Hey channel insiders, welcome back to channel insider partner POV. I'm your host Katie Bavoso and my guest today is Dory Albert, CEO of Spyglass MTG, a Microsoft consultancy. We dig into how Spy Glass is leveraging AI to help customers build Microsoft environments with confidence and why so many solutions and service providers struggle to execute this kind of task. Plus, we'll learn how Spy Glass uses AI internally to stay competitive in the very crowded Microsoft ecosystem.
Welcome, Dory. So great to see you. >> Great to see you too, Katie. Thanks so much for having me. >> Thank you for being here. I know I'm talking to a fellow New Englander. So, it's very nice to be able to sympathize or I guess commiserate about the weather that we've been experienced here. Just a lot of cold. A lot of cold temperatures. >> Been a doozy. >> It's been rough. I Are you dug out by now from the two feet of snow that we've gotten? >> Dug out.
Um, still frozen. Everything's frozen. Although, we did have a 30°ree day yesterday and I noticed lots. >> Yes. I my roof thanks the sun and the below freezing temperatures because of that yesterday. So, spring is what they say it's another 6 weeks, whatever that groundhog said, but I don't I don't think so. I think we've got a while. >> I think it's a lot more than six weeks. Oh, yeah. I was thinking the same thing. >> Well, thanks again for being here.
We'll talk about some much warmer topics as we go through today. Uh so, I'd like to start by getting to know a little bit more about Spy Glass MTG for our audience. And I should point out the MTG stands for Microsoft Technology Group, which tells us a very important detail about Spyglass. You're a boutique consultancy delivering data, AI, and security solutions on Microsoft platforms. So with all of that said, what is your core mission over at Spyglass and who are your customers and verticals that you serve? >> So yes, like you said, Spy Glass is a boutique consulting firm.
Um, we specialize in all the things you mentioned, AI, hot topic these days, data and security on the Microsoft platform. We also do data bricks and some other technologies too, but Microsoft's really our core. And our focus is really to help our customers drive into the digital realm with AI data and do it with um the security and governance that they need to feel comfortable. AI, as you know, is a new and scary and exciting area that we really do need to make sure we have governed and managed in the correct ways.
And the only way you can do that also is to have all of your data platforms connected and make sure that you have accurate data. So, we're really on a mission to help our customers achieve all these things. uh we had an announcement recently about a new framework that we'll talk about, but that really helps us move faster and better. From an industry perspective, we really work across industry. You know, we're very specialized technologies, so we don't really focus too much on one industry, but we do have a lot that we have a lot of customers in, such as higher education.
Being here in New England, that makes a lot of sense. Financial services and insurance, health care, manufacturing, but we also dabble in legal and oil and gas. So, we kind of spread the diversity across several industries right now. lots of heavy regulated industries in there as well as what you were just talking about which I know comes with its own set of rules, regulations and headaches. So when we talk about AI, I look forward to digging into that a little bit further.
And in fact, let's let's keep going down that AI rabbit hole because many providers in the channel say that they do AI, putting air quotes around do AI for those listening on the podcast, but you've been vocal that most AI projects fail if done incorrectly. When you look at today's market, where do you think solution providers and service providers of the channel are still getting AI fundamentally wrong? And what are the ones getting it right doing differently? >> That's a great question, Katie.
So AI in general, there's actually an article, I'm sure you've probably seen it, that MIT came out with back last summer saying that 95% of AI projects show no ROI. That's terrible. And every customer that you talk to wants to do AI and and a lot of times it's actually driven from either their executive boards or their CEO typically coming to it, not necessarily the business and saying, "Okay, we need to have an AI strategy. You need to do AI like you said." And that can mean all kinds of things.
And that actually caused lots of bad behaviors, especially over the last year, of companies jumping into AI just so that they could say they're doing AI. And I mean, there's not a service provider out there now that doesn't say they, like you said, do AI. But to correctly put any platform in in place, and especially AI, you really have to think it that of it that way. How am I going to implement this new technology platform into my company? And unfortunately what I think a lot of companies do and a lot of service providers did over the last two years is we just jumped in saying oh let's build you a use case what's your use case let's build an AI app and a lot of times that application that was built was you know a test pilot to try it out and that makes sense that's what happens with all new technology but what ends up is that you have an application that's not connected to correct data that it hasn't really been thought through like if we build this what value ad is it going to bring to my organization other than it's cool.
So there were so many projects that we saw and and even you know admittedly participated in in the early stages where we were just trying to get an application out there that was an edict that came from top down. Now, we've gotten much smarter and we've really organized our whole company around this that you have to look at implementing AI as a platform. And what does that mean? You have to think about what data sources are you pulling into AI with the models themselves.
If it's not correct data, then the answers you get are not going to be correct. Obviously, that makes sense, but fundamentally, we see it over and over again that there's disparit data systems. the company hasn't taken the time to actually pull their data into a cohesive platform where they can actually use AI on top of it to make real informed decisions. Secondarily, the governance of AI is is scary and it's dangerous and you really need to before you start building things, think about how that's going to be used in your company.
It's it's shocking how many companies still allow or don't maybe don't allow but accidentally allow the bring your own AI to work because company secrets company data is getting out into these models and you know I don't care what any of them say that's going out and training these external models and that is a huge potential breach of internal uh information that you don't want out on the internet on the models etc. So it's really important to think about the governance from a technology perspective but also from a policy perspective and this also requires a lot of change management right I think in the very many years that I've been doing technology this is the first time that I've seen a big embrace of what change management really is required for something like this because it's going to change how we do everything in our lives changing technology in ways that the computer changed changed the world and that the internet changed the world maybe even more so.
Um so everyone needs to think about that. So it's a combination of I guess policy change and technology and what do we do from the beginning and start by creating some of the metrics that make sure we do it right. >> Very well put and I agree with you on several of the things kind of converging to make this to make this a success and to at least make sure it's not a failure. Uh, as we were saying, let's talk about your AI navigator. You mentioned it earlier.
So, Spyglass recently introduced AI Navigator and you've been clear that it's not a standalone product. It's a framework and an accelerator. Could you explain to me what exactly the AI Navigator is and what it allows your clients to achieve? >> So, AI Navigator is a framework. Um it is something that we have really put together over the last several years of working in this industry and it's basically collection of data and technology really which comes in in the mode of of code um to help expedite our projects.
And what that means is that we built basically just a SharePoint site that is organized into the phases of AI Navigator which we take clients through and it includes a lot of the documentation and for instance architectural diagrams and things that we have put together for best practices to help us jumpstart projects as well as calculators and other things that help us with the customer's decision points on what's the ROI I going to be what do the use cases what use cases make the most sense to move forward to so kind of at that beginning stage of how do we select what we're going to do what is the ROI and business case to help them as they're going in their journey what are the core foundation pieces that we're going to put into place that's kind of the part two and that when we talk about core foundation that's really when I'm talking about the data platform the governance platform the infrastructure that needs to put in which is almost always cloud infrastructure these days And then third, what is the development process that we're going to go through to kind of build out what applications are identified that make the most sense.
So it's this combination of years of documentation that we've put together as well as what we call code accelerators. And um it's funny when we first started rolling out these code accelerators, we actually have names for them. So one is called Fabric Loom, another one's called AI Genie. Customers were really getting confused. They were thinking this is a product and it's not a product. It's really code snippets. And these code snippets basically help us put code into their environment.
So we're not starting from zero every time we do a project because there's certain things that every one of these projects will need. And there's always customization that needs to happen. But these code uh accelerators that we have are given to our clients as part of our delivery process. And the really cool part about that is it it speeds up the process and it it standardizes a lot of things around how we put things into production. The other thing that it does is that we actually built our own agents on top of this framework.
So our agents are used by our delivery team and we're delivering projects and essentially they can go in and say okay I'm building a fabric data platform and it includes data bricks. what are the uh tasks that we need to complete in the first phase and it will go look at all of our documentation and just like chat GPT or any of these AI models will come back with okay here's the scope and here's what you need to do for your first 10 tasks so it's really a our own agent that we've built um also on an agentic architecture because we have a an architect agent we have a developer agent we have a project manager agent so basically you say I'm a project maner manager, I need to do XYZ and it will help us deliver more efficiently and more accurately.
The other really cool part of it is that with all of these code snippets, we have them all housed in GitHub and it's it's thousands and thousands of lines of code that we have created over the last few years. um which in which represents about $700,000 of investment that we put in ourselves and we are now having all of our development team leverage GitHub co-pilot which is another AI agent within the Microsoft realm that helps with coding. So we're really doing everything we tell our customers to do.
We're leveraging agents so that we can be more efficient and we can provide our services to them more efficiently as well. Let me ask you more about the the point you just made about GitHub and releasing that GitHub copilot releasing that internally. You referred to that as eating your own dog food. Uh making sure that you're kind of practicing what you preach internally. So talk to me about how you've seen that affect your team's day-to-day work.
Have there been any outcomes that you go this is really making the difference that we thought it would or are we still working up to that? >> So we officially started that this fall. So I will say it's it's fairly new. Um that being said, we have seen one example that I can think of that's really specific is that when we would do um a PC, for example, we typically would have to hire a UI designer to come in and build out that UI, right? That you're going to actually show the PC on.
And the UI isn't really something that customers care that much about when it's a proof of concept. It's like you're just trying to prove out something to make sure that it's going to work and that it makes sense for them, right? So, you don't want to spend a lot of money on some fancy UI until you have decided that you're going to move forward. But, we typically would always have to engage um a UI expert for that. And that was typically that we would something we would do with one of our offshore partners because it's not an in-house.
We're really more architecture and and development focused. Now, with GitHub Copilot, we actually can have GitHub Copilot develop our own frontends for these PC's, and it's pretty amazing how well it does. Um, it's scary and amazing at the same time. But that's one example that I can tell you. I mean, someone that really has never built UIs before can go in that's maybe a hardcore data coder and actually develop a UI for a proof of concept. Now, that might not be how we want to do it for a production system, for example, but the fact that we can make progress like this is really fascinating and amazing and and we're personally, you know, trying to increase everyone's productivity.
And I look at it like it's not now, you know, AI is a scary thing when we look at jobs and what is that going to do for jobs, but I don't look at it like that. I look at it like how can we make everyone more productive and how can we embrace it so that something that would take us a month before now takes us two weeks. >> Impressive. Yeah. And I I agree with you where you go. This might not necessarily be something we'd use on the production side going forward into what our clients might expect to see from us as a final draft, but the idea that you can get there that much quicker is really impressive and that it can show you something that much more put together quickly.
Back over to your customers using the AI Navigator. You've talked about it as a way to slow companies down before they rush into building their own bots or co-pilots. What kinds of conversations are you having with customers who want to just build something as we talked about earlier in this conversation? And how do you reframe that urgency into something more sustainable? How do you get them to understand, hey, you don't need just to check a box to say, we have AI done profitable.
It doesn't quite work that way. How do you talk to them to make them understand we can get to a better side of this without having just to throw resources at it with with blind intention? >> Yeah, that's a great question. So, we really do take that first phase of AI navigator and say what is the outcomes that we're looking to provide out of whatever project that it is. And you can't always stop something. I mean, a customer calls you and they want to build, but we do insist that they take a pause and we take them through a process that says, "Okay, what are the ROI?" Or maybe you can't identify ROI right away, but what are the business metrics that we're trying to hit here?
Um, and that really does help people because a lot of times they're again, it's coming from top down. They're told you just have to go do it. and we say okay that's great but before we decide to do this you don't want this to fail right so let's take you through the process of of what we have and we actually have some calculators that we use depending on the technology like with co-pilot for example we have a process and calculator that helps them calculate some of the ROIs that there might be out there so that's one way another way is that we challenge customers to say okay this is you want to do this So, so why you know explain explain what this is going to do and for who?
And I think that makes a difference too. I will say we also see a lot of infighting with customers because they'll say, "Oh, okay. We want to do AI like you said." Um, but finance wants to do this and operations wants to do that and maybe the CEO wants to do this. Well, you know who's going to win that battle? But there's kind of a slow down process. And then the other thing that we we try to really instill in them and I think that governance and security is where you start to hit home is if you don't think this through there's a danger for your business, right?
So I think when they sit back and you explain these are the fundamentals that need to be set up so that you don't have a a risk and an exposure of your data. And that's where I think customers really do sit down and start to think about, okay, you're right, like this is a platform. It's not a a one-off, if you will. >> Knowing how new this is, uh, I I temper my next question with that reality, but what does customer success look like with the AI navigator?
Even if you were to say this is what we're hoping to see in the future, if you don't have an actual use case, but would you be able to talk about what success looks like, what would it mean to you? >> I mean, absolutely. because I think I think even before we called it AI navigator we've been doing this right so success and um I will say given even where AI is in its journey right we're still on the hype cycle going up here there aren't that many customers that are in pure production leveraging AI uh to run their business that is the ultimate success we're all trying to get to in the meantime what we see success as is we develop a project and a platform and begin with use cases that are actually showing real results.
So, you know, that includes some upfront work with the customers and an investment. You know, these aren't $25,000 projects like these are expense if you want to do it right. These are fairly expensive to put a full platform into and and and thought and knowledge about how you're going to do it. A lot of customers and companies think that using AI is like, okay, I'm just going to go get the paid version of chatbt and that that's not what we're talking about here.
This is really about how are you implementing this into your everyday process to make your employees more productive, to make your customers more happy, to make your students more effective. So that's really what we're trying to get to. And we do have some examples where we are there. But I would say in the uh journey of where AI is right now in the industry, that's where everyone's trying to get and we're starting to make that leap, I think. And I do think 2026 is going to be a big year for that. >> I agree.
Yeah. Looking forward to seeing what else comes out this year. With that in mind, so we talked about data earlier and a bit about data readiness. You made a strategic shift years ago to focus heavily on data before AI became the big headline and the big to-do. How does data readiness or lack thereof show up as the biggest limiter to AI success today? >> My background is predominantly from data. And in my opinion, data is king. Data is is the core asset that every company has and owns.
And I know this being in the industry, Katie, you know this, but it's no shock. So many companies have data all over the place. They're siloed. They don't talk to each other. I mean, effectively, you can't you can't really globally roll out AI if you don't have your data in some sort of connected fashion. Like for example, we are working with um a higher ed university right now and this is one that does a lot of things online and they are very smart.
They are investing a bunch of time and money right now into pulling all the student data together because their ultimate goal is that they want to have an advisor that can be a basically agent advisor for each student. That's a first stop for all your questions. But if you think about, you know, these online universities, there's students working at night, there's students working in different time zones. Like having an adviser available to you to answer questions at all times isn't possible.
So there's a lot of self-service that you can get, but if you have one system that has their course loads and you have another system that has what the next semester brings and and credits that you need to have. I mean, it's really hard for these students to navigate. And so they're taking the time to pull all of that together. And that goes for every industry, right? Like if you're if you're trying to have an operational efficiency and you don't have your data in a place where you can actually provide whatever that information is or the use case is that you're trying to typically ask questions to, right?
Because if we think about AI, it's not all that it does, but a lot of what we're seeing at the moment is kind of that chat interface where you're asking questions back and forth. Well, you can ask the AI, but if the AI doesn't have your company's information in the right format so that it can actually look at it and make informed decisions, then you're going to get what the AI is answering from that model, not what you know your years and years of information have as part of it to make informed decisions. >> I'd like to talk a little bit more about the cyber security and compliance side of artificial intelligence use.
Um because as you know as organizations expose more data to AI models, governance and compliance move from an IT concern to a business risk. And I definitely think we're seeing that shift in the mindset of customers where they understand, oh, this isn't just an extra x amount of dollars you're trying to squeeze out of me. I actually do need to be compliant for x number of reasons. So how are your customers thinking differently about security and governments now that AI is part of everyday workflows? or are they not thinking about it and you're having to let them know this is something you need to be considering? >> So I think a year ago that would have been a different issue answer.
I think a year ago people were really just and again everyone was bringing AI to work. Everyone was putting data and I think everyone's gotten smarter about that. I think that most customers are pretty aware that it's a huge compliance risk and governance risk uh within the within the companies and even if you roll things out internally and you allow AI to ask questions, how do you make sure that a person at this level isn't getting information access to information that should only be at this level for example and that's a huge risk anywhere, right?
Um, identity and access management has been a term that's been around for quite a while at this point, but identity and access management with AI is a whole another level because you really have to make sure that answers aren't coming back with things that are proprietary and only for certain individuals, right? One of the things that we love about Microsoft and one of the reasons we're a Microsoft partner is that because majority of companies operate their businesses on Microsoft platform, the security tools are already embedded to make sure that even when AI is introduced that a lot of these things are in place, but you have to, you know, you actually have to turn on all these services that allow for identity and access management.
I mean, you know, mobile device management, all those things that are really core to making sure that you have a protected system. So, I think it's more there's more awareness around it. And what we don't like to see and we and we do sometimes see is that companies create within their security divisions, you know, a governance AI governance and it's really about shutting everything down. So, that that's hard too because you get it right. it it's a it's an unknown and sometimes people's jobs are to shut down but if you go about it the right way and you architect things correctly then you definitely can protect. >> Very well put.
So you mentioned a goal of growing without adding headcount unless absolutely necessary. This was something that we talked about when we were chatting ahead of the interview. How do you think AI changes the traditional consulting model, especially for boutique firms like yours that are competing in such a crowded Microsoft ecosystem? There are thousands of other providers within the Microsoft ecosystem. It's more massive than I think either of us can truly imagine.
So when you think about how AI is differentiating your business, how has it impacted just the way that you do business? >> That's a great question. So first and foremost, we have shifted our business really over the last I would say probably last 18 months to an outcomebased pricing model. So I think this isn't just going to be in IT professional services. This is across the board because the the old model of and it's not gone, believe me. But like the old model of just hourly resources is kind of going out the window because everybody wants to pay and put a value on what you're getting, right?
Um and as a provider, um hourly rates and hourly business is is difficult. I mean, think about the legal industry for example. I mean, that's that's how they run their business, right? But even that industry, I've heard, is getting quite a bit of pressure that you're going to pay for an outcome, whether that be, you know, and and some of that is in place, like a divorce, for example, or a a home transaction is kind of a fixed fee type of model, but that really is something that we have started, we started moving to about 18 months ago.
And we find it to be much more effective both for the customer and for us. And the goal as a services firm is that we can become more productive internally and provide the same amount of value and eventually that will probably drive some pricing down for customers which is good for them but also for us hopefully we become more profitable which which is you know a win-win on both sides. Now that being said what does that mean for the industry right I mean um what does that mean for jobs in the industry? does that mean for I think that's another whole area that you need to think about in this space and and we don't look at it as like we're trying to replace jobs.
It's just really how can we be more productive um and how from an industry perspective how can we be more competitive like you said um and I think AI is helping us deliver things better and faster and more strategically for our clients. In thinking about your clients, you you've you're seeing a lot of increased momentum in industries like legal and higher education. Again, we talk about being in New England, plenty of higher education schools around here with big names that I'm sure you're familiar with.
These are spaces that, as I mentioned, are extremely regulated or just generally document heavy. Without names, unless you can name names, what are some of the most compelling AI use cases you're seeing actually deliver value today within your customer base? So, one of the coolest ones that I can't right now name name, but will be going public soon, so stay tuned for that. Um, is a global insurance company, and we've been working with them for several years, long-term client.
Uh, but we worked with them to really implement a full data, AI, and machine learning platform. So, it's a combination of Microsoft technologies and data bricks. And one of the coolest use cases that they've implemented that's actually in production that is actually showing uh real results has to do with how they insure factories. So it's a it's a company that actually ensures factory facilities. And in order to do that they actually have to send engineers out to the factories and they have to walk around these factories and they do an evaluation essentially.
And so they're evaluating them for things like, oh, is the fire system up to code? Or, you know, how is the machinery spaced from this to that? And there's there's I think I believe that the number is over 20,000 pages of PDF standard operating procedures that they have to go through to make sure that everything is up to code. So that's two things for them. It helps them make sure that it's an insurable entity, right? But it also gives them upsell capabilities because they have their own fire suppression systems and other things that are kind of um additional um offerings that they have for their clients.
In the old days, they would have an iPad that literally just had this list of all of these PDF documents that they would be scrolling through. What we have created is an agent for them that sits on top of this this iPad that they walk around with and they can ask questions and the questions um I believe we have like a 99.9% accuracy answer rate and it's it's pretty cool because it it includes like diagrams of things and other things that have to be read obviously that aren't just straight text.
So that has sped up the process of them being able to actually go and do these evaluations and made the job of that engineer just so much easier. Um so they have you know a better evaluation when they're deciding whether to insure as well as additional upsell opportunities. So that's a really exciting use case um that we've seen. Another one is for a medical device firm, another global medical device firm, and they do uh they provide respiratory care products.
And what happens when uh you go into a hospital and your doctor says, "Okay, I'm going to give you a you know breathing machine to go home with." That is a prescription that first goes to the medical device company. The medical device company has to go through and make sure that it's the correct device for this person. They have to do an evaluation of whether they think it will get covered by insurance. There's like a whole process that they have to go through.
As you can imagine, what comes through to these companies is you don't even know. It's the it's a PDF of medical records of and it can be pictures, it can be x-rays, it can be you know this this thousands of pages in one document typically a PDF of all different formats. And it was a very very difficult process and it was all humanled to go through these these documents to try to decide that this was approved. Basically this prescription was approved.
So we're not at all I AI is not at all trying to make decisions about whether something is approved or not. what the um agent is that we put on top of this is one that pulls out all of the relevant information out of that thousands of page documents and points the person that's doing the evaluation to the points in that file so that they can say okay well here was an instance where you know they had a problem breathing go read that here's another instance of a diagnosis okay go read that so that they have a much easier time I believe that it increased the speed of eval valuation of these documents by like 125%.
And at at the time when we first started, they were only getting through 20% of of these prescriptions that came in on a monthly basis and now they can get through all of them. So, it's like a huge, you know, real life um example of where it it's for the greater good, I feel like, you know. Um, but again, not not AI deciding, just AI helping the person that was already doing the job get to the information faster, >> which I think is such a great use case of showing how humans with AI are going to be able to outperform humans without AI.
It's not always about AI being the scary competition. It's about how do we use it as to use a word we used a few times, an accelerator, and this is a perfect use case to show that. So, I love that. Oh, really great examples. Thank you for sharing them with me and I look forward to uh naming names in the near future when you go public with them. So, thank you for that. Dory, my last question today is a more personal one for you. You lead an IT consultancy in a space that's still largely male-dominated.
And I'm not just talking about the Microsoft ecosystem. I'm talking about technology, the channel, the industry as a whole. What has helped you build credibility as a leader? And how do you actively encourage women to pursue leadership roles in both technical and channel focused careers? >> That's a great question. I think we tout that were women led uh womenowned. I do think it's a really important part of of my personal journey um in the IT space and I'm sure as you know it the whole industry is predominantly male-dominated.
As the years have gone on, I think it's gotten better, but there's it's still still not. I mean, I will say for any role that we have, it's still 80% men and 20% women. And there's no silver bullet, I don't think, to helping women in and in getting them more into the IT space other than, you know, I think that trying our best as as females in this space to encourage others. And I will say early in my career, I did find it somewhat difficult, especially dealing sometimes with IT managers, that women weren't supportive of other women.
Uh I don't understand that it's it's, you know, especially as a as my in my young 20s, I found a lot of that. I do think that that seems to be starting to go away. There's been like a nice movement of of how do we help each other and not go the other way and not help each other? And again, who knows where that started from in the in the in the days of time. But I think all all we can do and all I've tried to do is be as supportive as I possibly can.
At the same time, I always say I want the best person for the job. I never really try to see gender or see race or see anything like who is the best person. Um, but the more we can encourage each other and the more we can lift each other up and the more we can try to encourage more women to have a voice. Um, I think a lot of times that we tend to be more shy. Uh, especially in technology. I see this too. Like there's a lot of men and they're all everyone's brilliant, but a it takes a certain personality to be able to speak up in that environment and be confident and I do my best with with all the women on our team to to do that as best we can.
So, I think it's just about encouraging each other and lifting each other up as much as we can. So, one example that I I like to really think about is I was lucky pretty early on in my career. I had a female mentor. It was kind of when I first went into sales. So, I started out technical and then I moved over into sales. And I was really lucky and it was really unique, especially back then to have a female leader. She ran kind of the branch that we had in this national company. and she took me under her wing and made it a point with me to meet with me weekly and to really help guide me in my career.
And that was a huge lift for me. So, I try to pay that forward especially with any of the females that are direct report to me or a level lower. I try to have weekly or bi-weekly or I I even have a quarterly ladies lunchon where we get all the ladies of spy glass together and we talk about different topics like what's one challenge you had in your career. We kind of have topic questions that everyone comes in with which is fun. The last one we actually had everyone come in and say what's your song for 2026 and why.
So, some things like that. But I think having a a female mentor is is important and it's hard to find in technology. And so, if you if you have one, it's it's it's definitely a leg up and I try to be that myself and hopefully other people listening to this will will do the same and are doing the same. >> I love that and I agree with you. I think a lot of the work starts with us and how we treat each other and how we're supporting each other. And I agree with you in seeing I've had the same experience where women seem to feel a little bit more competitive, especially in the earlier side of my career when I was in my 20s.
And I think it's because we're all competing and we lose sight that each other is not the competition here. Uh because we're all trying to to get further in our careers, but if we help each other and lift each other up, we'll get there faster and we'll sleep better at night too doing it. So I I agree with you where that experience has has happened. But I do feel it becoming easier as we go along that we all are understanding this more and that starts with having more conversations.
I've absolutely loved this conversation today though, Dory. So, thank you so much for your time. Before I let you go, tell us if we'd like to get involved with learning more about Spy Glass MTG, the AI Navigator, and all the great things that you do at your company. Where can we go to learn and potentially even begin a relationship as a customer with you? Well, obviously you can start with our website which is spy byglassmtg.com. Reach out to us there or info@spyglassmtg.com which is our our email and yeah we would love to have a conversation.
We would love to come in and show anyone interested how we use AI Navigator, what it looks like and how we can help. So please reach out. We would love that. >> Dory, thank you so much for your time today. I'm excited to be able to continue to follow along with Spy Glass through the rest of 2026 and good luck through the rest of the year. >> Thank you so much, Katie. I really appreciate you having me and uh it was a great conversation. >> Thanks so much to Dory for joining me today and thank you for watching or listening.
You can check out every episode of Channel Insider Partner POV on channelinsider.com or watch us on YouTube at youtube.com/ channelinsider_news and trends. You can also listen to us as a podcast wherever you get your podcasts from. Don't forget to like, subscribe, and follow wherever possible so you never miss an episode. Come connect with Channel Insider or me, Katie Baboso, on LinkedIn and X. Once again, I'm Katie Baboso, and I'll see you next time.
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