Insight ON The Truth About AI Hype — And How Leaders Are Turning It Into ROI

Audio transcript:

The Truth About AI Hype — And How Leaders Are Turning It Into ROI

Bijah Gibson:

I think that the capability that AI gives organizations and non-technical people mm-hmm . To make a difference in what they're doing without having to rely on an IT team is not hyped enough, right? The idea that we can get people all across our business operating more efficiently without having to go and get a ton of money to run a big initiative and depend on it and all the things that go with that, but actually getting that in the hands of those folks and helping them to make their day-to-day life easier and get operating leverage is huge. If

Jillian Viner:

You're making technology decisions that impact people budgets and outcomes, you're in the right place. Welcome to Insight on the podcast for leaders who need technology to deliver real results. No fluff, no filler, just the insight you need before your next big decision. Hi, I'm your host, Jillian Weiner, and today I'm joined in the studio with Jeremy Hodge. Well, Jeremy, thanks for joining me here today. Yeah, glad

Jeremy Hodge:

To be here. Jillian.

Jillian:

We were at Ignite what feels like a long time ago. It was only a couple weeks ago. That's right. Yep. We took the advantage not just to hear what was going on with Microsoft, but really just to sit down with experts from insight from Microsoft, from other partners, and even a couple clients, which we will hit on those, uh, in a future episode. Yep. Um, but it was really fascinating because I remember the drive from the airport to the venue. Billboard after Billboard was an AI focused ad. Yep. Then you get to the venue and booth after booth. It's ai,

Jeremy H.:

More AI messaging

Jillian:

Everywhere, more AI messaging everywhere. Um, and I think more than anything, my biggest takeaway was that the conversation very much is AI centered, but it is still feels like it's so much hype.

Jeremy H.:

I think we saw that with all the messages and what people were saying. Everyone's talking about ai, you can't escape it, but a lot of uncertainty of what do we actually do to drive real business outcomes. So I thought a lot of discussions we had with, you know, various leaders and our partners, um, and the clients really reinforced like, what are the steps you need to take to actually drive real AI results?

Jillian:

Yeah. And I think what was kind of comforting too is to find out that in fact everybody's at a very different stage of adoption. That's right. You've got organizations who are just getting started with copilot as one example, and you have organizations who are already into agent building and are really excited about Microsoft's Agent 360 feature coming out.

Jeremy H.:

Yeah. A lot of different, um, announcements we heard from the Microsoft side, they also have the anthropic models now available. Um, so a, a huge amount of opportunity for builders. And I think what was interesting when we talked to everyone about ai, especially for the, the leaders, it wasn't about models. It wasn't about, you know, what's the latest benchmark it came back to what is your use case? Um, think about your organizational culture and what's the outcome you're trying to achieve? And that was the number one principle we heard again and again, um, when talking to people about how do you get beyond the hype and into the how.

Jillian:

Yeah. And as I said, we talked to a lot of different leaders and I think our very own CMO Hilary Kerner set this up really nicely. Let's take a listen.

Hilary Kerner:

So the thing that I hear the most when I'm talking to other leaders is that

Hilary:

The results that they're getting don't live up to the hype. So the, the promise of AI is so enormous and so incredible, and they're expecting these results to be on par with that promise, and they're just not. And I think that the reason that people aren't seeing big enterprise transformative results is that they're not doing big enterprise transformations. They're doing small projects in different places and their people, each individual person is maybe getting a little bit more productive or maybe they're eking out a little bit of cost savings. But the real value, I mean, that real promise is gonna come when you have systematized your AI deployment when it is integrated in your enterprise systems. And anybody who has a tech stack knows that that is so much easier said than done because your tech stack is not a brand new beautiful off the shelf thing that you bought yesterday.

Hilary:

It has been acquired over decades. It is in various states of modernization. Your data is sitting in different places. Maybe you have a cloud strategy, maybe you had a data modernization strategy. Hopefully you've got your security strategy intact. But, um, the fact is the, the vast majority of companies and leaders that I talk to are, are grappling with tech debt of some form or another. And in order for AI to be really, really meaningful, it's like anything else, it's gotta work with your enterprise systems. And that is not a flip the switch off the shelf kind of deployment. And I think that that's why there's so much, um, it's not disappointment. It's just like a reality check. You know, you kind of come crashing down to earth. And that's in the cases where they actually got it off the ground and the the deployment was actually successful. We, I talked to a whole other host of leaders who have barriers to even getting their pilots off the ground because of things like security issues, data issues or skills issues are a really big one that we see a lot, you know, workforce change and modernization, um, that needs to happen.

Jillian:

Hilary hit on so many interesting obstacles that I think people tend to overlook. I love how she mentions that some organizations are struggling with pilots because of things like security or data. We hear that all the time. And I think one of the biggest lessons that, you know, leaders can take away from this is understanding before you start an AI initiative who needs to be with you at the table from the very start. Yep. And we actually got really great insight about this from our chief information security officer, Jeremy Nelson

Jeremy Nelson:

InfoSec needs to be invited to the team. Yep. You need to have business people, like people who genuinely understand, like, what are the things that make this business flow? Like what, what are, what are our objectives as an organization and how do we achieve those who are the primary consumers? But just understanding the dynamics of how the business functions. So you need representation from the business in those, in those early conversations. You need your data team along with you. Uh, they and definitely don't ever forget about infrastructure. Right? At the end of the day, we're talking about data, data needs to move from point A to point B. You need to make sure that it's being done as a reliable and efficient way possible. And so that's your infrastructure team. A lot of folks will often forget about infrastructure and sometimes we're not necessarily talking about public models.

Jeremy N.:

We're not even talking about, um, private cloud-based models. We're starting to see more and more where these models are gravitating towards on-prem, whether that's edge types of computing system or edge type AI solutions that are coming out to even running on individual laptops. And in order to make that happen, your infrastructure, your devices team, they need to be a part of that conversation. So really representation across, you know, various different business stakeholders, InfoSec team, um, you wanna have data representation, uh, and you wanna have your traditional infrastructure team, that's, that's who's in the family truck store with me at least.

Jeremy H.:

So I love Jeremy talking about the team you need to make AI successful. Hillary talked about the barriers she saw and then, you know, hearing everyone from the business users to the infrastructure team to InfoSec. But what's really missing still is that AI literacy, right? You need everyone to be on the same page in terms of how to think about AI from a business standpoint down to the technical. And Mike Morgan, who's our senior vice president of apac, really touched on how do we think about skills? How do we think about enablement and how do you build that culture of AI literacy? I'm curious if you could talk about, you know, the barriers to AI adoption. Yeah. As companies go from experimentation to actually driving impact. What's the one thing that's really holding them back right now?

Mike Morgan:

Uh, look, it's, um, it's interesting because I sort of think back to, I think we're at the same point now as we were in the early days of cloud, when there's a lot of demos, there's a lot of technology, uh, there's a lot of people playing with, with tools and the, the heavy lifting's not quite as sexy, but it's where it becomes substantial. So it's the downstream infrastructure pieces, it's the integration, and most of all it's the, the people implementation. So it's change management, it's helping people to work in different ways. That's what makes things stick. And that's, uh, that's the piece that we're focused on at the moment.

Jeremy H.:

I wanna double click into the people aspect a bit more. So we talk about AI literacy a lot, right? And how important that is. What would you say is the most effective way to drive that in your organization? Yeah. And also on the flip side of that, what's sort of the worst way that you don't want to drive that

Mike Morgan:

Air literacy? I might start the worst way first because I see this in our organization even internally, that we have people at different levels of maturity and comfort with AI and the opportunity in front of them. And those at the lowest end are, are kind of apprehensive. They have a fear that they're falling behind, and their natural instinct is to ask for training programs for, for sort of academic support. And, you know, this world's moving way too fast for that, that, so by the time we sit down, we find a provider, we have a course, by the time the course is developed, it's probably 60% out of date. Um, so it's not that kind of a learning opportunity. It really is, uh, uh, creating safe spaces. And I like to think of it as being sandboxes where people feel free to be messy. And that creates the environment where people can experiment and make mistakes and just get familiar with the tooling to understand what's possible in a, in a low risk, low pressure environment. And then it sticks all by itself.

Jeremy H.:

So it sounds like you're having a lot of different employees experiment, not just Yeah. Technical folks, but what does it mean for more of a business user who's using Gen AI and what it can do for them?

Mike Morgan:

Yeah, it's a funny, it's a funny thing as I was reflecting on this, uh, earlier. I think the people who are leaning in furthest fastest are actually leaders and executives. Um, so I would, I would say I'm one of the, personally one of the most avid and excited users of ai because in my environment, I'm not, I'm not having to disrupt core systems to get things started. I'm able to do things that are meaningful right from the get go, because a lot of my day involves information planning, scheduling meetings, and those sorts of areas. You can get big boosts out of AI without too much effort and they're very meaningful. Uh, the thing I like about that is that I think when leaders visibly display their willingness to change behaviors to get advantage, and they do it in a messy way, it kind of creates a halo effect that people kind of inherit that freedom to be messy and they see their leaders doing it and they start to do it themselves.

Jeremy H.:

So you shouldn't feel you have to get it perfect right out the gate. You can be

Mike Morgan:

Experi. No, I think perfect experiment is definitely the enemy of good here. And uh, and that is something that holds people back, is that like most technology people that are, that are coming to it for the first time are almost afraid of, of not doing things the right way. So in, in the, the fear of that they, they kind of hold back from experimenting and being free to, to just mess around and, and see what's possible.

Jillian:

I like Mike's little humble brag in there that he's been an early adopter and also that he's just giving teams permission to get a little messy and experiment and even fail at times. 'cause we're, honestly, we're all learning this no matter where you are in your journey, everyone's figuring this out.

Jeremy H.:

Yep. And experimentation's so key. We've heard that again, again, you just have to get out there and start using it. And I love what he said about how execs aren't tethered to some of the same processes and workflows. So there's a bit of freedom to just experiment mm-hmm . Which I think is encouraging to hear from a leader. 'cause often, um, they might not be super AI literate, so the fact that they're diving in and are encouraging that tops down as well as their employees are working on the bottoms up use cases as well. Yeah.

Jillian:

I was particularly interested to find out how that adoption is going on at Microsoft, because obviously Microsoft is driving the ai, they're, they're releasing co-pilots. So you know, that internal adoption has to happen and it has to happen at a speed that is, you know, faster than the market, which is a really hard thing to keep up with. Um, so it was really great to actually sit down with one of the leaders from Microsoft, Julie Sanford, and she gave us kind of a behind the scenes look at how Microsoft has performed as Client Zero.

Julie Sanford:

What we have learned through this process of driving wall to wall coverage for copilot, like we do believe copilot is the next cloud socket to go win. Whether that's a free, free to paid, um, free to agent securing that environment is just such an amazing monetization opportunity for our partners, regardless of what your business model is and the learning that we had around the importance of being customer zero, that was an amazing set of learnings, like what it takes to actually adopt AI internally. And it's, that's, it's not for the hype. It's you've gotta have champions inside the organization identifying business processes that could be simplified. Um, where can we take the, like the monotony out of somebody's role that then frees up their ability to go and either drive revenue, look at new markets, be more creative in their environment. So that that concept of customer zero was so important for us to invest the first three months of this journey with our, our channel partners. And those, the ones that have adopted that internally, like you all have, um, the, the amount of value you're delivering to your customers is exponential because your sellers are talking from firsthand experience. And so, um, I would say the understanding the use case being your own customer, zero, um, is, is what is gonna turn this from hype into real business value.

Jillian:

Yeah, you're absolutely right. And AI has been a huge part of our journey and insight. And I think one of our biggest takeaways is understanding that the technology is only half the battle. Totally. Change manage. It really is change management. The change management upskilling. Yes. How specifically is Microsoft helping your teams with that hurdle of upskilling and change management? Yeah.

Julie Sanford:

Um, huge investment just in our own internal readiness. Um, and so we don't take that for granted. And it's really nice the way our skilling actually works for ourselves, for our customers and our partners. It's all the same. So, um, just a massive investment in that we were, um, challenged as business leaders to come up with our top 10 scenarios that we believed AI could help us solve. And that was a real paradigm shift for us because we have typically, and I'm sure you do the same thing where, um, you write these long business requirement documents, they go into engineering, engineering comes back with a cost around what it's gonna take to actually deliver that, um, business outcome. This was a reversal because it was, it put the accountability on the business leader, not it, and not just engineering. I shouldn't, like, it's more of a, a collaboration.

Julie Sanford:

And that actually forced us to get better faster because, you know, it's not like I wanna apply technology to this problem. It gave us the freedom to reimagine what we wanna accomplish within said projects said area said business model, and have to understand how AI can actually help either reshape that business process or help us have a better engagement with our customers, which for me is our partners. And so it really, that was, that was probably the most beneficial thing we did as a company because again, it, you've got business leaders now having to be accountable for understanding how AI can help improve the mission that they're on. And that was a game changer for us.

Jillian:

Yeah, I like that. What's been like your biggest lesson or takeaway through this experience? Something that maybe you're sharing with business leaders who are asking the question of like, we know we have to adopt ai, but it feels very daunting figuring out how to do it. Yeah. What would you tell them?

Julie Sanford:

Two things don't under underestimate the cultural investment you have to make along the change management that you were just, um, talking about a moment ago, depending on where you are in the world too, there's a, there's a different level of, um, comfort or, or Yeah, I would say comfort around what does this mean? And, you know, we've had some conversations around, well, we don't know if we wanna go all in because we want to, you know, we don't, we wanna protect jobs or we don't want jobs to go away. I mean, one very specific example, we were starting to talk about Agent Riley, which is a agentic sales agent that we use internally to help, um, our prospecting and improve the quality of opportunities and getting those to our sales, our sales leaders. And you know, every time we engage in a conversation like that and there's any sort of concern that you're picking up in the room around, well, I don't know if we wanna do that.

Julie Sanford:

Um, I think what was freeing, the freeing conversation we had is nobody's not experimenting right now. Whatever platform they're using, every employee in every company around the world is using some form of ai mm-hmm . And so flipping it from a fear base to an empowered, like an empowerment conversation, that was when we started seeing eyes go, okay, let's lean into it. 'cause then we can actually be on the frontier part of it and define how that can help us rather than wait for something to happen to us. So I think that was a, a really good in the moment learning, um, that we had. Um, so that's why I would say change and, and cultural, those two things. And then just the sheer importance of the investment and skilling. I can't hit that one hard enough. Like, and again, not just on the technical side, but the change management, the adoption, and then also the sales aspect of it. How are you positioning the value of AI so customers can see the ROI? 'cause we've talked a lot about do we need a thousand use cases that we're gonna go and, and, and work on? No, there's probably the first five that really matter that are gonna pay off in the first 12 months. That was a learning moment for us to go through with both customers and partners.

Jeremy H.:

I love Julie's focus on this customer zero notion and how you have to build the belief internally before you can really do that amongst your, your customers, your constituents. And you know, it was also interesting to hear her talk about how the leadership got together and identified those 10 business use cases. Right? So again, going back to, you need to start with those use cases that are most relevant to you. She talked about culture as one of the most important factors, and that's huge coming from a company like Microsoft as a huge, uh, technology provider, they're an AI leader. It goes back to those fundamentals. AI matters, but in some ways these other factors matter a little bit more.

Jillian:

Yeah, I agree. And I think getting that buy-in from the start is also really important. When I think back to what Hillary was talking about with just a lot of the frustration and sort of the, the expectations not quite being met because the hype has been so big. And the only way I think to really understand the actual tangible potential of AI is to be hands on and to discover the use cases that are gonna impact your day to day, not just the overall business impact. Um, but I think once we get into that, you really start to see that there's tangible potential out there. And Baja gives us some really good use cases of exactly that.

Bijah:

I think that the capability that AI gives organizations and non-technical people mm-hmm . To make a difference in what they're doing without having to rely on an IT team is not hyped enough. Right. The idea that we can get people all across our business operating more efficiently without having to go and get a ton of money to run a big initiative and depend on it and all the things that go with that, but actually getting that in the hands of those folks and helping them to make their day-to-day life easier and get operating leverage Yeah. Is huge.

Jillian:

Do you have an actual tangible use case of that? An example?

Bijah:

Yeah. So we've got a ton of use cases, insight. The thing I love about Insight is that we've been doing AI related work for years, right? AI's on everyone's lips right now, it's all the hype, all the buzz, but we've got years worth of stories and data, machine learning, ai. I think one of the places that I've been really impressed with that, um, we have a company that we work really closely with that is an oil and gas refining and exploration company. And we've helped them to establish a capability to do exactly that, which is empower their people to go out and produce agents, produce machine learning models, whatever it may be for these specific problems in their area. Mm-hmm . And what it does for us is insight is great because it gets us connected to all these different parts of their business, right? Where we can actually start to help them solve problems just beyond just it. And to me that's a lot of the power of AI is it's not just things for it, it's actually actually getting out into the business and solving the problems that all of these business areas face, especially the revenue generating customer facing sides of the business, right. Which we all know is the bread and butter of any company. So I think that's, you know, an example of that and I can be more specific if you like, but yeah,

Jillian:

I mean, I think a lot of the more the back office use cases are things that we really see value from. Yeah. Sort the, the unglamorous things that actually make a huge difference.

Bijah:

And they do, by the way. Yeah.

Jillian:

Give us just like one concrete example of

Bijah:

That. Yeah. So a really good back office one, so we work with a major airline company, uh, you would recognize that it's one of just a handful of, of really big brands. Um, and what we actually help them to do using machine learning and AI is to come up with a staffing optimization solution. So they have issues where airline gets shut down, the weather's terrible, they've got hundreds of thousands of bags that they have to get distributed, get to the right people. And as you know, if they lose them, they're paying claims, they're, you know, they're losing a lot of money when that happens. Yeah. So having a way to optimize their staffing if they know, hey, we're gonna have a huge storm coming, or we've got a, a crisis that's gonna shut us down and give us an influx of bags sitting around and to optimize their staffing ahead of it and make sure they've got the right people to get all of that handled and, and not lose money having to pay for all those bags. I think that's a really great example of something that, to use your word unglamorous, right? , it's not something we're seeing, it's not flashing on a screen anywhere, but it makes a pretty big difference to people when they're traveling and their bag doesn't get lost. Absolutely. And to the bottom line of the airline company when they're not having to reimburse people for, you know, hundreds of dollars of lost clothing and, you know, prize possessions and everything else.

Jeremy H.:

You know, when I think about AI use cases, I think about, you know, things like, uh, call center transformation or fraud detection. Yep. A lot of the ones that we hear day to day. But it was really interesting talking to Alex Mooney from Logitech, and he really made me rethink, you know, the role of the device in an AI context. You know, we've heard about IPCs and Logitech obviously makes, um, things like mice and keyboards and it was interesting hearing about all these sort of micro moments in our day. We all do the, you know, the copy paste or we all have, we can almost memorize the click sequence to do something in an application, but he really impact how can AI help make those interactions a lot more simple.

Alex Mooney:

Every time that you have to go and interact with your co-pilot assistant that's helping you with your workday, you're having to really do task switching. You're switching from the tool that you're in to the sidebar or to a separate portal. If you've got a document someone just emailed you and you need to ask questions about how relevant that document is, you're gonna have to move that document or do something with it. So being able to just take these reproducible steps and call them with a single click of a button right at your fingertips, I think is a really nice way to bring AI into your actual human workflow, the reality of your human workflow. So one of the things I do do is the forward button on my mouse, on my MX four is actually written with a, uh, with a, a smart action recipe and options plus that high takes my highlighted text, it auto automatically invokes copilot pastes, it adds a little bit of a custom prompting that I like, that gives it some context around what I do, what kind of insights I'm looking for, and then submits the prompt to get an answer and copies the response

Jeremy H.:

All with just one click all

Alex:

Of that with a single click. Wow. And so if you sent me a chat message and I get it in teams and I'm not sure how to answer, I really don't have time, I can highlight, click one button, get a response, and resend it back to you in a matter of seconds. And that keeps me from task switching. So Microsoft's, uh, work Trends Index had a, a really interesting update that was talked about the infinite workday. And what we found as a part of that was that users are being interrupted in the course of their day approximately every two minutes, so around 270 ish times a day. And so all of these interruptions are causing users to have to like go to work and then break their focus flow and go to work and break their focus flow. It may seem like a tiny thing that you're improving three or four seconds here or there, but in the context of this kind of ever increasing workday, those really add up and it means you're getting more done with that two minutes of focus before that next interruption comes in.

Jillian:

You know, we talk all the time about AI changing how we work, but you don't really think about AI impacting the technology devices that we're using. So that was such an interesting use case to hear from him with the mouse click.

Jeremy H.:

Yeah, it's, it's something I wouldn't have thought of, but it makes sense when you add up again, all those little interactions. And if you can simplify that and you shave a few seconds here and there and you times that, you know, hundreds of times a day, it really does make a difference in your workflow. Now we can't talk about hype to how, without talking about agents, which was a huge focus at this conference, Microsoft rolled out a number of, uh, new updates to agent orchestration and capabilities and co-pilot. And so I asked Sonny Wang, who's our, um, agentic AI lead in Canada breakdown, what is, what does it actually mean to be agentic? What are the different types of agents? And I loved her response on this

Sunny Wang:

From a client perspective. There's like three different tiers of AI in terms of the power of it. And so the first thing is think of like retrieval, augmented generation rag. This is like your chat bot. This is your chacha bt you ask a question, it sort of goes through, its, its backend data and then it comes out with an answer that has been around for quite a few years now. And think of this as like your co-pilot, for instance. Um, a lot of clients we're seeing using these tools say, wow, it's so great for personal productivity, for finding things. So that's actually very real and and in play today. The second thing we also saw a lot of this conference is like deep model, deep reasoning. So think of like more advanced models that think like an analyst or think like a master student. And they're outputting a lot of really good work product that a person can then go and just edit or take a look at or understand.

Sunny:

And deep reasoning is currently embedded in pretty much all of the big AI providers like Microsoft has, researcher, Gemini, et cetera. Um, so that's another level and that's also real and also being used again, tons of, tons of value. The third element is really around the whole autonomous piece. And that was a huge theme of this conference. Now it's emerging, right? We've had RPA for a long time, but I would say the whole autonomous agents is like RPA on steroids. That's how I've heard other people describe it. And honestly, in Canada, this is still very nascent. So clients are coming, they're super excited, they can see how it can now go to workflows, maybe even whole mirroring whole departments, but where it's actually coming into play, I think is still emerging. And so that's the work that we're doing now is guiding clients through that process, but also building agents and helping them set up different workflows within their environment too. What's

Jeremy H.:

One thing you're tired of explaining to people about agents that you, you, you just have to kind of reinforce over and over again?

Sunny:

I think it's just this idea of like humans as operators is, is super important. Now of course the space is evolving so fast and I'm not an expert by any means like a frontier lab expert. So maybe in a year or two years, humans will no longer need to be operators. But even with like looking at the agents that Microsoft has built, there is a hand back to the human when it comes to more pivotal steps or sometimes it fails or it doesn't do everything perfectly. And again, you only know that if you've really played with it and understood its limits. And so whenever a client comes to me with, with good reason and says, Hey, I'm really worried, I would say, have you tested it against your workflow? Have you conducted an evaluation and understood where it may or may not fall short? And often it's not as perfect as a person or it takes things like 80% of the way. Now that's still valuable, but I think it's just like grounding everybody in that knowledge that humans are still operators. It's like a copilot or a support that's still super critical, at least in this current state. As we record today,

Jillian:

We had some really interesting conversations, everything from the hype to the how, the use cases, the barriers getting in their way. I don't know about you, but I think one of the biggest takeaways I have as a user is just the reality of this actually becoming a tangible thing. AI is coming to the workplace in really good positive ways. When we hear the use cases about, you know, making some of the tedious tasks easier, less disruption in our workflow, having agents that work with us to do things, it makes me very optimistic about the future of the workforce.

Jeremy H.:

Yeah, and I think we also heard a, a nice roadmap of sorts, right? So Hillary started talking about the need for integration and the need to think about all your different systems. We heard, um, you know, from Mike and, and Julie in terms of how do you drive that culture change, how do you drive that skill building? Heard a lot of different great use cases as well, whether that is, you know, those sort of micro moments and using, you know, say a copilot integration with a device to make, have less friction there all the way to a larger transformation with an airline. So you definitely see that there's one, this is very real today and that there is a path forward and there is a how through that hype.

Jillian:

Yeah, absolutely. And the good news is it doesn't matter where you are in the journey, we're all somewhere along that road and you don't have to take it alone. And so we, thankfully we're able to talk to you a couple more experts on those topics. So I think that'll be our next episode.

Jeremy H.:

That's right. We're gonna dive into risk and governance, um, all those important aspects that once you're up and running, you need to make sure you're doing it securely, you're doing it responsibly. So looking forward to hearing more.

Speaker 10:

Thanks for listening to this episode of Insight on if today's conversation spark an idea or raise the challenge you're facing, head to insight.com. You'll find the resources, case studies, and real world solutions to help you lead with clarity. If you've found this episode to be helpful, be sure to follow insight on, leave a review and share it with a colleague. It's how we grow the conversation and help more leaders make better tech decisions. Discover more@insight.com. The views and opinions expressed in this podcast are of those of the host and the guests, and do not necessarily reflect on the official policy or position of insight or its affiliate. This content is for informational purposes only, should not be considered as professional or legal advice.