00:00:00:00 - 00:00:35:22
Tom
Welcome back to the Teams Insider Podcast. This week we have Jamie Wheeldon from Symity. Jamie's a long time friend. We've worked together, both at customers and partners, and he gives us a really deep insight and perspective on the whole AI journey, not just the Microsoft journey, but kind of what's happened in in the AI space historically, where that brings us to today, how will that history kind of impacts where we are today and what Microsoft are doing with Copilot and the various Copilots?

00:00:36:00 - 00:01:01:12
Tom
Great insights from Jamie. Very much, appreciate his time and hope you enjoy the show. Hi, everybody. Welcome back to the pod. Excited for this one. We are on the the hot topic of the the year at the moment I think, which is, generative AI. Jamie, I've worked with, many years in previous lives, and we had a really good conversation offline about kind of setting context for what's going on in AI, what's going on with Microsoft.

00:01:01:14 - 00:01:07:15
Tom
so I thought we'd have the same conversation on the pod. So, Jamie, thanks for coming on. Just give us a little bit of, intro background for you.

00:01:07:17 - 00:01:33:01
Jamie
Yeah. So, hi everyone, Yeah, I'm. I'm I've been working in the Microsoft partner space for for a long time. I'm currently working with, company called Symity. I'm that. I'm the chief architect. and we do a lot of work across the entire, modern workplace stack within, within the Microsoft M365 suite. we're also a Microsoft Copilot jumpstart partner.

00:01:33:03 - 00:01:44:11
Jamie
and we're doing a lot of work with customers as part of that program to, to help them navigate the new world that is generative AI and all of the challenges that that, that that brings.

00:01:44:13 - 00:02:00:19
Tom
Yeah. Was we can't can't do customer names. You're working with some pretty big customers around this. It was interesting to get your feedback on what you're seeing and what they're seeing. And, I'm relying on you here to kind of, de-marketing this a little bit and, give us some insight as to what's going on.

00:02:00:21 - 00:02:41:06
Jamie
Yeah, absolutely. So we we definitely see customers in, in lots of different spaces at the moment. You know, there's there's obviously huge excitement and huge buzz around AI and Gen AI, in particular, which is generating real driving interest. there's lots of challenges though, in this space. You know, there's definitely organizations that are going all in on this and that they're investing money to do it properly, run adoption campaigns, work out the best way of driving, driving this technology into their business and getting value from it, because it's not it's not a cheap set of tools and capabilities.

00:02:41:08 - 00:02:59:22
Jamie
and that's great. But it's it's an entirely new sector. So there's lots of thought leadership that those companies are looking to take advantage of in terms of how they do that correctly. and then the other side of the scale, we've got organizations that are just going out and buying half a dozen licenses and having a play.

00:03:00:00 - 00:03:14:02
Jamie
yeah. They don't know what they don't know, though, because it is all new and therefore they're struggling to, to to really leverage the potential of the technology because they're going at it with a, a fairly simplistic mindset. yeah.

00:03:14:02 - 00:03:34:09
Tom
It can honestly be quite underwhelming if you don't deploy this properly in terms of, you see, the marketing, it's not just Microsoft, like the whole industry's super hyped on the potential of AI. And then unfortunately, you see some customers buy a few seats and throw out there, they do meeting recap. in the case of M365, you know, and they don't do much else.

00:03:34:12 - 00:03:44:21
Tom
I've seen other line of business, GitHub in particular, be super successful because the people using it kind of get the value more inherently, I think.

00:03:44:23 - 00:04:03:12
Jamie
Yeah, absolutely. So I've just discovered, power Copilot within Power BI recently, which is like hugely powerful for kind of doing that advanced data analytics thing and writing all of the really complicated queries that I just don't have the brain for. It's like huge.

00:04:03:14 - 00:04:10:02
Tom
Don't tell Kevin on our team. We haven't got it yet. So, he's curious about.

00:04:10:04 - 00:04:24:16
Jamie
So yeah, you're right. It's it's it's it's a challenging, industry. It's going to be exciting watching how people get the most out of these tools moving forward. And which companies do really well at that and which companies start falling behind.

00:04:24:18 - 00:04:37:13
Tom
Yeah, definitely. So it's kind of less scene set because we're all talking about AI and, I wouldn't say I could explain it in the best way from scratch. You've got a really good way of explaining kind of the history here, so maybe we could start there.

00:04:37:15 - 00:05:11:09
Jamie
Yeah, absolutely. so, I've done a little bit of talking around this, with customers and internally across our business. So I kind of feel it's worth stepping back a little bit and looking at, what we mean by AI. so for those that are watching on, on, on YouTube, I've got some slides here, but, but I've pulled a definition of, of, of AI, which is technology that aims to create machines and software capable of mimicking the cognitive functions of human intelligence.

00:05:11:10 - 00:05:37:22
Jamie
I thought that was a really good way of defining what we mean here. but it's also worth recognizing that, you know, this isn't new technology. A lot of this stuff has been around a long time. So like the first AI systems were created in the 1970s, and they were called expert systems, and they were basically rule based systems that were that were mimicking human expertise in, in specific domains.

00:05:38:00 - 00:06:11:12
Jamie
so if we think about it as new, because it's all very current and it's there's all the current hype, but actually we've been working with AI for a long time. We've also got things like fuzzy logic, which are about dealing with uncertainty and imprecision and making predictions in, in, in uncertain systems, robotics, AI, this is something that's been in manufacturing for a really long while, helping robots deal with the fact that the physical world is not always predictable.

00:06:11:14 - 00:06:21:03
Jamie
so so, you know, that component is not always going to be precisely in this place. So helping those robots deal with an unpredictable physical environment.

00:06:21:05 - 00:06:26:18
Tom
So is that way the kind of vision stuff comes in the computer vision. And so those pieces yeah.

00:06:26:19 - 00:06:55:02
Jamie
Yeah, a little bit and obviously that's not just cameras and things. It's all the kind of real world sensors as well. So it could be right sensors to help them interpret what's going on in the non-digital space. yeah. And neural networks have been around for a long time in terms of trying to mimic our learning capability, and apply that to, to, to various different data sets.

00:06:55:04 - 00:07:21:19
Jamie
machine learning, again, really popular a while ago, you know, we saw how this was, was used to really great, a great use case. I think, in New York, where they did an analysis of crime rates and weather patterns and understanding, you know, where to place policemen based on certain patterns of behavior and weather and, and traffic information.

00:07:21:21 - 00:07:47:14
Jamie
they were able to reduce crime rate. And so this is all about learning from large sets of data and drawing a pattern recognition where you wouldn't expect there to be a natural pattern. and then moving on to things like natural language processing. So this is really, you know, where we see the development of things like Siri and Alexa and all of those, those capabilities that have pervaded our home and personal life.

00:07:47:16 - 00:08:13:05
Jamie
and that's really the precursor to, to to the technology that we're talking about now, which is generative AI, which is using that natural process in natural language processing to, to, to take instruction, but then use that instruction to generate content, you know, whether that content, images or text or, or, or, or query results from, from, from data that you've got.

00:08:13:06 - 00:08:21:05
Jamie
and obviously this is creating the buzz, but it's, it's worth understanding that this is built on a whole bunch of different technology that has come, come before and been around for decades.

00:08:21:05 - 00:08:45:10
Tom
Yeah, it's it's really good to get the, the, the context and the history. Like you say, it feels like in some ways it's come out of nowhere. But actually, even, you know, what Google were doing with DeepMind that OpenAI have been in for years and years is it's like when OpenAI released ChatGPT to the public, it kind of caught everybody's attention is, oh my God, this is a new a new, powerful capability.

00:08:45:12 - 00:08:52:23
Jamie
Yeah, yeah. I mean, I remember like way back watching that war games. Do you remember the film from the from the 1980s.

00:08:52:23 - 00:08:56:14
Tom
Yeah. Yeah, yeah. With the, with the green screens and.

00:08:56:16 - 00:09:26:00
Jamie
absolutely. Yes. It's been there on the fringes of, of, social consciousness for a long while, but obviously know ChatGPT has just kicked it into the mainstream like nothing before. So. Yeah. Yeah, absolutely. So I guess then when we're, when we're looking at that generative AI space and that revolution, that's that ChatGPT in an open AI, of course, is it's kind of worth understanding that we are right at the beginning of the cycle.

00:09:26:00 - 00:09:44:04
Jamie
You know, it's it's almost like back in the 80s and early 90s where the internet was a new thing, like, nobody everyone's really excited about it, but no one really knew what we're going to get from it or what some of the challenges of of using that are going to be. And it's exactly the same with AI at the moment.

00:09:44:08 - 00:10:05:10
Tom
That's a great yeah, that's a great comparison of like we I think most people agree it's going to be impactful. But at the moment we're all kind of guessing at where where the value is where where the ROI is. And and I think if you look back on the internet, even some of the most bullish estimations of how impactful it will be, it probably underestimated what it ended up being.

00:10:05:12 - 00:10:10:10
Tom
And it could could be the case with AI, but who knows what the time frame is.

00:10:10:12 - 00:10:33:09
Jamie
Yeah, exactly. And it feels like at the moment we're in this massive race where everybody's trying to bring a new AI tool to market and find those use cases and corner that industry, right. And everyone's doing it slightly differently. And, and that that inherently is also creating some challenges because no one's really got a proper grip on how to do this responsibly.

00:10:33:09 - 00:10:55:07
Jamie
Yet there's lots of different thoughts on responsible AI frameworks. Microsoft and other organizations have got a different slant on it. and, and how these different platforms are going to use our data. So we're all running out there. We're we're putting information into ChatGPT and we're saying, hey, can you help me with this and have a look at this document and summarize that for me.

00:10:55:09 - 00:11:16:13
Jamie
All of these different platforms I want treating our data differently. And some have quite strict rules on data security. Others will take all that information and use that to get better and to learn and to train their large language models. So does that mean our data could be surfaced by someone else at some other point who's written a similar query?

00:11:16:15 - 00:11:22:10
Jamie
Like, all of that stuff is all and best practices all still being worked out. And I think we.

00:11:22:12 - 00:11:46:07
Tom
Also the content for training the models is a big topic as well, isn't it? It's like like how what's fair use on the the internet content that's being scraped and these licensing deals that are being made with different big media outlets. So like Microsoft have got their kind of copyright commitment around if it's generated by them on the commercial tools, they'll kind of stand by it being defensible and big in different, different tools.

00:11:46:09 - 00:12:07:07
Jamie
Yeah, absolutely. And yeah, at the end of it, this is a business. It's a new business. It's a new section of technology. We've got to be aware that this is all being commercially driven. and we need to, you know, we need to just approach that sensibly and understand there's a little bit of self-awareness here of, well, you know, these tools are great.

00:12:07:07 - 00:12:43:21
Jamie
And on the surface it looks amazing. But I do actually need to know how that how they're being used underneath and what happens to all the information I put into them. Okay, it's really key. But yeah. And like we're saying it's it's big business. So I took some stats from, a recent Bloomberg survey, the market research type paper that, that predicted that I was going to grow to a $1.3 trillion market in the next, 7 or 8 years, which is just, you know, phenomenal that that size of growth and that acceleration.

00:12:43:23 - 00:13:15:10
Jamie
yeah. And within that, when I look at my area in terms of the IT services industry, it was predicted that we're probably, an $83 million market size for, for AI within IT services. But again, over the same period, that's predicted to grow to 86 billion. So this huge amounts of revenue here and like we were saying earlier, everybody scrambling to get a share of that pie right.

00:13:15:12 - 00:13:44:18
Tom
Yeah. And where the it's interesting isn't it, to see how much the, you know, the, the, the big global cloud players are really going to suck a lot of that, that money up to providing the underlying models. But there's also the, the small models, the line of business use cases, the the in-house AI tools. It's not really agreed yet how that's going to shake out between these large, you know, generalist models and the specific models and third party tools versus rolling your own.

00:13:44:18 - 00:13:48:04
Tom
It's all very, fluid and exciting at the moment.

00:13:48:06 - 00:14:25:16
Jamie
Yeah, yeah, definitely. Definitely. and then obviously, like, why is this so popular? Why is it currently such a big deal? And why is everybody so excited about it? I pulled some stats from, Microsoft's Work Trending Index, which is, an annual survey that they run that gathers all sorts of information around about businesses and modern ways of working with some really cool stats on, here you know, 62% of leaders are struggling with, you know, lack of information or ideation processes.

00:14:25:18 - 00:14:34:00
Jamie
lots of people are spending far too much time searching for information or stuck in meetings and not being productive.

00:14:34:01 - 00:14:56:06
Tom
and that one's super. That one's super resonates me. Right. Like having worked with various organizations, like all the information is out there somewhere in your possibly in emails, OneDrive, SharePoint. But can anyone lay their fingers on that? It how much gets recreated and reused or old things get used because we're we're not able to find the information we need.

00:14:56:08 - 00:15:17:19
Jamie
Yeah, totally. And the other thing for me is that post-Covid, I definitely find that I'm spending so much more time now in Teams meetings, calls and than I was previously, you know, and and that chews through a huge amount of your day. which again means I'm probably less productive than I used to be.

00:15:17:21 - 00:15:29:19
Tom
and your mental capacity as well. I think people underestimate, like, joining meetings, even if you're not. It's not your meet your super engaged. It just chips away at your your mental energy for that day. So that that's a big one as well.

00:15:29:21 - 00:16:11:10
Jamie
Yeah. Yeah. So I think I think across all sectors of the business world, everybody's latched on to all of these facts, and they've seen the potential for AI to come in and help them solve all of these problems. and transform, transform the way that, that we work and do business. But at the same time, those same organizations are concerned by a whole range of different challenges that that Gen AI going to is going to generate, kind of like basic things like what the ethical and social implications of of using these tools.

00:16:11:12 - 00:16:41:18
Jamie
what access to my data do they have and how do I need to secure that? how does this affect things like intellectual property? You know, if if AI will generate something that's very similar to something that I've got intellectual property rights on, like how what was the implications of that? You know, how do we get users on board with using this technology and not feeling threatened by it and not feeling like it's going to be the thing that comes along and makes them redundant?

00:16:41:20 - 00:16:42:05
Jamie
yeah.

00:16:42:05 - 00:17:00:03
Tom
And that's a really tough conversation. Like all the obviously all the marketing is going to the, the classic I.T story that we've all, you know, used for every technology ever, which is like we can get rid of the drudgery and you can be doing newer things, more exciting business impacting things. And that's definitely true to an extent.

00:17:00:03 - 00:17:15:08
Tom
But also like pragmatically, businesses are looking to get the most out of their employees. So people might well be nervous about, well, if this makes people, you know, twice as productive, do we need the same amount of people for these particular use cases?

00:17:15:10 - 00:17:38:13
Jamie
Yeah, absolutely. And I think a really critical thing here to focus on is that reskilling of employees. so I had a really interesting conversation. the other week, I was, I was on a panel for our internal annual company kickoff. and somebody was asking the question that, you know, they work with lots of customers in the education sector.

00:17:38:15 - 00:18:00:21
Jamie
There's obviously a lot of concern within that sector about, well, how do I how do I stop my students using this technology to cheat? and we had a really good discussion there about the fact that, you know, we are in an inflection point in society in terms of, you know, we've got quite old ways of looking at education and thinking about how we measure knowledge acquisition.

00:18:00:23 - 00:18:01:03
Jamie
and.

00:18:01:05 - 00:18:16:06
Tom
That word cheat is interesting, isn't it? Because I was on a call with Microsoft the other day, just yesterday, actually, and I said, yeah, I wrapped up. I was like, thanks, everybody. I'm going to cheat and get Copilot to bang the summary out. And then somebody or Microsoft called me out. They're like, it's not cheating, is it?

00:18:16:06 - 00:18:34:06
Tom
It's just using the tool. And I was like, yeah, it's funny. Like, same thing with the education. Like, my older daughter's only nine, but I'm starting to show her some of this stuff just to be like, you know, it's going to be a reality of this tooling. So what what is the point of education? Because people are going to have these tools.

00:18:34:06 - 00:18:42:14
Tom
Are we educating them to remember things, to think about things, to generate content? It's a whole interesting space.

00:18:42:16 - 00:19:15:16
Jamie
Yeah, absolutely. And and the way I think of this is that rather than teaching people which buttons to click in Excel or Word or Power BI or how to write a KQL script, if you're a security analyst, all of the facts that you need to remember to pass your exam is much more about teaching people to be critical thinkers, and to have a set of skills allows them to interact and search for and find the relevant information and draw conclusions from it.

00:19:15:18 - 00:19:41:07
Tom
Yeah, yeah. And how to leverage the tools I had a fun debate, a few months ago with a customer, and it was like, you wouldn't not give your employees access to the internet or laptops or mobiles. Like, that would be mental, right? Like how would you restrict their productivity? And I can make the same case for AI tools like, like you're artificially restricting their potential to, you know, deliver great value.

00:19:41:09 - 00:19:54:15
Tom
Obviously you got to be carefully measured and everything else but I feel like it's going to go the same way. Like you'd be mad at some point not to let your people have these tools, because you won't be allowing them to get the most out of themselves for you.

00:19:54:17 - 00:20:20:04
Jamie
Yeah, yeah. But I think unlike previous generations of technology change, where it's been very, very evolutionary, it's been one step after another. This is very transformational. It's a big step. You know, people have been very used to using technology. And you'll give them a new app and it's like, right, well, these are the buttons you need to press, but you're used to pressing buttons because that's what you've done in every other program you've ever used.

00:20:20:06 - 00:20:41:12
Jamie
But now with now, we're giving them this. I told it's like, well, here's a blank box, just ask it to stop. and it's a different skill set. So we need to get people transitioned over to that. And it is it's not as simple. It's not as simple thing to do. Giving giving people that blank piece of paper and asking them, you know, off you go.

00:20:41:13 - 00:20:57:11
Jamie
Have a play. so we need to we need to help them understand how to how to create a prompt that is fair and gives accurate results, and how to detect bias in the questions that we're asking and the response that we're getting back.

00:20:57:12 - 00:21:22:03
Tom
like it's been critical about the responses as well. It's it's a weird it's a weird one in terms of technology to be like, here's a tool. It's amazing, but also be cynical and critical about what it returns because it can be wrong. In fact, it's fallible. Well, the, so, like, I think it's tough, to have a conversation with employees sometimes around here's a great new tool, but use it loads but don't trust it.

00:21:22:03 - 00:21:25:15
Tom
But do use it but don't trust it like it's it's a weird one.

00:21:25:17 - 00:21:46:09
Jamie
Yeah. So when I'm talking to customers, the way that I describe this best is by comparing it to having an intern in your team. And there are really valuable resource you can get and go away and do all sorts of great stuff, but you have to really give them clear instructions. You have to spend a little bit of time reviewing what they what they provide you back.

00:21:46:11 - 00:22:02:23
Jamie
and also you can't expect to just give them one instruction and have them go away and then come back with a perfect result. You know, it needs to be a bit of conversation and a bit of back and forth, right? Yeah. Getting the most out of Generative AI is that is the same. You've kind of got to treat it a bit like an intern.

00:22:03:01 - 00:22:16:08
Tom
That's a good that's a good, good mental model. What what are you guys doing in terms of working with those customers to help? You know, it's one one thing I was saying this on the podcast, but how do you do that with employees?

00:22:16:10 - 00:22:39:13
Jamie
Yeah. So we've spent a bit of time internally developing a framework, specifically around Copilot and at the moment more focused on Copilot for M365, where we, where we'll run almost like a sprint process. So we'll take a, a tranche of users we’ll educate them on what Copilot is and what the value is and the how to use it.

00:22:39:15 - 00:23:03:05
Jamie
We'll drip feed them training content, video, snippets. we'll have weekly meetings. We'll track their usage so that we can give them, key information back. We have, what we call Center of Excellence team, where, you know, people can come together, share hints, share tips. we use.

00:23:03:07 - 00:23:10:23
Tom
A pretty sustained effort. They're not just, here's a here's a video. Here's a training course. Off you go. Like a multi-week process.

00:23:11:01 - 00:23:42:06
Jamie
Yeah. So it's about 8 to 12 weeks per sprint to take, you know, a group of users or a community of users through that whole end to end process. so, yeah, because, you know, it's what we tend to find is that, if you just throw it out there, you'll give people a training video on it, they'll have a play for a week or maybe a little bit longer, and then very quickly they'll default back to their old ways of working, because humans just naturally do that.

00:23:42:06 - 00:24:16:12
Jamie
Right? Yeah, absolutely. So you need that sustained communication and engagement period to, to truly affect change. but also to help them get them up the learning curve about the best ways of using this technology and working with colleagues to share ideas and things. at Microsoft, we've got an app called, Prompt Buddy, which is really good for collecting internal knowledge about the best ways of using AI and sharing that across teams, like capturing those organization specific use cases.

00:24:16:18 - 00:24:18:19
Jamie
That's definitely worth having a look at.

00:24:19:00 - 00:24:22:08
Tom
Oh good tip. Thank you.

00:24:22:10 - 00:24:50:16
Jamie
so like when we're looking at that that Microsoft proposition around Copilot, that's like so many different things Microsoft are doing with Copilot at the moment. And it's worth understanding, I think that Copilot is not a product. It's it's a suite of products. And we've got the Copilot tools within the modern workspace for things like M365 and Windows Copilot and Bing Enterprise Chat.

00:24:50:16 - 00:25:05:22
Jamie
I think there's been recently renamed Microsoft Love a rename. yeah. And then we've got the tools that you were talking about earlier in terms of like GitHub Copilot in Power BI. just a new security tool that's been releasing.

00:25:06:00 - 00:25:27:06
Tom
I did a session over at Comms vNext, and Microsoft seemed to be defaulting to in the product is part of a bigger skew. And for the product is like an add on. So for Microsoft, 365, first is versus in Excel. And I think I got up to I think it was 14 fours that are genuine separate product SKUs.

00:25:27:07 - 00:25:37:07
Tom
And then there were like something like 19 ins. that's a constantly growing list. So really range of products, range of use cases.

00:25:37:09 - 00:26:04:14
Jamie
Yeah. And and I think at the moment Microsoft have probably got the strongest portfolio of, of generative AI tools and services, as it seems to stand pretty much the entire Microsoft stack in terms of capability. which is great. I obviously, I work for Microsoft Partner, so it makes my job really easy because I'm going out there saying, well, you know, this is the most complete set of AI tools on the market at the moment.

00:26:04:16 - 00:26:06:02
Jamie
Yeah, yeah.

00:26:06:02 - 00:26:25:05
Tom
And it's like, well, you can work backwards from the use case, right? Isn't it? I love the GitHub Copilot. I mean, so obviously that kicked it all off the Microsoft. But I think that's a really strong Gen AI use case. And those uses are already technical already coders is can can unlock value there. obviously modern work is getting there.

00:26:25:10 - 00:26:33:21
Tom
The the most hype in our space because the potential for the productivity improvements are massive. so that's really exciting too.

00:26:33:23 - 00:27:03:16
Jamie
And that's the easy place to start, right? It's really easy helping an organization understand that. Well, you know, here's a whole bunch of meeting summary stuff that you can do easily, or here's a tool that's going to help you discover documents across your your SharePoint environment that have the information that you need. Those use cases are really easy to hang your hat on when when this is all new to an organization, but it also provides that foundational understanding of what Gen AI is and how it works.

00:27:03:16 - 00:27:25:04
Jamie
And and you can use that as a springboard to then start looking at, well, okay, can I can I build this into some of my contact center applications or my line of business apps? And, you know, and then you start getting into all of the great stuff that you can do with, you know, Copilot studio, and extending this into other data sets and other, other applications.

00:27:25:05 - 00:27:38:11
Jamie
Yeah. but I think we need to get people to that common understanding of what it is and what it's good for, and how you get the best out of it before you start branching off into all of that custom stuff.

00:27:38:13 - 00:27:56:04
Tom
Yeah, it's interesting, isn't it? Because actually, like, I mean, some of the custom stuff can be some of the biggest value and really good ROI because you're into kind of consumption models as well. but then you've got the if you're in line of, yeah, I guess knowledge worker would be the right phrase of your in Outlook.

00:27:56:04 - 00:28:04:19
Tom
Every day you're in Teams, every day you're in Excel every day. Then really, that's where the the Copilot of M365 really comes to life.

00:28:04:21 - 00:28:31:15
Jamie
Yeah, absolutely. And if you can bag all of that, that's saving, like that, that's obviously got to be the place to start because that that's where you're going to get the most cross user. easy business case in terms of ROI and, and use case. but again, you need that to build the understanding of what else is possible.

00:28:31:17 - 00:28:53:12
Jamie
yeah. yeah. So I, I think it's a really interesting space. I'm really interested to see what's, where this is going to go going forwards. And, which organizations I got to float to the top of the pile when it when it comes to making the most out of this technology, I'd like to think all Symity customers, are gonna be somewhere near the top, obviously.

00:28:53:14 - 00:29:17:20
Tom
So, yeah. Yeah. And it's really exciting. And I, I'm, I'm really selfishly excited that Microsoft have, you know, got on this wave early and are in the mix area you and I both focus on. but more generally as a, as an industry, it's just a super exciting time to see how fast this technology's moving and how people can use it to add value.

00:29:17:20 - 00:29:20:11
Tom
Increased productivity. Definitely.

00:29:20:13 - 00:29:22:12
Jamie
Definitely. Yeah.

00:29:22:14 - 00:29:35:05
Tom
Awesome. Well, thanks Jamie for taking the time to explain that. It's, it's really useful to get your point of view particularly as you're working with the customers. if people want to find out more about you or Symity, what's the best thing to do?

00:29:35:07 - 00:29:48:01
Jamie
yeah. Well, reach out, tap me up on on LinkedIn. So it's, jamie.wheeldon@symity.com. or you can just head over to, to our website, right. Which is, symity.com

00:29:48:03 - 00:29:50:14
Tom
Awesome. Thanks for taking time mate. Talk again soon..

00:29:50:16 - 00:29:55:07
Jamie
That. That's been great. Thank you very much.

00:29:55:09 - 00:29:57:07
Jamie
For.