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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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A Music Illiterate Reviews The Eurovision Finals Part 2

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

I previously went and reviewed the songs that did not make it through to this years Eurovision final, there were a few hidden gems and songs that definitely deserved their flowers and a spot in the final. But now’s the time to go ahead and review every performance from this year's Eurovision final, as there are 26 songs this article will go over the first 13 songs from this year's final, starting off with hosts Sweden!


Sweden “Unforgettable” Marcus & Martinus


Sweden has been a powerhouse in Eurovision for quite some time now having the joint most wins along with Ireland. Eurovision is where many Swedish acts have managed to break into the mainstream world of music, such as ABBA, and so every Swedish act almost immediately has this incredibly high expectation of producing a good track and performance. So surely this year they’d be able to do the same right? I mean with a song called “Unforgettable” surely no one is going to forget that one! Except, I’ll be honest, this might be the most forgettable song out of all the songs performed at this years final. The song is an electronic pop song and featured one of the most generic beats for a song and yet it finished 9th, which I get the staging was kind of fun to watch but I don't think the song was special enough to warrant a 9th place finish. If Sweden were not hosting, I don’t think this would have qualified ahead of some of the songs from the semi final. Definitely should have been a bottom 5 finish.



Ukraine "Teresa & Maria" Alyona Alyona and Jerry Heil


Next up is Ukraine. Ukraine have had a series of very strong performances in recent years, and a lot in general since they joined back in 2003, usually concocting a unique blend of performances, cultural elements, and contemporary music. And this year was no different with “Teresa & Maria” a song about hope inspired by Albanian Roman Catholic saint Mother Teresa and the Virgin Mary, the song was written by Alyona, Heil, Anton Chilibi, and Ivan Klymenko. I personally really enjoyed this song, Ukraine always has strong entries into eurovision and this years was just another in a long list of bangers. Easily one of my favourites from the entire show but I probably would not have ranked it as the 3rd best from the night, for me it would sit around 5th/6th.



Germany “Always on The Run” Isaak


Germany has had a rough relationship with Eurovision in recent years, finishing near last place in the last 4 contests. But could this year break the streak of poor performances this year? Well it did! The song managed to finish 12th overall a much needed improvement for Germany over their last few entries, however I do think this is a bit unearned. Whilst its great that Germany have managed to finally produce an entry that can break above dead last or 2nd to last, positions normally reserved for the UK and Germany, I don’t think this song was unique enough to warrant that drastic rise. The song is a lot like if you somehow made Lewis Capaldi more boring and German. A 12th place finish is probably a little high for this type of entry especially when Eurovision is known for its eccentricity and I don’t know if this song truly is that much better than their previous entries in previous years. I feel a finish around 20th would have been an improvement and more accurate as to what the song was.



Luxembourg “Fighter” by Tali


Luxembourg haven’t been in a Eurovision for over 30 years, with this year being the first time they’ve performed since 1993. Despite its absence from recent contests, Luxembourg's legacy in Eurovision remains influential, and its winning entries are still celebrated. So expectations were high for this former powerhouse of the competition. And I think it was a strong re-entry into the competition with the song “Fighter”. The song has a catchy beat and honestly is very Eurovision, for those who regularly watch the competition you’ll know what i mean. Singing in both English and French, Tali’s performance involved her dancing with a group of male dancers whilst a CGI Leopard roared behind her, the whole thing was very camp and what we love about this yearly competition. The song finished 13th on the night, and I feel like that’s fair however this does mean it finished below Germany somehow.


Netherlands “Europapa” Joost Klien


Going into the competition, this was the favourite to win by almost everyone. Joost Klien had made the most Eurovision-Europop song that he possibly could have made and on top of that, it was fun to listen to. The song had gone viral on social media platform TikTok with many people using the sound all across Europe, peaking at number 1 in the Netherlands. This wasn’t just a song that was loved for the Eurovision moment but also by its home-country. Europapa also had a deeper meaning behind it as Joost had used his own personal experiences after the tragic passing of his parents when he was young. The song was massively loved by fans, during the live performance at the Semi Final the entire crowd was singing and clapping along. But hours before the competition took place, Joost was disqualified from the competition after an alleged incident occurred backstage. This backstage incident occurred moments after Joost had called out the Israeli entrant during a press conference leading many to speculate that the 2 events might be linked. Whatever your opinion on that matter, I think that this song is the true winner of the entire competition. Europapa is my number 1. The song is Eurovision.



Israel “Hurricane” Eden Golan

Another entry with controversy this year, with many fans protesting and boycotting the show simply because they were allowed to perform. And whilst there are a lot of questions in regards to that, for the purpose of this article I am only going to focus on the song, the singer, and the performance. Whilst Eurovision is often entwined with politics, whether intentionally or not, I will refrain from doing so.

Anyway the song itself, the song is a sort of generic power ballad, reminiscent of the series of former Disney stars doing power ballads in the early 10s to try and break from their Disney molde. The song when only looking at public voting finished 2nd, which honestly is wild that it got that many votes. Is it a bad song? No. Is it a good song? It’s alright. Is it a good Eurovision song? Also no. The song doesn't stand out for anything other than the politics behind the scenes. The song deserved to finish somewhere in the mid table, I’m going with 12th.



Lithuania “Luktelk” Silvester Belt


Lithuania has been a consistent and ambitious participant in the Eurovision Song Contest since its debut in 1994. Although it has yet to secure a win, the country has made a significant impact with several memorable performances and strong entries. And they had another memorable performance this year from Silvester Belt. This year's song is an interesting mix of 90s techno-pop and modern elements. On one hand, the song has a catchy rhythm and a memorable hook that some people find appealing. It brings a nostalgic feel with its retro influences, which might resonate with fans of that era. The lyrics delve into themes of time and reflection, adding a bit of depth to the otherwise danceable track​. However, "Luktelk" struggled to stand out in a competitive Eurovision lineup. This year features several other entries with similar 90s-inspired sounds, such as Finland's Windows95Man and Austria’s Kaleen with "We Will Rave." Overall it finished in 14th place, and I feel this is about right for the song. I like it but it wasn’t anything special or uniquely Eurovision.



Spain “Zorra” Nebulossa


Spain has had a significant and enduring presence in the Eurovision Song Contest, being one of the big 5 nations mean they don’t need to compete in the semi finals to qualify for the final. This years entry tries to reclaim the term "zorra," which translates to "slut" or "bitch," aiming to make a strong feminist statement. A commendable goal for sure, but outside of the meaning of the song it is an electro-pop track that feels somewhat generic. It doesn’t really stand out in the lineup, especially with other strong entries this year. The melody and production are decent but nothing groundbreaking. For a song that's supposed to be so bold and defiant, it doesn't bring anything particularly new or exciting to the table. And this song finished 22nd overall, which honestly I agree with. It definitely shouldn’t have finished any higher.



Estonia"(Nendest) narkootikumidest ei tea me (küll) midagi" 5miinust and Puuluup


Estonia has made a significant impact on the Eurovision Song Contest with its diverse and high-quality entries. The country achieved a historic victory in 2001 with "Everybody" but did this years entry live up to that former glory? This year's song blends hip-hop and modern folk, which gives it a unique sound. 5miinust brings their energetic hip-hop vibes, while Puuluup adds a touch of Estonian tradition with the talharpa, a traditional bowed harp. This mix has made the song stand out, especially since it's the first Estonian-language entry in Eurovision since 2013​. However, the song does have a very niche feel to it, which doesn't really make it very accessible to many of those across Europe. I do have to give Estonia credit for the bold entry but I think it ended up placing around where it should have at 20th, maybe a couple places higher if I’m feeling nice.



Ireland “Doomsday Blue” Bambie Thug


Ireland actually tied for the most amount of Eurovision wins along with Sweden, but despite that they had not qualified for a Eurovision final since 2018. Until they chose Doomsday Blue by Bambie Thug to represent them this year. The song, described as "Ouija-pop," features dark, haunting lyrics and a distinctive stage. Taking elements from alternative rock, pop and jazz, Doomsday Blue is a very unique entry from the former Eurovision Powerhouse and its clear to see why this is the song that has brought them back into the finals of the competition. Bambie Thug's performance was eerie and featured spellbinding qualities, creating a memorable Eurovision moment that people will be talking about for years to come. I personally really enjoyed this song and its performance, it definitely stood out from the crowd. The song finished at a respectable 6th place, but for me this is a top 5 song from Eurovision this year.



Latvia “Hollow” Dons


Latvia has made a significant mark on the Eurovision Song Contest with its early victory and continued diverse contributions but would “Hollow” by Don step up to that previous legacy? "Hollow" is a lyrical ballad with influences of soft rock, starting off with a simple piano arrangement and building up to a more orchestral feel. The chorus is particularly impactful, with Dons singing about the hollowness of superficial advice and the importance of staying true to oneself. His voice has a raw, gritty texture that adds a lot of emotion to the song, which is very reminiscent of Rag’n’Bone Man and Hozier. But much like other songs on this list, it's a decent song, just not a Eurovision song. Which is probably why it’s not going to get a huge ranking from me but also why it didn’t place higher than 16th which is exactly where it should sit, middle of the table.



Greece “Zari” Marina Satti


Greece’s entry this year is definitely one of the more unique ones. The song mixes traditional Greek music with modern and ethnographic elements, creating a really engaging and unpredictable experience​. It starts with these ethnic-sounding drums and Marina's powerful vocals, which immediately draw you in. Then, it unexpectedly shifts into more modern, hip-hop-inspired segments, which keeps the song feeling fresh and dynamic throughout​. Some people have said that the shift is too jarring, but personally I like the shift and it only helps promote more traditional music getting a much deserved spotlight, which is one of the best parts about Eurovision. This me is easily a top 10 song, it managed to finish 11th so it’s that far off my opinion.



United Kingdom “Dizzy” Olly Alexander


This year our entry was from the former lead singer of Years & Years, Olly Alexander, who previously had the number 1 hit King. The UK has had a mixed relationship with Eurovision, we’ve had a tendency to send acts that either don’t push the boundaries or aren’t fit for Eurovision. The one time we did take it seriously and sent Sam Ryder, we missed out on the number 1 finish to Ukraine who had just been invaded by Russia. This years entrant however I don't think it lives up to the highs of Sam Ryder’s entry. Whilst it is better than last years, it’s still not my favourite performance from this years Eurovision. First off, the song feels like it's trying too hard to capture that nostalgic 90s dance vibe but ends up sounding a bit dated. It lacks the big, memorable chorus that could make it a standout track. Olly Alexander is a great performer, no doubt about that. He’s got a lot of stage presence and experience from his Years & Years days. But even his performance was not enough to elevate a song that feels somewhat flat and repetitive. "Dizzy" has some fun elements, it doesn't seem to have the emotional punch or memorable hook that you need to really make a splash at Eurovision. Overall the song finished 18th which is an improvement from last year, and is around where I would have placed it anyway.

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