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

5 March 2026

Paul Francis

Want your article or story on our site? Contact us here

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.

Current Most Read

5 Ways To Reduce Microplastics In Your Home

AI Everywhere: Innovation, Infrastructure, Investment and the Growing Backlash

The Lost Legends of Cinema: Films That Never Came to Be

Connor Banks
Aug 12, 2024
3 min read

In the glittering world of Hollywood, not all dreams make it to the silver screen. Some projects, despite their enormous potential and the star-studded talent attached to them, remain forever in the realm of "what could have been." Among these are some of the most intriguing and ambitious films never made, each with its own unique story that has captivated the imaginations of fans and filmmakers alike. From Alejandro Jodorowsky’s psychedelic epic to George Miller’s ambitious superhero ensemble, these unproduced films offer a glimpse into alternate cinematic realities.

Jodorowsky's Dune: The Psychedelic Epic

Jodorowsky's Dune stands out as perhaps the most legendary of these unfinished projects. In the mid-1970s, avant-garde filmmaker Alejandro Jodorowsky embarked on an audacious quest to adapt Frank Herbert’s science fiction masterpiece, "Dune." His vision was nothing short of revolutionary, intending to create a 10-14 hour cinematic experience that would transcend traditional film and become a transformative journey for viewers. Jodorowsky assembled an extraordinary team, including surrealist artist Salvador Dalí, Orson Welles, Mick Jagger, and H.R. Giger, with a soundtrack by Pink Floyd. Despite the staggering talent and creativity involved, the project was ultimately deemed too ambitious and costly. Financial and logistical issues, combined with Hollywood's reluctance to back such an unconventional vision, led to its demise. The story of "Jodorowsky’s Dune" was later immortalised in a 2013 documentary, offering a fascinating look at what might have been and showcasing the profound influence it had on future science fiction films.

The Man Who Killed Don Quixote: A Dream Delayed

The Man Who Killed Don Quixote concept art piece

Equally compelling is Terry Gilliam’s "The Man Who Killed Don Quixote." Gilliam, known for his work with Monty Python and his uniquely surreal directorial style, spent nearly three decades attempting to bring this project to life. The film, a loose adaptation of Miguel de Cervantes’ classic novel, faced an extraordinary array of setbacks. The initial production in 2000 was plagued by natural disasters, financial issues, and a severe back injury suffered by lead actor Jean Rochefort. These calamities, captured in the documentary "Lost in La Mancha," halted the project, and subsequent attempts to revive it faced similar challenges. It wasn’t until 2018 that Gilliam finally completed the film, though it differed significantly from his original vision. The journey of "The Man Who Killed Don Quixote" remains a testament to artistic perseverance, highlighting the often tumultuous path from script to screen.

Atuk: The Cursed Comedy

"Atuk," based on Mordecai Richler’s novel "The Incomparable Atuk," has earned its place in Hollywood legend due to the so-called "Atuk curse." This comedy about an Inuit navigating the modern urban jungle was attached to several high-profile actors, each of whom died under tragic and unexpected circumstances before production could begin. John Belushi, Sam Kinison, John Candy, and Chris Farley all expressed interest or were cast in the lead role, only to meet untimely deaths. The eerie pattern of misfortune has led to a macabre fascination with the project, ensuring that "Atuk" remains one of the most infamous unproduced films in history.

Batman: Year One: The Dark Reimagining

Concept of Gotham City as seen from Above

In the realm of superhero cinema, Darren Aronofsky’s "Batman: Year One" represents a radical departure from the traditional portrayals of the Dark Knight. Aronofsky, known for his dark and psychologically intense films, envisioned a gritty reboot of Batman that would strip the character down to his essence. This version of Bruce Wayne would lose his fortune, live on the streets, and don a makeshift costume. Despite the intriguing premise, Warner Bros. ultimately chose a different path, opting for Christopher Nolan’s "Batman Begins," which balanced realism with a more traditional narrative. Aronofsky’s bold vision remains a fascinating "what if" scenario, reflecting the creative risks involved in reimagining iconic characters.

Justice League: Mortal: The Superhero Ensemble That Almost Was

Finally, George Miller’s "Justice League: Mortal" was an ambitious attempt to bring together DC Comics' most iconic superheroes in a single film long before the success of the Marvel Cinematic Universe. With a cast that included Armie Hammer as Batman, D.J. Cotrona as Superman, and Megan Gale as Wonder Woman, the project promised a sprawling, epic narrative. However, it was plagued by a series of setbacks, including the 2007-2008 Writers Guild of America strike, financial issues, and concerns over audience confusion due to multiple actors playing the same characters in different franchises. Despite never being made, "Justice League: Mortal" has become a source of endless speculation and interest, illustrating the complexities and challenges of launching a shared cinematic universe.

The Allure of the Unmade

These unproduced films, each with their unique blend of ambition, talent, and misfortune, offer a tantalising glimpse into the alternate realities of cinema. They stand as reminders of the fragile nature of filmmaking, where even the most promising projects can falter and fall into the realm of legend. Yet, their stories continue to inspire, serving as both cautionary tales and sources of endless fascination for those who dream of what might have been.