisting with scheduling and logistics. Now they are increasingly assessing how people speak, how they respond and how closely they align with brand standards. Each step may seem incremental. Taken together, they represent a fundamental shift in how work is structured and supervised.

Historically, managers evaluated service quality through observation, feedback and experience. There was room for interpretation, for context, for understanding that a difficult shift or a complex interaction could influence tone. Human judgment allowed for nuance.

When evaluation becomes data-driven, nuance can be harder to capture. Metrics tend to favour what is measurable. Words are measurable. Frequency is measurable. Context is far less so. The risk is not that AI becomes tyrannical overnight. The risk is that over time, it narrows the definition of good service to what can be quantified. And what can be quantified is rarely the full story.

A Question Worth Asking

Technology reflects priorities. If a company invests in systems that measure friendliness, it is signalling that friendliness can be standardised, monitored and optimised like any other operational metric, but service is not assembly. It is interaction.

It is shaped by region, by culture, by individual personality and by the particular chemistry between staff and customer in that moment. It shifts depending on who walks through the door. It changes across communities and demographics. It even evolves over the course of a day. When AI systems define behavioural benchmarks, someone has decided what the ideal interaction sounds like. That definition may come from brand research, from head office strategy sessions or from consultants analysing survey data. It may be carefully considered. It may be well-intentioned, but it is still a definition created at a distance from the frontline.

Many workplace standards across industries are designed by people who have not stood behind a till in years. That does not invalidate their expertise, but it does introduce a gap between theory and practice. When those standards are encoded into algorithms, that gap can become structural. The core issue is not whether AI can improve service. It is whether those deploying it are prepared to listen as carefully to staff experience as the system listens to staff voices. If friendliness becomes a metric, then it is fair to ask who sets the parameters, how flexible they are, and whether they reflect the messy, human reality of service work.

Because once the headset becomes the evaluator, the definition of “good” may no longer be negotiated on the shop floor and that is a shift worth paying attention to.

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After the Machines: Can Creative Work Survive the AI Age?

Paul Francis
Jul 30, 2025
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It started with a row of birthday cards.

While shopping at a local Tesco, I spotted a display full of birthday cards that didn’t look quite right. At first glance, they seemed like any other range of quirky illustrations and sentimental messages, but something was off. The characters had odd expressions, the hands and proportions weren’t quite human, and there was that unmistakable uncanny quality that comes from AI-generated art.

Greeting cards on display feature animals, kids, and humorous themes. Categories include "Almost Funny," "Get Well," and "Thank You."

I work in the creative industry and regularly use tools like Leonardo AI. I recognised the signs immediately. Every single one of those cards had been made by a machine.

It was a quietly shocking moment. Not because AI art exists, we’ve all seen it by now, but because it has gone mainstream, tucked into a supermarket aisle where once there had been work by real illustrators and designers. The thought struck hard: this is already happening, and it’s happening faster than people realise.

But as creative work becomes cheaper to generate, a bigger question emerges: when most people have lost their jobs to AI, who will still have the money to buy what these companies are selling?

The Jobs at Risk

Freelance illustrators designing cards and similar products might typically earn between £30 and £250 per piece, depending on the client and usage. Over the course of a year, a dedicated freelancer might bring in between £25,000 and £35,000, though that varies with commissions and demand.

It’s not a high-income job, but it supports a wide network of creative professionals, from recent graduates to long-time freelancers. These are the very people now being undercut by companies using generative AI tools to produce hundreds of designs in hours.

AI-generated content is already appearing in online marketplaces, book covers, and even music videos. It’s a quiet revolution, and not one that has left much time for retraining or regulation.

Surreal cityscape with geometric buildings, pastel colors, floating spheres, and sketched figures. The mood is dreamlike and tranquil.

If Jobs Go, What Happens Next?

The reality is simple: if creative workers lose their incomes, their ability to participate in the economy vanishes with it.

One widely discussed solution is Universal Basic Income (UBI). The concept involves giving every citizen a regular, unconditional payment to cover essential living costs. Trials in Finland, Canada and the United States have shown promising results. People were able to focus on long-term goals, retrain, or pursue creative work without the pressure of living month to month.

However, critics argue UBI could be expensive to sustain and difficult to fund without significant changes to taxation. Even so, in a world where AI threatens jobs across multiple industries, such support systems may soon become a necessity.

New Creative Roles With AI in the Loop

Some companies are working towards new hybrid roles. Instead of replacing creative professionals, they aim to involve them in the AI process.

Examples include:

AI Prompt Artists, who specialise in writing detailed inputs to guide AI tools.
Creative Curators, who review AI-generated work and refine it for production.
AI Trainers, often artists themselves, who help improve how generative models understand style and composition.

While these roles are still emerging, they offer a glimpse into a future where creativity doesn’t disappear, but shifts into new forms.

Protecting the Artists Who Came First

There’s growing pressure on governments and platforms to protect the rights of original artists. Most AI tools are trained on vast datasets scraped from the internet, often without consent.

Several lawsuits are already underway, challenging the legality of this training data. In response, the EU’s AI Act and similar legislation in the UK may soon require greater transparency, and even give artists the option to opt out of training datasets.

Some creatives are also calling for a royalties system. Just as musicians earn money when their songs are streamed, visual artists could receive micropayments when their style or content is used in an AI-generated image.

Consumer Power and the "Human Made" Movement

A growing number of consumers are beginning to notice when something is made by AI. In response, some companies are experimenting with Human Made labels, signalling when a product or design is created without AI tools.

This shift could give consumers the power to support real artists directly. Subscription platforms like Patreon and Ko-fi already allow for fan-driven support, and ethical marketplaces are beginning to highlight human creators.

But the movement needs wider awareness to have a lasting impact.

The Bigger Picture

No technology arrives in isolation. AI isn’t just changing how we work; it’s changing how we value work.

If companies can produce products without human labour, but also eliminate the spending power of the people they replaced, they risk breaking the cycle that keeps economies turning.

The Tesco card display was a small moment, but it points to a much larger shift. As a creative, it made me question where things are heading, and what it might take to ensure there’s still room for real human talent in the world ahead.

The machines are here. What happens next is up to us.