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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Designed to Be Replaced: How Planned Obsolescence Fuels Waste in the Digital Age

Paul Francis
Nov 12, 2025
4 min read

As the festive season approaches and millions prepare to give new phones as gifts, there is an uncomfortable truth beneath the shine of packaging and ribbon. Globally, smartphone sales continue to grow, and by late 2025, analysts expect more than 180 million new phones will be gifted worldwide over the Christmas and holiday period. The result is a surge of electronic waste, much of it tied to devices that still function perfectly well.

Pile of discarded cell phones and electronics in a landfill, under overcast skies. Scattered cables and tires create a sense of waste.

This phenomenon is closely linked to planned obsolescence, the practice of deliberately designing products to have limited lifespans so that they are replaced sooner than necessary. While technological progress drives convenience and innovation, the environmental cost of constant replacement is becoming impossible to ignore.

The Roots of Planned Obsolescence

The idea of designing for failure is not new. In the early twentieth century, companies sought ways to increase sales in an already saturated market. One of the earliest and most infamous examples came from the Phoebus Cartel, formed in the 1920s by major light bulb manufacturers such as General Electric, Osram and Philips. They agreed to limit the lifespan of light bulbs to around 1,000 hours, ensuring repeat purchases and steady demand.

In the automotive industry, General Motors took a more subtle approach. Under the leadership of Alfred P. Sloan Jr., GM introduced yearly styling updates to its vehicles, making older models look outdated even if they were mechanically sound. By the 1950s, this idea of “dynamic obsolescence” had become a core part of the car industry’s marketing strategy. Consumers were encouraged to buy a new car not because the old one had failed, but because it no longer looked fashionable.

This approach worked so well that the average ownership period of a new car in the United States fell from five years in the 1930s to around two years by the mid-1950s.

The Modern Battlefront: Electronics

Today, the same principles apply to consumer electronics. Phones, laptops, tablets and even smart appliances are updated annually with minor design or software changes. Marketing emphasises the new features while subtly implying that last year’s model is inferior.

Software updates also play a role. Older devices often stop receiving updates, making them less secure and incompatible with new apps. Hardware designs that prevent users from replacing batteries or repairing parts further shorten a product’s usable life.

The environmental impact is staggering. In 2024, the world produced around 62 million tonnes of electronic waste, a figure expected to reach 75 million tonnes by 2030, according to the United Nations Global E-waste Monitor. Only about 20 per cent of this waste is properly recycled.

When we consider that tens of millions of new phones will be purchased and gifted this Christmas, the scale of the problem becomes even clearer. Each device requires metals such as lithium, cobalt, gold and nickel, all of which come from resource-intensive mining processes that damage ecosystems and contribute to carbon emissions.

The Environmental Cost of Short-Lived Design

Planned obsolescence harms the environment at every stage of a product’s life cycle.

Manufacturing requires extraction of raw materials, water use and energy-intensive production.
Distribution and transport add carbon emissions and packaging waste.
Disposal leads to landfill waste and the release of toxic substances, including lead, mercury and cadmium.

Devices that could have been repaired or refurbished often end up discarded because it is cheaper to buy new than to fix the old. Repair restrictions and closed design systems make it even harder for consumers to extend product life.

The environmental consequences of this pattern go far beyond landfills. E-waste frequently ends up exported to developing countries, where informal recycling exposes workers to hazardous materials without proper safety equipment.

Is Planned Obsolescence a Design Flaw or a Business Strategy?

Manufacturers argue that regular product refreshes promote innovation and create jobs. They claim that shorter product cycles allow faster adoption of new technology, such as energy-efficient screens or improved processors.

However, critics point out that this cycle primarily benefits profit margins rather than the planet. Many of the annual “upgrades” in smartphones or consumer electronics are incremental rather than revolutionary. A new colour, camera mode or interface rarely justifies replacing a working device.

In effect, marketing has replaced mechanical failure as the main driver of obsolescence. Consumers are encouraged to buy the latest model not because they need it, but because they feel left behind if they do not.

The Global Response

Governments and regulators are beginning to take notice.

The European Union’s Circular Economy Action Plan now requires manufacturers to make products more durable, repairable and recyclable.
France has introduced a repairability index that scores electronics based on how easy they are to repair.
The United Kingdom has introduced Right to Repair legislation, forcing appliance manufacturers to supply spare parts for up to ten years.
In the United States, several states have passed or proposed similar laws to give consumers and independent technicians access to parts and repair manuals.

Public attitudes are also shifting. A growing number of consumers now consider environmental sustainability in purchasing decisions, especially during holiday periods. The second-hand and refurbished electronics market is thriving, and companies offering longer warranties are gaining favour.

A Sustainable Approach to the Festive Season

With Christmas around the corner, consumers can make choices that help reduce waste.

Repair instead of replace: A simple battery replacement or software refresh can extend a phone’s life by years.
Buy refurbished: Certified refurbished devices perform as well as new ones but come at a lower environmental cost.
Recycle responsibly: Use verified e-waste collection schemes rather than general waste disposal.
Support brands committed to sustainability: Some companies now design phones with modular parts that can be easily swapped or repaired.

Every small decision makes a difference when multiplied by millions of households.

Planned obsolescence may once have driven economic growth, but its environmental consequences are now undeniable. The constant cycle of buying, discarding and upgrading has created one of the fastest-growing waste streams on Earth.

As we enter another season of gifting and consumption, the challenge is clear: innovation must no longer mean replacement. It must mean resilience, repair and responsibility.

If consumers demand it and manufacturers respond, the devices under next year’s Christmas tree could tell a different story, one of sustainability instead of waste.