Understanding AI Implementation: Challenges, Benefits, and Governance

Mark Smith: 0:01
Welcome to the Ecosystem Show. We're thrilled to have you with us here. We challenge traditional mindsets and explore innovative approaches to maximizing the value of your software estate. We don't expect you to agree with everything. Challenge us, share your thoughts and let's grow together. Now let's dive in. It's showtime. Hey, welcome everybody.

Mark Smith: 0:23
Hello, hello, all five of us are back today, except for the. The thing is we. We decided we, we don't need will on the show, so we kicked him out. We said you will, we go have some sleep. And then andrew was complaining. So he said andrew, you're out, like we don't need you, and um and chris. Um, chris is out getting another tattoo, so that just left Anna and I. So you're lucky to have exclusively Anna in the building to hear all about what's top of mind for her at the moment in tech and technology. We're coming off an interesting week. Fy25 has kicked off, microsoft have just done MCAPs, and so they are setting the priorities for FY25. And, lo and behold, ai is kind of featured quite prominently in the FY25 game plan. So we might touch on some of those things as we go through today. But, anna, what's been going on with you, hey?

Ana Welch : 1:26
so everything's been really busy lately, maybe also because I work with central government institutions and, as you know, the UK had an election last week. Had an election last week.

Mark Smith: 1:42
Yeah.

Ana Welch : 1:43
So there was a huge shift that, frankly, I think everybody was able to foresee, but it does mean that a lot of these organizations had things in progress or things that were top of mind for them and they had to pause everything and now they're ramping up again rather rapidly. So it's been a little bit chaotic, not to mention exactly what you said. You know microsoft's new financial year, new priorities, and everybody, even the strictest, uh central government organization, is talking about AI and they really, really want to at least try it out, get into it, just improve that operational efficiency right now. They were under pressure before, but they continue to be under pressure right now and, yeah, they need to do something about it.

Mark Smith: 2:44
That's interesting. It's interesting that I, you know, every company, you know, as you say, it should be on their lips talking about what is the game plan in the era of ai.

Mark Smith: 2:55
I just want to share something here on screen this is an email, a newsletter, that is put out by benedict evans, and I don't know if you've heard of benedict evans, but this guy's been in. I don't know if you've heard of Benedict Evans, but this guy's been in tech a long time. If you take a look at the About section on his website you can see 20 years in the space. He has 175,000 subscribers on his newsletter and he talks to companies, lectures at companies like Alphabet, amazon, hitachi, all the big players kind of thing out in the market, and so and he, he does this deep analysis of of what's going on in the world and he talks about in this most recent newsletter, the AI summer and the whole.

Mark Smith: 3:37
You know everybody there's been hundreds of millions of people have tried jet beat chat, gpt, but most of them haven't been back. And I feel it Sometimes we live in people have tried chat beat chat, gpt, but most of them haven't been back. And I feel that sometimes we live in this bubble of everything's around ai at the moment and it's definitely, you know, taking the oxygen in what we're doing. But I'm wondering, is that the case out in? You know, general pop and and is the general population really? Is ai on their radar as much as it on ours? Or is it because we're in this space that we, we like, feel like everybody's on board? Like you know, over the last two months my subscriptions to AI models on AI tools have over doubled. Like I'm, I'm paying for. You know I've had a GPT from day one paid. I have a perplexity. I paying for. You know I've had GPT from day one paid. I have perplexity. I pay for. I have stable diffusion, I pay for on the image side and I have mid-journey, mid-journey, mid-journey, mid-journey. Oh for ARCH. Right, I feel more engaged than ever in these technologies.

Mark Smith: 4:46
But going back to what Benedict is saying, there, a lot of companies he's referenced there have done a POC and it died to death, but it hasn't progressed beyond that. And then you get these customers that go, we need AI and you're like, oh, what for? I don't know that. Go, we need ai. And you're like, oh, what for? I don't know, we just need ai, you know, and it's kind of like. I think it's a massive opportunity for consultants, by the way, unbelievable. But a consultant has to get really crisp on one. How do they uncover ai problems in an organization? And what I mean by that is is areas where ai can solve something.

Mark Smith: 5:28
I heard this case the other day, which I and I love kind of hearing these ones that are unique from the, the field of sainsbury's right, which is a.

Mark Smith: 5:38
For those of you not in the uk, sainsbury's is like a, a supermarket where you can go get food and stuff, and they are using ai to decide what meat they buy based on the weather conditions. For a sainsbury's store, because you think about it, if it's a heat, if it's hot and you know the uk's had a bit of a heat wave at the moment they know that sausages, burger patties and things like that are going to be in higher demand and and so when you think about this and stocking of a shelf in a supermarket, you can see that ai could be absolutely helpful in that scenario by analyzing where the patterns people are going to want to barbecue people are going to. You know they're not going to want, let's say, a stew. You know meat for a stew or mince or something like that, because they're going to be out and about. So let's stock the shelves with that because that product's going to be moved over. What might be. This is a time of year. We would traditionally put that on a full raw chicken.

Ana Welch : 6:39
Yeah, right, yeah, exactly. That's a really good example and I find it really interesting because we didn't even talk about this before. But actually, sainsbury's is a model of data management. In 2016, 2017, if I remember correctly they were starting to have big problems with matching prices and, mainly, stocking up the shelves and making sure that their shelves have the best, the absolute best targeted audience, but at the same time, trying to be sensible, so you don't understanding the fact that you can't just put named brands, because that's where the profit is, and the fact that you need to, like, cater for everybody.

Ana Welch : 7:41
So they did a huge machine learning exercise to understand how to stock their shelves in a way that makes sense, in a way that optimizes their storage units. They did a huge supply chain exercise where they would be able to offer prognosis over what they will have to buy, even in their storage areas. So what this tells us is that it still goes back down to data, because Sainsbury's is able to stock up their stores based on the weather projections in the UK. It does not mean that other supermarkets may be able to do it, because Sainsbury's already has the infrastructure and the platform to actually implementing it, and I think this is what potentially customers do not understand. Without that baseline, we can build a POC, that's not a problem.

Mark Smith: 8:49
And then what happens?

Ana Welch : 8:52
And with regards to how many people use AI tools, I remember at the beginning of last year where my best friend was very worried about artificial intelligence and how that's going to steal, you know, our jobs, and I was like, well, I'm not sure it's quite that way. And, yes, maybe some professions will be changing or disappearing completely, but others, you know, potentially will be enriched, whatever. I didn't have enough information to make a very good point either, especially because fear is a very, very powerful thing, right.

Mark Smith: 9:37
Yeah, yeah.

Ana Welch : 9:38
Well, fast forward to this year she has been. She. She's very smart individual, so she has been promoted twice and now she's in a management position and her number one job is to liaise with people. And what does that mean? Writing emails. And I'm like why don't you use AI to draft those damn emails? Because otherwise you're going to be working 14 hour days. You're never going to finish if you don't get some help. This is where I feel people in organizations can use AI, if they realize what's the real use case, not what they want to play with what is the real use case in their day-to-day lives.

Mark Smith: 10:57
It's an interesting one, because AI you know a lot of what has been talked about is not a single thing, and one of the things that Benedict says in his article is it a product? What is it actually? And and because I've been using this metaphor that I feel like it is like electricity, and so therefore, it's an enabler of everything. It's an enabler of my toast that I eat for breakfast. It's an enabler of the you know, the fried bacon that I have. It's an enabler of the heat that I have in my house. It's it's an enabler of the heat that I have in my house. It's an enabler of so many things lighting, et cetera and so one, how do you ROI that?

Mark Smith: 11:37
And I know Chris Huntingford, if he was with us, would have a lot to say on ROI and AI, and so I feel it is really a different paradigm, it's a different mindset and, like we've talked about the power platform, one of the things that have come up recently again for me is the, the difference between basic licensing and premium licensing, and this concept of of uh software economics, which is when you build a solution, and I was just yesterday talking to a company that has, I think it was a either 11 or 12,000 employees, 6,000 offices around the world and they are using the Power Platform on premium.

Mark Smith: 12:23
You know for that organization and you know this is a corporate end customer using this technology, um, uh, out of Portugal. And what was incredible is that they said that the thing is, is that when we we did the ROI on the first project, right, it was a no brainer. But they said now every app we build from this point on, we're not having to think of who do we need to license? They're licensed already, right, the. Do we need to license? They're licensed already. Right, the right, the. Everybody's licensed.

Ana Welch : 12:56
So now they're like well, the more solutions that we build that we architect now on this platform, we have diminishing costs as such, because it gets extrapolated right out across across that you build the economy at scale, and it's not just the amount of applications that you have to build, or flows or connectors or pages or whatever you're building, but it's also the supporting framework of it all. Right, you set up Android once you will always have to log into your applications. Right Done, you have put in that information. Once you will always be able to intelligently search for it. No matter what application that data sits in, no matter who touches it, you will be able to extract it back out. Our friend Chris said in his presentation in the latest presentation that I've seen of him, he was like you know how you like, type in data or import data into a system. The really cool thing would be to be able to find it again. How true is that?

Ana Welch : 14:15
Yeah, yeah yeah, like that's so funny, but so true, right, and all of these things are things that you have probably built with your project managers and, I don't know, your BAs, your UX team, who know what to do always. It's. The scale is huge. It's humongous.

Mark Smith: 15:00
What are you doing in the space of customers that have perhaps really leaned into the concept of personal productivity with a power platform and realizing that there's limits to how far you can go there and that a non-trained software developer builds software quite differently than a trained software developer? Right, and you know, standard rigor that we have around things like application lifecycle management, around testing in all its different formats, around documentation, supportability, all those type of robust things anything down to security-based testing of an application is what you have in a traditional software world. And then you've got the personal productivity and, dare I say it, citizen dev comes to mind. There's limits to how far one can go with that. What are you seeing around that?

Ana Welch : 16:08
and moving into the area of more, um, you know, enterprise, like going up that ladder to into the enterprise more you know, in the last few months I've seen it a lot from from organizations who are starting, who have started rather straight on a mature platform, either on Dynamics or Power Platform, but with data first, to customers who have not, and they've leveraged SharePoint lists and Excel spreadsheets in order to create their productivity apps. They're all trying to navigate this world and, I'm not going to lie, it is not easy. It's like actually very complicated. From the organization who swallowed the citizen developer pill and they thought that actually we're just going to do all of this, all of this, ourselves. We only need a partner to support the stuff that we're doing.

Ana Welch : 17:14
I know that you've said in previous iterations that the partner is dead.

Ana Welch : 17:18
I would say that not yet, because, listen, these organizations they're not prepared for the level of assurance and diligence that the projects need.

Ana Welch : 17:31
They're not ready to write, but they need to put in a lot of time to learn basic things like how to write a user story or how to ensure that it will travel correctly through all of your environments, how to make sure that you're not, you know, sending random emails to a production database. So that's like a huge, teething pain where I believe partners need to be consultants. You know Power Platform icon and started building applications and they're all hanging on SharePoint lists and Excel spreadsheets and some of them are like, oh, actually wait, we have a few licenses for you know premium Power Platform, but what do we do next? Who do we give them to? Do we buy more? They are truly recognizing the fact that they need the functionality, that they need the power, the discipline, the government, the governance, the security underneath it all, but it's very, very hard to get started. So that was a really, really, really long way of saying it's complicated to get started in these situations. Where do you think one should get started?

Mark Smith: 19:13
actually, from your experience, yeah, well, when I ever think about where do you get started and it's not explicitly about what you're talking about I always think at the very beginning it's a very good place to start. When you sing, you begin with ABC, sorry, wrong show, this is a. It's not that story. No, I always think about data. Right, as in. I was brought up in that you model data up to the app layer, not the other way around. And I feel what my observation and I've worked with a few architects recently I am concerned about, about how even architects looking at building a solution from a data model perspective and I'm talking about going down to a field level, or is it a column level now, sorry, I still use the old nomenclature, um, because I was brought up with the old nomenclature. So the text field I hate it with a vengeance, right, yeah, and and the reason sorry, I don't hate it, like it's obviously has its place, but I hate it when it's used as a catch-all, right, and I see this, like you know, back back in in the early days of Dynamics CRM. There was a field in there, I just from memory and it said something like, let's say it was on a contact record and you said this contact was a manager and there was a field that said what's their executive assistant or no, it was their spouse, who was their spouse? And it was a text field. And I'm like, if it's another person, why isn't that a look up to a contact entity? Because now you leave intelligence in the system. Yeah, right, because you know a contact is more than a name. Right, it has other things, like an email address, a phone number. Why do I just want to know their name? Was it just so I drop it into a conversation? Anyhow? That aside, I see it on things like informed data collection online or anywhere where they go. The country field is a text field and you're like, imagine somebody reporting on that field now and all the spelling mistakes, um, that will come just with people typing the country name incorrectly, as opposed to that being you know a pre-populated list of what it could be, and then, of course, with longer lists, how do you make the interface so that you don't feel like you're scrolling through a massive drop-down list? But I'm like I feel that there needs to be more rigor around choosing the right type of field. Is it field or column? What am I meant to be saying I think it's column. Yeah, what type of column? What? What is the column type not name, but type that I should be selecting to make my system stay intelligent beyond just me building the application. Now, what is it going to mean from a reporting perspective? Because is it going to mean from a reporting perspective? Because it's going to wrap metadata around it based on, you know, the field, type, um, the, the column type you specify. Yeah, I just notice that there's.

Mark Smith: 22:17
There's often not that level of rigor, yes to, and I always say the worst critic you can have is your customer, because your customer doesn't know what good looks like. Right, you can show them something and they might think, wow, that looks good. I always say the best critic is my competitor, yeah, somebody with the same skills as me in a different organization, because they're going to apply the rigor at the same knowledge level as I have to go. Is that best practice? Is that the best way of doing this? Is that going to make the system more intelligent or less intelligent, etc. Over time?

Mark Smith: 22:56
And so I feel that there needs to be a robustness around that area. So when you say where do I start, I'm always thinking about you know. How's this data going to be used outside of just it's? Now, you know application, how is it? And that's why I love andrew's comment that the the power platform is not an app story, it's a data story, and I think, until people really start to realize that, and then and then my question is how do you learn to get good about data, how do you learn as an? What I mean is much more beyond just the power platform, but you know how to catalog it, reference it, how to make it available, how to sanitize it, how to, uh, archive it right. A lot of organizations have no archiving strategy, right?

Ana Welch : 23:40
especially if you're young yeah, like if you started 15 years ago. You kind of know about data because you have to yes, there's no way around it. But if you didn't, if you're just trying to do something right now, it's really really hard to know about data and, even more tragic, it's really hard to show others the value of data. So I agree with you, we do start with data, we start with the data model, we start with you know, controls around it, guardrails. We start with the idea that we want to know more than that person's name, that we actually want to know her date of birth to send her a birthday card? I don't know.

Ana Welch : 24:28
Her hair color, I don't care, but we need to know more about that. Yeah, so that's the idea. However, if I am the executive sponsoring this, in the age of AI, when I can see everything right now and instant gratification is even at higher levels, we need to come up with a way to make data sexy, to make it attractive, to make it the cherry on top, something that people want to see.

Mark Smith: 25:02
Yeah, I think that's easier than what people realize to make it sexy. And the thing is that, you know, I remember on one project where the customer said for every item of data my salespeople put into the system, I want five benefits back. Right, and it really made you think, right.

Mark Smith: 25:24
Yeah, right, benefits back, right so and it really made you think right, yeah, right. What you realize pretty quickly is that it's actually achievable, especially when you've got the ability to use a connector and bring in other data so you can enrich that data set by getting a small amount. So you know, for example, in the uk if you put somebody's postcode in, it narrows down to pretty much the house on the street even without knowing the street number. There's a high degree of accuracy which you can look up and validate. Things like phone number format Don't ask me to put in a certain format. I'll put it in whatever format that I think of, programmatically updated to the format that you want. Right, this stuff's all possible nowadays, but it's often that kind of polish and stuff that we leave off in the speed, I suppose, of getting things done. You know, don't make me enter the same piece of data twice. If it's this in the organization, bring it in from where it exists. Let's not create multiple copies um of or make people fill in data multiple times if it's available.

Mark Smith: 26:26
Right, show it to me, it's one of my biggest pet peeves is that you know this information about me. You know whoever I'm buying from. Why are you asking me again? You know this? Yes, exactly, and that's where the rigor needs to come in.

Ana Welch : 26:38
You know I just bought paint and that it was white. Stop trying to sell me white paint.

Mark Smith: 26:44
Yeah, yeah, yeah, so true. Another thing I just want to share and I'll just bring up my screen here again is this comment, sorry, this slide that I saw this week from Satya, and I'm loving this right because back on our AI again. Ai is the greatest commercial opportunity in today's economy. This next generation of AI will reshape every software category in every business, including our own. From Satya, right, and what I'm seeing is that, and you can see here, bloomberg Intelligence 2023 is where the data on the right is referenced, and then the Microsoft Work Trend Index, 2024. But look at these numbers 280 billion an additional software revenue opportunity based on rising demand of ai products. 71 say their companies use ai now. And I bring this up for a couple of reasons.

Mark Smith: 27:36
One, the opportunity is massive right yeah you know, for every dollar invested, three to eight times the return and that that's really that 92. There there is the partner opportunity on this, and so there's a real need for partners to upskill their staff right, because you can't? And I feel there's two kind of skilling types that are needed. What you see on Microsoft Learn is how to use our technology skilling, and I see that the world and the community that we're in seems to have indexed heavily on that, but there's not a lot on like first principle, thinking around data outside of a piece of software as an example, right or or or any of these type of things. Like you know, you can technically get into what purview does, but what's the application? What's the broader implication of doing it? Why is it important? And it's answering all these business value questions. I think there's a lot more focus. The other thing is is that this number here of 71% say their companies use AI. I know, I feel I expect that's kind of bullshit, and why I say that is that the telemetry metrics that people use sometimes inflate these numbers or deceive these numbers, and it's happened for years. So, for example, if you took YouTube, tiktokiktok and facebook and you had a video on each. They will all count the number of views of that differently. So youtube, for example, say if it's watched for 30 seconds, that's a view. Tiktok will say well, if it loads on screen, that's a view, doesn't matter if it was played. If it loaded on screen, it's a view. Facebook might be five seconds. They all say this is my view, time right, and it's just sell a story.

Mark Smith: 29:28
And and when we look at co-pilot, co-pilot, for example, you get the scenario that how do you tell whether you're getting adoption inside an organization? Is adoption measuring the right thing? So I I saw this example just yesterday. Where they're going, oh, look at this, we're measuring the word is the. So I saw this example just yesterday. Where they're going, oh, look at this, we're measuring Word is the most common copilot use case and I'm like hang on a second, how is it telling that somebody is using it in Word?

Mark Smith: 29:57
Oh, a plugin fires when Word opens. It calls the copilot. So actually nobody used AI, just the software called and said you know, is the co-pilot connector alive? And we're counting that as use. Come on, nobody even used it, it just happened to be a trigger in the software and that's why I'm like these metrics sometimes, and it's. You know AI is a fuzzy term because AI has been around since the 60s, the 1960s, right, and it's like now, anything that's kind of an algorithm or a if, then statement, that's ai. You know that people are categorizing that as broadly ai as well, because the computer made a decision but it does make sense, like it does remember how we have.

Ana Welch : 30:42
Um, what's the name of the app Office Vibe, I want to say, I don't know the one that tells you oh, you have collaborated with your peers for three hours a day, or you've had focused work for such and such. Well, that can definitely measure whether you're so. Are we measuring if someone has Teams Premium or Copilot for Microsoft 365, that they've used Copilot just because they've been in Teams or they did a Bing search or whatever they did? Because that wouldn't be accurate.

Mark Smith: 31:20
Yeah accurate?

Ana Welch : 31:20
yeah, if you look at it from one perspective. But if you look at it from your premises perspective, that says ai will be like electricity we will not feel it, we will not see it, it will just toast our toast brilliant then it's true yeah, valid, valid, you have a good point.

Mark Smith: 31:43
and, and, and there is that whole thing around digital exhaust, right? All the stuff that goes on that you don't think about, which you know the graph is great at understanding and picking up and making sense of the information reported on might be more helpful in other contexts, even though that individual might not, you know, be doing it. But then, you know, will AI become so immersive that we're not on purpose using it? It's just like I don't think of, when I turn on a light switch that I am going to use electricity.

Ana Welch : 32:16
No, I just lit the room.

Ana Welch : 32:18
Yeah so I agree. And then shooting out a figure like 71% can make people believe. Wait a second, if this is 71% already, you know of organizations using it already why should I even bother to move my data into and model it into like a sensible program of work? Why should I do that? That looks like several millions of investment and it doesn't make sense because my stakeholders will not see it straight away. So maybe I should just ignore it and use AI the way I want to, because everybody is warning me that bad things are going to happen, but I've not seen them yet.

Mark Smith: 33:04
Yeah.

Ana Welch : 33:06
And I am worried about that. You know, in my day-to-day work I feel like instead of ramping up and investing and grown-up, problem solving organizations are taking corners already.

Mark Smith: 33:24

Yeah, and I suppose in this probably we're almost, we're out of time, but yes, another thing that I find concerning is ai washing and yeah, what is?

Mark Smith: 33:36
that tell me. So ai washing is this. You know, when I was in london, um, there was a, there was a pride event on and something that came out in the media was was, um, gay washing, uh, your business, right. And so businesses come out with their stance on, you know, inclusion and things like that and we're kind of riding the bandwagon. We've seen it in the. So it's kind of like we're seen to be doing the right thing, whether they're doing it or not. And we see the same thing in sustainability, right, in that whole scenario where we're ticking the boxes for the marketing, um, and then you see what's going on beside, behind the scenes. I've seen it in the whole area of women in the workplace. You know, oh, you know, we so appreciate equal opportunity and everything, and then women go on maternity leave and their job's gone when they come back, right, it's like the chairs got shifted and it's kind of like to the public, it's all, yes, we're this.

Mark Smith: 34:39
Behind the scenes, it's like still cutthroat as ever. There's no adhesion to it. So when I look at it in the terms of AI, I see these companies now and all their marketing is AI, this, co-pilot, that AI, this. And I look at the company and go okay, how long have you been in business? 15 years, oh so, and email's been one of the ones for me, right, like how do I get a real optimized email experience using AI? And I look at these companies that have got these AI tools and you're like no, you've been doing email classification for 15 years and all of a sudden, because AI has become the trendy word, you're now AI infused and it's kind of like it's like you know the whole. Responding to an RFX, it's like yes, we've got that feature. And it's like the customer's expecting a feature this big and you're meaning it's this big.

Mark Smith: 35:32
But it ticks the box of saying, yes, it has AI in it, and that's why I think there's so much AI washing going on in the market, where people are calling their products, they're doing startups that are, they're riding the ai bandwagon, but there's no, you know, there's no substance to what's behind it. They're just using it as a marketing phrase rather than it's intelligent yeah, I see that.

Ana Welch : 35:59
I see that a lot as well. I'm not sure I see it a lot in my customers, in our customers. I find my customers to be incredibly honest and very much set on good intentions Like I've never seen organizations really, really, really want to work for the greater good. However, I'm seeing a lot of partners go on and on and on with AI and what they do.

Mark Smith: 36:37
Yeah.

Ana Welch : 36:37
You know, with these tools for their customers, and I am wondering where are these customers who are doing extra? You know extra work in AI and it's also intelligent. Why aren't they in the news? And in fact, I did try to study this. I've listened to podcasts and I've read magazines and publications and some more obscure than others and the reality is nobody's offering any sort of consistent result that can hold water. You know that you can say yeah, yeah, okay, so I can see that organizations really are using AI, rather than 71% of organizations may have co-pilot in the corner of their being.

Mark Smith: 37:30
Yeah, yeah, yeah. Well, that's all we've got time for today. Has anyone picked up the paid or the unpaid placement through the show? Can I have a look at your hands, anna? Are you also wearing a ring?

Ana Welch : 37:44
that looks very similar to my ring.

Mark Smith: 37:46
Oh my gosh, look at that.

Ana Welch : 37:50
What's what?

Mark Smith: 37:50
is what is this ring that we're wearing? Anna, it is, you sold me on it.

Ana Welch : 37:55
Indeed, and we are like you know. My relationship with the aura ring um is a longer living than my relationship with my husband. Wow, I've had this ring since the very first generation of smart rings like that. It's got like little sensors that can tell me all sorts of things about myself. From how will I sleep about myself? From how will I sleep on whether I should do like some housework or go for a walk or do whatever, and it's like super inclusive. It doesn't want me to go on a spin class or something, but it does want me to move around. It tells me if I'm stressed. I've got like meditation techniques on my phone if I want to use them, but if I don't want to use them, it's smart enough that it doesn't push them on me. It's sort of like a smartwatch, but 10 times better, in my opinion. It's awesome, it's fabulous.

Mark Smith: 38:58
I agree, I've had it now since Dynamics Mines or thereabouts, when Anna introduced it to me, and it's permanently on me and that's what I like about it over, you know, because I have an Apple Watch as well, but it needs to be charged so often and so it's not on me. But the beauty of the ring is that the only time it goes on the charger is when I'm in the shower, and that's enough, and it's like got about seven days charged. Now, this is not a paid placement, this is just us geeking out over our wearable technology. Um, and with that, anna, it's been a pleasure to just have us talking on the show.

Ana Welch : 39:35
We don't need those other guys um.

Mark Smith: 39:35
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