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

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 meal with wrapped burger, fries, and drink cup with logo on table. Bright, casual setting, with focus on branded items.

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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Breaking Down the Most Memorable Eurovision 2024 Entries: Part 3

  • Writer: Connor Banks
    Connor Banks
  • May 20, 2024
  • 8 min read

Well I’ve reviewed the majority of the songs from this years Eurovision, which just leaves the last 13 songs to be reviewed. So lets not waste anymore time and get right into the last 13 songs left from this years final.


Norway “Ulveham” Gåte


Despite some ups and downs, Norway remains a beloved and respected competitor on the Eurovision stage. Known for its resilience and diverse musical entries, Norway continues to captivate audiences with its unique blend of contemporary and culturally rich performances. This year they were represented by Gåte, a band known for blending Norwegian folk music with elements of metal and electronica, and they bring that distinctive style to "Ulveham".

Gåte's performance was a visual spectacle utilising powerful, almost mythical quality, vibes which made it stand out from a lot of the other songs. But despite that the song only finished 16th, this in my opinion is an international tragedy and one that we all should be ashamed of. The unique blend of folk traditions and contemporary rock, combined with the intense staging, are all aspects of what actually makes Eurovision great. This song deserved to break into the top 10.



Italy “La Noia” Angelina Mango


Italy this year were represented by “La Noia” by Angelina Mango. Italy have had a long history in Eurovision and have won it 3 previous times, there most recent win being in 2021 but was this going to be another year of Italian dominance? La Noia mixes cumbia rhythms with pop elements, creating a unique and catchy vibe that's hard to resist​. Angelina Mango’s performance is full of energy and charisma, her vocal delivery is both powerful and authentic, bringing a relatable touch to the song. The lyrics talk about the mundane aspects of life and existential boredom, but the upbeat music keeps it engaging and fun. But on top of that the stage performance was very eye-catching and engaging. With a psychedelic forest theme, complete with elaborate graphics and costumes, and a throne that rises from the stage. This was easily one of the best songs of this year's Eurovision, and if it wasn't for tougher competition it probably would have ranked higher, overall it finished 7th and I feel like this is the perfect place for it to sit.



Serbia “Ramonda” by Teya Dora


Serbia has made a significant impact on the Eurovision Song Contest since its debut in 2007. Known for its powerful ballads, diverse musical entries, and cultural richness, Serbia consistently delivers memorable and high-quality performances. This year was no exception with another powerful ballad from Teya Dora with “Ramonda”. The song is a beautiful ballad that dives deep into themes of solitude, resilience, and hope. The lilac ramonda, which is a recurring motif in the song, symbolises endurance and rebirth as well as being a significant flower in Serbian culture. Teya Dora's performance is really something special. Her voice carries so much emotion and sincerity, which makes the song incredibly moving. The staging for "Ramonda" also enhances its impact. With subtle yet effective elements like flower motifs, the visuals complement the song’s melancholic yet hopeful message perfectly. It’s not overly flashy, which works well with the song’s introspective nature. This song was one of my favourites from this years Eurovision, its just a shame that theres another 10 songs that I feel deserve top 10 more than it, however I would like to say it should have challenged for one of those spots rather than being relegated to a 17th place finish.



Finland “No Rules!” Windows95man


Finland has made a notable impact on the Eurovision Song Contest with its diverse musical entries and memorable performances. Highlighted by Lordi’s groundbreaking victory in 2006 with "Hard Rock Hallelujah" and Käärijä’s innovative "Cha Cha Cha" in 2023, Finland is known for its ability to surprise and captivate audiences. This year their song was “No Rules!” by Windows95man and it's definitely one of the more unique songs in the competition. The song is a high-energy Europop anthem that really stands out for its infectious rhythm and playful, over-the-top presentation but whilst it does stand out for all of these reasons, they are all reasons as to why the song does feel a little cheesy even for Eurovision. Overall, "No Rules!" is a mixed bag and it finished 19th in the competition, again I feel like around this spot is about right.



Portugal "Grito" Iolanda


Portugal has had a rich and evolving history in the Eurovision Song Contest, marked by its commitment to cultural authenticity and distinctive musical entries. Known for its beautiful melodies and emotive performances, Portugal has made a significant impact on the contest. This trend continued with this year’s entry. “Grito” by Iolanda, translates to "Shout," is a beautiful fusion of pop, R&B, and traditional Portuguese Fado influences. Iolanda's vocal performance is incredibly powerful. From the haunting acapella opening to the huge note at the end, her voice fills every corner of the arena. The staging is equally impressive, featuring five dancers in white with covered faces, adding a contemporary and artistic touch that perfectly complements the song's emotional depth. The lyrics of "Grito" are all about self-empowerment, healing, and the pursuit of dreams. It's an inspiring message that resonates deeply, celebrating the indomitable human spirit and the journey to overcome adversity​ and is by far one of my favourites of this year, overall it finished 10th and I’m going to say it definitely earned that top 10 finish.



Armenia “Jako” Ladaniva


​Performed entirely in Armenian, “Jako” features a vibrant mix of Armenian folk music with global influences like Balkan melodies, jazz, reggae, and maloya. The performance is just as lively as the song itself. The staging is colourful and dynamic, featuring Armenian motifs and a fun dance routine. Lead singer Jaklin Baghdasaryan sings about her experiences growing up and being told to behave a certain way. Instead of conforming, she embraces her true, unapologetic self, and the song becomes a call to dance and live freely. The song is very catchy, and it managed to finish 8th in the actual final. I’m not sure if I would place it in the top ten but it was definitely one of the standout performers of this year.



Cyprus “Liar” Silia Kapsis


Cyprus is known for its vibrant performances and catchy pop songs. With a strong track record of qualifying for the finals and a commitment to showcasing both contemporary and culturally rich music, Cyprus continues to be a dynamic and beloved participant in Eurovision, bringing fresh and memorable performances to the stage each year. This year they were represented by 17 year old Silia Kapsis. "Liar" combines a modern pop sound with a catchy beat that's hard to get out of your head. Silia's performance is full of energy, and she really brings the song to life on stage. "Liar" is a strong entry for Cyprus, blending contemporary pop with a meaningful message and a compelling performance. It finished 15th overall and whilst I would like to rank it a little higher I don’t think it’s good enough to break into the top 10.



Switzerland “The Code” Nemo


Switzerland won this years Eurovision with this song by Nemo. “The Code” became the first victorious song for the Swiss since 1988 and the first winning song ever to have been sung by an openly non-binary person. The song is a complex blend of genres, combining elements of rap, opera, pop, and drum and bass, which has been praised for its originality and innovation. Nemo, known for their powerful stage presence and vocal versatility, delivers a performance that includes vocal trills, belts, and falsetto notes, making "The Code" a memorable and dynamic entry. This song was easily in my top 3 songs from this years Eurovision. I personally would have had Europapa win ahead of it, but unfortunately circumstances said otherwise.



Slovenia “Veronika” Raiven


Slovenia were represented this year by Raiven with the song “Veronika”. The track is inspired by the historical figure Veronika Deseniška, a countess from the 15th century who faced tragic accusations of witchcraft. But other than, I mostly forgot about this song. Which is unfortunate as it did feel as though Slovenia were trying to do something unique with this entry. It just unfortunately didnt work out this time. Overall it finished 23rd, I probably would have had it rank lower.



Croatia “Rim Tim Tagi Dim” Baby Lasagna


Croatia really struck it big with this song, it's just a shame that it happened to be submitted when the competition this year was so strong. The alt rock “Rim Tim Tagi Dim” had one of the catchiest choruses of this year’s Eurovision. Baby Lasagna has put together a performance that’s visually stunning and full of energy. The music itself is vibrant and lively, blending traditional Croatian sounds with a modern twist. This fusion creates an infectious rhythm that makes you want to dance. Baby Lasagna’s vocal delivery adds to the song's charm, bringing a playful and energetic vibe that perfectly matches the upbeat tempo of the music. This was by far my 2nd favourite song of the entire competition and it did finish 2nd overall in the actual final so I guess we agree on something?



Georgia “Firefighter” Nutsa Buzaladze


This year Georgia were represented by Nutsa and it paid off for them as they broke their streak of failing to qualify for the final. However that's pretty much as far as the achievements of this song go, its a fine song and Nutsa has great vocals but it wasn’t anything special. I don’t really have much more to say about it which probably isnt a great thing. It finished 21st, I think it could have finished further down and no one would’ve been upset.



France “Mon Amour” Slimane


France's entry for Eurovision 2024, "Mon Amour" by Slimane, is a standout ballad that has captivated audiences and critics alike with its profound emotional depth and powerful vocal delivery. From the very first note, "Mon Amour" grips listeners with its intricate melody and heartfelt lyrics. The song's structure, beginning softly and building to a dramatic crescendo, allows Slimane to showcase his vocal prowess and emotional expressiveness. A very memorable moment is when Slimane walks away from the microphone as the music cuts out, and then continues to sing whilst standing away from the microphone as his voice fills the arena. This song and performance are absolutely beautiful and was one of my favourites of the entire show. It’s definitely in the top 5 for me this year, which it managed to do as it finished 4th overall.



Austria “We Will Rave” Kaleen


Kaleen, whose real name is Marie-Sophie Kreissl, is representing Austria with a techno-pop anthem that takes a nostalgic dive into 90s Eurodance. The song, "We Will Rave," is all about finding joy and unity in the midst of darkness, using dance as a way to heal and connect. However there were quite a few other retro inspired entries this year, all of which I think might have been a little bit better than this song. Or maybe it’s just fatigue from having to sit through all of the songs of this years Eurovision. Either way, wasn’t a huge fan of this one. It finished 24th which I think is about right for this song.


And thats that, I’ve finally reviewed all of the songs from this years Eurovision. I’m sure I’ve annoyed some people with my opinions but hey thats what makes music and arts good, that we can all take away different meanings and interpretations from them. If I’ve said something that you disagree with then just let us know. If you’ve not checked out any of the other parts then I recommend you do. Either way I’m looking forward to next years Eurovision in the heart of the alps in Switzerland!

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