Trust and transparency will be the CX differentiators of the future - Interview with Chris Angus of 8x8

0:00So welcome to the next edition of the Punk CX podcast. With me today I have Chris Angus. It's a good Scottish name, although I think you're going to tell by his accent that he's, hmm, he might have kind of like Scottish kind of bloodlines kind of there, but it's like he's definitely got an accent south of the border for all those people. All those people that are based in the British health will know what I mean. Anyway, Chris Angus is the VP of CPAS and CX expansion for EMEA and North America at 8x8. Hey Chris, how you doing? Welcome to the podcast. and thank you for having me yes as you can tell uh there is some scottish blood in there way down the line but um i fortunately or unfortunately escaped the accent at this point yeah thanks for having me you're very welcome so chris for the benefit of our uh listeners can i ask you to kind of tell me a little bit about yourself and a bit about the work that you do yeah absolutely you can tell it's a it's a wonderfully wordy uh job title so we don't often use that in this i should know that chris is currently in singapore which he's kind of like i think that's beyond these kind of like his job description kind of you know right now which is that because it's email north america i'm like how does the asia pack get in there well that's it's interesting so we have a really large footprint for the cpass that's our communications api that is part of the business over here in in apac so i'm here this week to work with the team and we kind of do a lot of cross-functional learning so my core roles responsibilities for this role within eight ways i spend a lot of time speaking to customers and partners around bringing the communication APIs into their customer experience journey and about how we make it as one.

1:28So part of my role is to kind of cross-function a lot across products and sales and partners and channels to see how we bring that to life and kind of look beyond that kind of feature stack and more outcome-based ways of thinking. Right. All the business, about 15 years, various roles in sort of sales and operational leadership. And so I've been here a long time. I used to smile loads and have loads of hair when I started. but now traveling between the UK and Singapore um the uh yeah the hair's gone a real bit south and so it's testing but yeah that's kind of a it's passionate of mine as I enjoy doing these these talks and doing having the conversation with customers and partners and figuring out what they're trying to achieve and working out whether or not we have solutions for them to help them get there perfect now because we were talking in the run-up to this kind of podcast and and And I always like to have a bit of a rummage around before we do these podcasts to try and just kind of find some sort of themes.

2:23Rather than it just being, I want to talk about this. It's almost like trying to capture some themes or some topics that I think might be kind of interesting. And one of the things I wanted to sort of explore with you is that we talked a little bit about is about trust. and like so i know that trust and particularly in ai has emerged as a major theme and i did this big end of year predictions piece in december and trust was this big thing that came out of that and in the run-up to the podcast you kind of said something which i thought was interesting i wanted to ask you to explain is that ai you said is now table stakes and customer experience but trust is not so i wanted to ask you can you explain what you mean by that and why ai is now sort of table stakes and also where are organizations falling down when it comes to establishing trust with their customers because it's it feels like it's a perennial thing it's all moved on it's like we like from like 10 years ago big data and analytics kind of exploded and personalization on the back of it and people were like oh data privacy security all these different things and then trust kind of like emerged out of like, oh, we don't really trust things.

3:36And it's just continued. Where are we at? Why is AI in CX now table stakes? And what's happening with the whole trust dynamic? I think the table stakes comes from two angles from the way I see it. I think from an organizational perspective, so competing brands, for example, this table stakes is for them to compete with one another. It's how they can, certainly now, with kind of like Claude Engineering, as an example, and biobengineering, they can deploy things a hell of a lot faster than they could before. So if you're not using some form of AI in that capacity, you're going to be much slower to market than your competitors.

4:14And also you've been able to give organizations much easier access to, you talk about data and data security, but it's much easier to get enriched data to create those personalized journeys, personalized marketing outreach programs, proactive support, trying to, like, we looked at previously, Craig and I, one of the people I work with, we talked about the difference between kind of first call resolution and zero call resolution. So preemptively reaching out to customers before there are kind of service inquiries. That's where AI has become real table stakes because they're differentiators now.

4:47In marketplaces where there's lots of opportunity, lots of competitions, but not a lot of differentiation in terms of how much things cost. The service you provide your customers is how people make decisions on who they work with that's how i feel like it's a table stakes the trust comes down to transparency i don't know if customers are expecting ai as part of their customer journey but what they what they do have is it's a lack of understanding sometimes people get caught in the way feel like it's an endless loop when they're working with let's call it an ai agent or bot or whatever you want to call it for example and it might feel like the ai agent or the bot is deliberately deflecting them from speaking to a human and solving their problems.

5:29And they're half right. They are absolutely trying to deflect them into a self-service program to empower agents to do other things. But it's not designed naturally to kind of give them a poor service. It just comes across that way because maybe it's been deployed in the wrong way. So in those examples, that creates that lack of trust from a customer side. So that's where we see people fall down, that lack of transparency. it's really simple to put things out there and say this interaction has been held by an ai agent if what you want to elevate into it to a human agent you can do so right those things in place before aside from guardrails create those transparencies and those kind of the that clear signposting which then builds trust and i think customers then will give it more more of a shot when they're trying to learn and navigate this new way of speaking to uh organizations i mean yeah we can let's come back to that around the sort of the trust thing because i think they're i think it's an interesting kind of thing because i think it's almost like that you know well actually let's not come back let's do it now and let's get up because the whole trust thing you talk about well the way you're talking about is it requires people to have a go right and then kind of build kind of trust from that.

6:46But that in itself is almost like a barrier if people have existing kind of thoughts about kind of how companies operate or whatever, even if just suspicions or kind of whatever it might be. Because that makes me think about something else you said. We've talked about this idea of responsible AI. And I think that maybe kind of like plays into that is because a lot of people,

7:14they they see all these big things in the news the data breaches and kind of identity thefts and fraud and all these different sort of phishing and blah blah blah all these different things and their concerns are real right and this new technology this new explosion in particularly genitive AI and now agentic kind of AI is exploding onto the scene in the last kind of few years and people are like going, oh, it just feels like you're doing a spinal tap thing. Everything's churned up to 11, right? And I wonder if the responsible AI approach is a way to get people over that kind of like hump to have a go, as it were.

7:58And so because you mentioned it, I wanted to ask you, what is responsible AI? And is there an agreed definition for it? Because it feels like there's a term that's just thrown out. And everybody's like, oh yeah, we'll do that. I wish there was. I wish we could all, all of us have worked in this industry. I wish we'd come up with something which we all uniformly agree on. But until you kind of get, which we don't really want at this point, until you get some kind of overall governance from an external body, I don't think there's an agreed kind of way to describe a responsible AI. I look at it in three different buckets and how to create responsibility as an AI vendor or a user of AI.

8:32So firstly, there's the transparency. So it's about the teams internally and customers externally knowing how decisions are being made using AI within their organization and how it's augmenting that human interaction rather than replacing it. Almost like it's a co-pilot to your services rather than an autopilot. Then there's the accountability. So humans remaining responsible for the outcomes that AI provides. I don't know if you've seen the interview with the chat from Anthropic in relation to the US government and kind of Anthropic's kind of battle at the moment. They talk about like they agree with 98 % of what the US government wanted to use it for and some of their guardrails as a couple of percentage points they couldn't quite get their handle on.

9:21And that was the use of like, it was taking kind of AI into the arms race and using it to completely make autonomous decisions. Now, AI right now - It was not only that. it was also domestic surveillance. Well, yes, that was one of them, but that was the other point, right? So yeah, we've seen the same interview, right? So we're not at that point yet where it has the ability to be completely autonomous in that decision-making protocols. So that human accountability is really, really key. And then the third point I think around responsible AI is around fairness and safety. So it's the models that are completely being monitored to avoid confirmation bias, hallucinations, and the harmful outputs.

10:00Those three things create kind of a responsible kind of guidance and guardrail for organizations. And I think, again, if we can put those things in place and have clear AI policies that customers can access when their information's being used significantly for AI, that should totally break down some of those barriers for those late adopters. Let me go through this stage with any technology, right? You're going to have early adopters who are keen to go, hey, I'm a keen early adopter in most technologies. different demographics and kind of everyone as people that are late to adopt these things those sorts of those sorts of areas where you can be really upfront and transparent about how you're using ai what your what models is being used to kind of feed into just start to break those things down and start to get some of the late adopters on board and giving it a go um and so i wonder if there's like when you think about the kind of responsible ai i mean i wonder if the there's the kind of operationalizing it sort of almost like deciding what you want to do and how you want to go about kind of things and then and then doing that sort of internally but then there's also the communication curve of it um and and how you kind of you know how you do that and i wonder if it is also do you think that's an also an opportunity for brands to actually say this is kind of like it's aligned with our brand this is aligned with our brand values this is how we're going to this is how we're going to go about this that you know hold us to account kind of on this because it feels like there's a lot of talk about it but it's a bit like it's like a skipping stone it's like going bing bing bing bing and we kind of catch it and then it's gone but that sort of doesn't that sort of ignores some of the real concerns that many customers can have and maybe it's that customers are saying well actually we'd want you to double down on this and tell us a bit more about it and be and do you think that's possibly an opportunity i absolutely think it is and i think also depending on the when you come down to the top organization using it i think that that requirement or opportunity it is heightened you think so that's the differences between k our public sector or our local councils and maybe a retail outlet so let's say you had a big retail fashion brand maybe they're using our ai inside a whatsapp channel or inside a an rcs channel for example and you can contain a whole shopping experience through one of those channels include suggestive things to buy based on the patterns of your buying behavior contain inside one of those channels but that's that's using ai and it may be obvious it may not be obvious but those suggestions on hey you've just bought this bike we think you might need these um these pedal clips too how about these ones these are really good work really well oh if you buy a nice what about these bib shorts for this water bottle and you can that's using ai to recognize your buying patterns and the stuff you've bought to suggest new things for you to purchase now you could put a flag in that conversation string to explain how it's being used you can have data within your terms and conditions the notes in the terms and conditions that kind of reference that also and abilities to opt out and make it explain i'm just going to ask have you ever read terms and conditions properly i haven't but but you maybe not know as a consumer but outside of those kind of as a guard random protection that you know of course is there but in the public sector when we look at they are serving a much wider demographic of people right they have to meet customers where they want to be met and have to transact with them in the way in which they're most comfortable so maybe they start they start using ai internally first yeah to post greater and they have to be more fun of how they're using it that creates an opportunity to break barriers down the softly softly approach before it becomes kind of customer facing and decision making and trying to make it agentic yeah that's kind of there's ways to approach it and obviously overcome the other opportunities i mean because i think you talked yeah so you talked about that sort of again the transparency and the kind of accountability in our conversation in the leader today she said that human oversight is essential to a great customer experience.

14:07And this idea, but if teams cannot trace how an AI recommendation was generated or interviewed when needed, trust erodes quickly. And again, we'll get back to that sort of trust thing. I agree that AI decisions and recommendations should be completely auditable by teams. I mean, this idea about kind of like open or, well, transparent and or opaque systems is a real issue. But I want to get your perspective on some of the things you were talking about and whether you think that brands should, and are they going to extend that transparency to customers? I'm almost like to do that audits ability because it feels like there's, I sense intuitively that some brands might go, well, we might kind of do this internally first or somebody queries something, then we know it because we don't want to kind of like, yeah, in development, and we don't want to wash our laundry in public, as it were.

15:02Somebody goes, click, tell me how you made that decision. And it goes, click, tell me more, click, tell me more, tell me more, tell me more. And then they find a thing and go like, what? So how are we going to make that sort of transition? Because that feels like a tricky sort of thing. I mean, it's a bit like some of these ideas. If you believe in the technology and believe in what you're doing, there's always going to be sort of glitches sometimes. sometimes, but sometimes brands default to the idea of zero risk more than anything else. It's a bit like, if you remember back in the day, before the advent of feedback surveys, where the common thought was like, we can't ask customers what they think because they'll start shouting us.

15:43And you look at them, just go, or they'll shout us, and then they won't do business with us anymore, which is obviously kind of ridiculous. ridiculous but i think those those things kind of like you know loom large in our in our mind so i wanted to understand do you have a view on this sort of how that's going to evolve that the internal transparency and all its ability so when you push to it particularly if you're in like financial services or a public sector and all these different sort of things because they have they're held to like higher account as it were but then within that whole thing around um responsible ai do you think we're likely to see an evolution of internal transparency and accountability and audibility view one thing like if you want to understand more about this then ask us about it but we that we can do a root and branch kind of internally and then you'll start to see a creeping of a more externally sort of an external view which then might assuage some of the concerns of some customers.

16:43I mean, do you think that's likely to happen? Yes and no. Okay. I see what you're making. I feel like, I'm going to use maybe a crude analogy, but like customers, in most cases, they want the sausage. They don't necessarily want to know how the sausage is made. Right. So this kind of transparency is key in some areas, but then customers aren't really going to want to, well, my experience and my thought process today is, it's not necessarily looking at the model architecture and kind of the algorithm details of how it came up with these suggestions and this kind of pathway they're not the things i think that the organizations need to start being transparent about but we should be open about when they use when a customer is interacting with that when and how they can escalate and make that easy to a human where possible if they're uncomfortable or uncertain and then how the data is being used and we saw this very very early on and i'm saying that like it was 80 it's probably about 12 or 18 months ago One of the organizations in the States used the data, but they were using open and public AI engine.

17:43And then the customer data was effectively being used to train the public large language model. And that's a real misuse and kind of misunderstanding of how guardrails work. That's not how it should work. You re-invent it, you create guardrails, you protect the data, and it's safe and it's secured. it meets kind of your in-country regulations gdpr or the iso 27 000 and all that kind of fun stuff right but from from that transparency perspective i think they're the three key areas that customers are probably most interested is when am i talking to an ai of some description how can i escalate to a human and is it easy for me to do so do i find it as a barrier to actually get out of this kind of autonomous autonomous loop which i don't want to work with working anymore how is my data being used?

18:29Can I trust that my data is being confined and being used in a way which is responsible, compliant and legal? That's the transparency I feel that our customers are looking for. Not necessarily, what algorithm did you use to come up with that suggestion and what model architecture you're using? What large language model are you using or collection of those to do that? How do you tokenize that decision-making process? I don't think that that nth degree is where they need to be. I hope Waker becomes very challenging for all of us in the industry. Well, you know, that's the thing. I don't want to just sort of explore that to almost like a logical conclusion because you look at it and just go, yeah, that's a difficult thing to do.

19:08We talked earlier on is we start with big data, machine learning, those things, they are almost what AI has become and they've always been there. They've always been there. There's filtration processes and how they use big data to analyze customer segmentation and how they market to you. like netflix for example you and i can look at the exact same set of tv shows on netflix but the the thumbnail you will see may be different to what i will see so netflix is using my dates how i interact with them to make recommendations on what i want to watch and it's also you pick in the thumbnail which is which they believe is most likely to attract me to that show okay so those things there that's that's the example of no one's asking netflix how why did you suggest this program to me.

19:56How did you characterize that film and that film together? Why did you show me the picture of this chap versus the picture of that lady? So we've evolved since those things. So I think that it's natural to assume that customers' inquisitiveness would follow the same trend. Right. No, that's a fair point. And so I think if we circle back onto a couple of things that you said, because it's all about how you can make this happen, as it were, right? And you mentioned a couple of times governance and clear guide rails will shape the next and will shape the next phase of ai and cx though because we all know that kind of it's easy to talk about harder to do with all the kind of things whether it's technologically technology technology readiness kind of data kind of cleanliness organizational capability etc etc um so on the kind of governance and clear guardrails what opportunities and challenges do you think that will pose for brands because sometimes when people talk about governance and guardrails and stuff that feels like we've got to do a big powwow to figure kind of like stuff out and that feels like can take a lot of time involve a lot of people a lot of discussion and also can feel slow in this kind of current kind of market sort of sentiment it's interesting because you're not wrong i think something you said a moment ago which is really key is that that the first phase of kind of this this ai explosion was was rapid experimentation ai systems everywhere and what i saw in the CX space was top-down directives on bringing AI into organizations.

21:28We have to have AI. Okay, cool. What does that mean to you? We're not sure. What do you want it to achieve? We're not sure. What outcome are you trying to replace or speed up? We're not sure. So the key thing, really, is the opportunities it creates and creating this governance is exactly that. It's exactly starting out, okay, so what problem am I trying to overcome or what outcome am I trying to create for my business or my customer? Do I need AI to do that? Or do I have other services and solutions which perhaps are more familiar with that complete the task and overcomplicating the approach?

21:58So those are the kind of things that we think about, our customers think about in that experimentation phase. The second phase of creating governance, the guardrails and operational discipline, it is quite a lot of work because before you can do those things, again, mentioned this a moment ago, it's the data readiness. So we all know if you put rubbish in, you can get rubbish out. One of the biggest challenges we see with organizations, we work with from 8x8's side, when they're trying to bring AI into their organization, and isn't that just internal to try and help with agent scripting or sentiment analysis, those type of things, their data's not in one place, or it's not accessible by the large language model.

22:38So we You can't use it to train the model to then help train the agents. So the governance first needs to be is, okay, so where's your data lake? What are you doing internally? How you can't operationalize it if there's nothing centralized to query. So that's really, really key. And that's a lot of heavy lifting. For some businesses who perhaps are still in their transition to cloud or still trying to make digital transformations, it is a lot of work. Okay. But it's valuable and required. so whether now or in piecemeal or later on you will have to at some point so it's kind of something where i can't be avoided we try and work with trying to with organizations make it sure it's not so overwhelming but you can't escape it you're not going to be ready to introduce kind of ai agents uh without that centralization of where your information sits right and then you then you create the policies so those clear policies let's help in doing so in the first case how do want to use it again comes out to the outcome we're trying to achieve what are we what are we trying to do can my humans override that again is it a tow pilot or is it an autopilot and then you know and it's we use like the uh there's the eu guidelines on how to how to use ai for data and whatnot so that creates a massive opportunity those things are again they may seem like there's a lot of work to do in some cases they will be but they were required to be done but then you can build trusted ecosystems and then we go back to what we said earlier on about being open and transparent and trialing with your customers to do more with less create more seamless customer experience journeys expand your footprint without expanding headcount the two most expensive things we tend to work with is real estate and human headcount if you can start to bring in ai agents to or even internal kind of ai systems to help you ratify data and make business decisions with you without increasing headcount, that's great.

24:31You get better customer insight. We can produce faster services, create that scalable personalization, but it all starts at kind of those governances at the start. And then the risk to organizations that skip those things is that lack of trust. You're unable to be transparent because you're not fully in control. That damages reputation. And then there's regulatory exposure. So without doing those things, you will feel foul to the regulators at some point. So there's a kind of opportunity and the risk behind setting the right governance in place to start with. And so, I mean, so we talked about this sort of stuff.

25:06I mean, and sometimes it's like, it's great to sort of highlight some of the big issues and some of the things that need to get done. But it's also just as useful, if not even more useful, is to try and find some inspiration or some motivation for people to go, ah, I'm not the only one. I'm not the first. and so i wonder if you've got maybe one or two examples of brands that you're working with organizations that you're working with that are getting that whole responsible ai trust building kind of right and and what they've been doing what challenges they face and what they've done to overcome the challenges and to your point what outcomes are they driving so yes absolutely and i think it's interesting it's the outcomes that we look for first again it and i it's almost like this being in a kind of a salesy type role when anti-sales person that capacity always starts with what are you trying to achieve is the problem you're trying to solve really a problem or are you trying to not you know shoehorn something in so one of the we work with a lot of people in our public sector and across the uk so central local government and we're working with one of the councils that were handling very running a very outdated manual switchboard handling around 400 calls a day had a human operator switching these calls out on a regular basis.

26:26Calls are being missed, other departments were having to step in and take over. So we looked at exploring AI, building accuracy and trust, how we can deflect some of these calls and how we can get the calls to the right people in real time. So we put our intended customer system into this organization and we were able to switch 70 % of calls accurately from day one. and then we improve that after iterations up to 90 accuracy so that saves them around 60 000 pounds immediately just from being able to use an ai engine in front of their call flows to direct call traffic to the right places and this this superseded kind of basic ivr dtmf tome this was this was ai driven speech recognition to deflect calls away from a switch board the switchboard operator is still there for calls that that can't be contained there but the level of calls that we're able to to move immediately and then refine and adjust and reiterate the flow it saved them tens of thousands a year and it meant that the there were no longer missing calls so they were able to service their 250 000 residents that they work with near manchester on a much more efficient basis um almost overnight perfect nice i mean it's just like sometimes just simple things i mean it's but i think it's simple things that you know as you say it's like when people talk about transformation and i sometimes think that that the words isn't very helpful no what it does is it builds this picture of like going because the example that you use it's a very very simple solution to like a big problem that affects both customers and also the organization yeah and it's not it is you could argue transformative but it's sort of not transformative in that capital t transformative type of thing i totally agree and i think what you hit me on the head that's our normal normally our advice is if you're new and again if you're going through the digital transformation journey start with the simplest use case like if you kind of want to try and go big bang you're likely going to cause pain first and that impacts customer distrust so start with my simplest way forward what's the what's the simplest thing that's called you the biggest headache that we can get more return on and it's going to but it's going to keep the kind of reputational damage to a minimum let's start there and see how impactful it is and if we can prove it if you can prove to your customers and to your business um uh business owners that or business you know kind of unit people you work with that there's value and you've proven the use case you're going to start to explore how else you can what other problems you can solve and bring it in and again ai is not the answer to everything it's just the way which we It's just another tool we can use to augment human interaction, to do more with less faster.

29:15And that's the way we should use it. Awesome. So one final question. Yeah, final question before we kind of get into some quick fire ones. It's probably fair to say you're deeply immersed in this sort of space. So give me your perspective on what sort of trends or things you're expected to see emerge in the coming sort of 12 months. Just, I mean.

29:39I mean, not to frighten anybody, but just to kind of get your perspective. Because it's like, you know, as I say, it's a heckling old phrase, that change is constant sort of thing. But like, tell me what you see kind of like some of the trends and things that you're expecting to see emerge in the next 12 months. We want to talk about tech years and dog years in the same sort of language. Like it's if they evolve so much faster than real life. My prediction is it's not very iRobot and kind of down that route. I'm not expecting robotic shop assistants in retail stores anytime soon. Although I did see a robotic security guard outside my offices in Singapore earlier on today.

30:13I don't know, the camera on top is scaling around, so I'm not sure what he's looking for, but that's the first time I've seen something of its kind. So we'll be interested to see what roles it was placing. I'm not looking at anything kind of groundbreaking and we're not seeing already, but it's just the adoption, I think. So one of the things I think we've started to see, we'll start to see more of, is the the empathetic nature and workflow automation in AI is going to improve. It's one of the things that holds it back and kind of why you need humans in play still. But I think we're going to start to see more workflows and more complete tasks being handled through AI bots and AI agents more frequently.

30:53And I think the agentic AI and the ability to look at language detection and sentiment analysis is going to start to involve how empathetic the voice bots can behave and change their tone to mirror the tone of the person they're speaking to and spot how to escalate to a human quickly without being asked to we'll see the evolution of that course over the next 12 months um we've started to see this already but real-time agent assistance so live call summarizations and coaching within contact centers so we see that the contact center worker who's super super important the front line of most organizations communications with their customers going to be able to have much regular coaching and tailored coaching rather than just basic scripts so it's going to start to listen to their wants and needs tailor their experiences to improve customer experience in turn um which will obviously improve productivity brand recognition customer sentiment all of things that come into play when working contact centers the convergence of communications and AI is something I'm personally, I'm excited to see more of.

32:00I mentioned earlier on around kind of the end-to-end retail experience inside a single device without being sent links, without being able to not reply to a message. And we think about the evolution of RCS, which is kind of your rich media communications inside your SMS channel. So not having secondary apps like Viber or WhatsApp, and then using kind of the AI agents to control conversations, make suggestions on how to shop or how to interact so i'm i'm expecting be slightly hopeful that we start to see that pick up because that's one of the things i am most passionate about i think it's gonna be really cool yeah i think yeah i think we started to see already we started to see organizations using kind of c-sat scores and nps data as differentiators but trust and transparency would definitely be some of the things they use to promote themselves with yeah so what be on cost or those sort of services.

32:54They'll start to use how they use AI, how they build trust, and that's how they'll sell themselves against organizations. So they're the things, the four things I think we'll start to see evolve across the next 12 to 18 months. Awesome. Thank you. Well, Chris, that's one of my main questions now. Ready for some quick fires? I'll do my best. Right, so I've got three for you. And before we finish up, first one, this is the best advice one. and it involves you completing a sentence. And the sentence is this. If you want to improve your customer experience, Chris says, do this, dot, dot, dot. Over to you.

33:32I would say focus on the outcomes you're trying to achieve, listen to what your customers are saying, and start small. Full stop. Thank you very much. Second one, punk one, obviously. So what company or brand do you think takes a more punk approach to the customer experience and why? oh that's a really good one interested it's been in the news i think uh the brew dog brewery and restaurant are super cool when it comes to their experiences but they've just sold so well it's more than that they've been bought because they were they went into it they went into administration and they got bought but um i in my experiences there so our friends of mine we do uh my friends go for wings and on a regular regular kind of week and yeah i just think they're they're my I suppose they're a really cool, modern, kind of on-the-edge organization to interact with.

34:22So I quite enjoy those experiences. So, yeah. Yeah. No, I love the brand and their proposition and their product development and the bars and all that sort of stuff. I just think that they got to the point where they overextended themselves and I think they suffered the fate. Their rise up through the ranks from kind of anonymity and fame was quite famed, wasn't it? Yeah. And I think they ever recovered from the pandemic. I think they took on quite a lot of debt through the pandemic to keep going. And I think then they started to expand. And I think that just kind of ended up just getting to the point where it just overwhelmed them.

35:01pretty cool brand yeah perfect but one final thing tell me a good news story chris because i know you've just flew to kind of singapore we are recording now at the sort of the you know in march time and the beginning of march and well you were flying through the gulf kind of area and then they almost kind of got sidetracked because there was lots of different developments there we won't go into the kind of the commentary around that but it's fair to say world is a bit weird right now and there's a lot of doom scrolling going on in the kind of and everybody's kind of morning about like oh what's next what's next things so yeah i've been trying to for the longest time you're trying to ask people to share with me a good news story to well it's a kind of selfish thing to brighten up my day um so tell me something that you've seen or you've heard or you you've experienced in the last week that you just it just made you smile you know what i'm going to do something i'll need something which is internal to the organization that's kind of right now in the room next door and i can i can hear them it's a it's international women's day at the moment okay and we're very proud eight by eight to have a real diverse member and selection of employees across across the world today we've had some of our senior leadership team who are female talking about their experiences and their rise through kind of technology which has traditionally been a male-dominated industry and spreading it spreading their knowledge and their feedback and and kind of in the art of the possible for all of the kind of the uh the rest of the organization and how and how those kind of you know those glass ceilings that do exist in today's world how they can be broken and how they've been shattered and i can hear that the office next door to me shouting and hollering and cheering which is which is lovely to see so um yeah and i said i am in singapore right now the team here are wonderfully welcoming it's been my third or fourth time being here they don't stop but giving me sweet take home to the kids um which lovely so yeah that's kind of my my good news story on my half vote by eight and uh and all of the all of the ladies watching or listening to the podcast that i've celebrated international women's day today well i mean so today at the time of recording today is the 4th of march but this won't go out until i think sometime in early may so this would be a retrospective shout out for international women's day because actually i do believe and thank you for sharing the story every day should be a woman's day and because it's like you know why not it's great they're awesome as a as a husband as a son and a father of a daughter i couldn't agree more awesome chris thank you for that and thank you for sharing your time and your insight and your experience with me today that's been awesome you're welcome thanks for having me it's been a pleasure

37:43wow what a great interview i hope you enjoyed it i know i did find out more about me and the work that i do at adrianswinsco.com do leave a review on your favorite podcast platform and if you have any comments feedback or questions about the podcast then feel free to send me a message to podcast at adrianswinsco.com and do tune in again thanks very much

38:09you