Trends in Business Transformation

Marc LeBlanc: When we look at what's emerging from a cloud perspective, some of the technologies that are available today that weren't available before, and we see people get onstage and we talk, talk through what's, what's possible, and it all looks great, and we say, "Yes, I want all of that!" But what's happening is it's being presented in a really greenfield perspective.

So there's nothing, there's no legacy, there's no old applications, there's no existing processes. They're saying this is what it looks like in the cloud. Looks great. It becomes really complex when you take some of that technology or processes and you say, "Guess what? It's got toco-exist with this application that's been running for, five to ten years."

Mike Reeves: Do you have any sort of a strategy or a plan that you have under consideration for how you want to look at bringing AI into the organization?

There's these perceptions of roadblocks you're going to have to deal with around the people side. And a lot of the FUD that's out there: the fear, uncertainty, and doubt around what it means to people on their jobs. And how are they going to interact or work with this tooling and these technologies?

And what does it mean to their job? Is their job at risk? Does it augment their job? All those types of things. The company readiness elements become pretty important.

Marc LeBlanc: How do you secure earlier? And it really, if you hear the phrase shift left, that's what we're talking about. We're talking about how do you take your security posture and putting it way out front before a developer writes a line of code before software shift, before you build a data center, before you go to cloud, what is your position? What are the security controls you're putting in place?

Mike Reeves: This is Solving for Change, the podcast where you'll hear stories from business leaders and technology industry experts about how they executed bold business transformations in response to shifts in the market or advances in technology.

In every episode, we'll explore real-world strategies and technologies that fuel successful evolution. We're your hosts, Marc LeBlanc andMike Reeves.

Welcome to Solving for Change. I'm Mike Reeves, President ofMOBIA Technology Innovations.

Marc LeBlanc: And I'mMarc LeBlanc, Director of the Office of CTO at MOBIA. We're your co-hosts and we're excited to launch this podcast and welcome all of our listeners. If you listened to our trailer, you probably already know that in each episode, we'll sit down with a business leader or industry expert to hear their stories of business transformation.

Mike Reeves: In each episode except this one, that is.

Marc LeBlanc: That's right. In this episode, we're going to do something a little different. We're going to set the stage for future discussions by exploring the major trends that have shaped the business landscape over the past several years and how they've fueled business transformation.

Mike Reeves: Thank you for joining us on Solving for Change. Let's dive in.

Over the course of the past few years, market shifts, advances in technology, and major global events have been the catalysts for major transformation in organizations around the globe. In fact, these events had such a profound impact on the rate of change that they've replaced the focus of digital transformation, ushering in the age of digital acceleration.

We've seen companies respond to major market and world events by executing transformations that, in the past, would have taken them many years in just a matter of months. But digital acceleration is more than just a greater emphasis on the speed of transformation. It also emphasizes using a continuous, holistic approach to leveraging technology and updating core business practices.

As we've watched this paradigm shift unfold, we've seen a few interesting changes in the way organizations are preparing themselves to stay competitive. If you look at over the past year or two, we've seen a lot aroundMicrosoft and Microsoft's investment in AI, and particularly around Copilot. And over the last number of months, Microsoft's made a significant investment in rolling out and making Copilot available to organizations.

And if you look at some of the things that are starting to evolve as a result of that, when you get that Copilot included in your subscription, is there's some recent research that's been published around the adoption of Copilot in organizations. And right now, on average, it looks like it's taking approximately 11 weeks for companies to start to stand up and get some value and start to weave Copilot into the day-to-day use of trying to leverage and embrace the value that you can get from AI and Copilot.

So that's been an interesting anecdote that we're starting to see take shape and I can certainly talk about that from our organization's perspective and our experience. And some other research or some other interesting things that are evolving and we're going to get into some more discussion around this as we get into today's episode, but also future ones around AI.

There's some research Juniper Research published late in2023/24 the impact that chatbots are starting to have on organizations and so we'll talk about that a bit more and Marc's going to have a few anecdotes on that as we get in a little later on into this session we're going to we're going to talk about AI today.

And we're talking about the great value that the chatbots are starting to provide, basically taking on the tasks of about 2. 5 billion hours of work around the globe annually leveraging chatbots. So some great anecdotes, some great value that's starting to materialize there. And we've certainly seen the results of digital acceleration, but what's equally interesting is the trends that are starting to materialize.

We're going to dive into a couple trends today. And the first one we're going to take a look at is cloud services. Everyone's been talking about cloud for a number of years, and there's a few themes we want to hit on today in this first this first section, and going to ask Marc a couple questions here and get some insights and some feedback.

He's been a cloud practitioner for a number of years, and the first thing I want to talk about is you've got this notion that we've been talking about it around hybrid. So, on-premise services and infrastructure and running your applications in business, and then there's been this push, certainly aggressive push over the last number of years, for folks to start to adopt or organizations to start to adopt cloud.And so when you bring those two and those dualities together, that's considered hybrid cloud, hybrid IT. And how you will set up and organize your company and your applications and your data has created a lot more complexity for people when you're trying to manage all those things.

And so, hybrid's become really, really big in the last few years as more companies try and drive that balance between what's good to have on-prem and what's in-cloud, and we're starting to see more trends materialize around that. And most recently, over the past year and a half, it's more of a trend around starting to migrate more applications back on-prem, and I'm going to pause there for a sec, Marc, because I know you spend a lot of time in this space, and I'd love to get some of your perspective in view in terms of that, that shift, and in terms of why and what are the reasons that companies are starting to now look at still maintaining a hybrid strategy, but they're really looking at moving more applications and data back on-prem.

Marc LeBlanc: Yeah, I think it's really interesting and it definitely is a trend that continues to shift and morph depending on which company you're talking with and what business persona you're speaking with. I think a lot of it stems to when clouds first started to become big, there was this big push, people thought it was going to be cheap, it was easy, it was really quick, you kind of just slap down a credit card and away you go, there you have a workload in the cloud.

And then, as we peel back, there's market, there's economic pressures happening around us, we're kind of looking and saying, "That's a really expensive way to run an app if we just moved it from our traditional data center and put it in the cloud." And there definitely has been a lot of that. We refer to that as lift and shift where you take up an existing infrastructure or an application and you like to like move it to an Azure or a Google Cloud or an AWS. And you watch your OpEx just kind of skyrocket because you didn't put the right guardrails around that.

And so companies are now saying, "Well, whoa, wait a second, maybe maybe Cloud isn't the right strategy. Let's reevaluate." And although ultimately it may be more costly, the quick recoil action is,"Let's put it back at our own data center where we at least understand what that OpEx looked like."

There's a lot of pressure there.

Mike Reeves: Wouldn't mind digging a little bit more there because you just touched on something I think that's super important and it's been one of the more material or significant drivers as to, this effort toward moving more applications back or repatriating them back onto the premise.

You know, it's FinOps or financial operations. You're starting to hear that term a lot more. There's designations out there now that the folks can go get to become a FinOps practitioner. And I think that's become a very strategic and very valuable role. And to your point, you mentioned the term guardrails and I think it's interesting to see there was always this discussion around guardrails just in terms of what do you want to move and, putting things out in the cloud and then the concept of security and security guardrails.

There's also financial guardrails, and so how you make decisions around the financial construct that you're trying to achieve from a business perspective or a business goal outcome perspective. And maybe spend a couple of minutes talking about that, because I think that's one of the things that's really become a pressure point and led to a lot of this on-prem movement.

Marc LeBlanc: When you think of cloud and you think of your own internal data center, your own internal data center, there's a finite amount of compute, there's a finite amount of OpEx you're going to spend just by running inside of those four walls. When you go to the cloud, and sure, there is an actual upper limit, but from what you're probably able to generate, there really isn't.

So you can say, "Hey, just scale up almost infinitely." Well, when your infrastructure scales like that, so does theOpEx behind it. And so, out of that, it became apparent that there's a need to,"How do I predict what that looks like from a financial perspective? What are some of the tooling that I can put in place? What is some of the visibilityI can get into place for that real time and that predictability?"

That wasn't a conversation that was really happening five or six years ago. There was a notion of it. There's always been a notion of it from even inside the enterprise. What does that chargeback model look like when an infrastructure ops team is running something developers come up with? There's always been that idea of, "How do I really get down to the level being consumed by some workload and how do I position that within the right business unit?" But there's a lot more maturity, there has to be, just because there is that more untethered availability in the cloud.

And then as we go to that hybrid multi cloud, your cost is actually spanning both physical and virtual planes. So, how do you bring that all back and get that single pane of glass that gives you visibility? That's the problem that we're trying to solve.

Mike Reeves: Yeah.Some, some great points in there.

And I think the other one is a, and you touched, you touched on this and maybe you could kind of unpack it a little more and that is the decision making. Cause with a lot of. Companies are doing kind of ties into the overall planning process, but also just into the financial construct and the costs associated to moving that workload to the cloud.

And that's the planning on the upfront. So a lot of companies are just taking applications and infrastructure, and they're just doing a pure lift and shift and migrating that over, and they're not really thinking about, do I need to take all of this older architecture? And the decision making as to how we designed and built the platform originally and push that out into the cloud where there's a lot more efficiencies and things you can consider and do differently when you move to cloud that hopefully you're kind of planning for and thinking for on the front end that can make that cloud experience and the cost associated to that much different and much better.

Marc LeBlanc: There's a few things here, let's try to unpack it. When we look at what's emerging from a cloud perspective, some of the technologies that are available today that weren't available before, and we see people get on stage and we talk, talk through what's possible, and it all looks great! And we say, "Yes, I want all of that." But what's happening is it's being presented in a really greenfield perspective. So there's nothing, there's no legacy, there's no old applications, there's no existing processes. They're saying, "This is what it looks like in the cloud. Looks great!" It becomes really complex when you take some of that technology or processes and you say, "Guess what?It's got to co-exist with this application that's been running for 5 to 10 years." And those are some of the challenges we're butting up against is that really idealistic, fully cloud native, "It looks great, I want that." But how do you integrate that with something that isn't quite that clean?

Mike Reeves: Great, great points to make there.

And I think we'll tie off on this one there today, but I think in one of the future episodes, we're going to spend a fair bit of time talking about maybe some planning and some of the front end, front end upfront effort that you want to do in terms of making those decisions and how you want to go look at your hybrid cloud or multi cloud strategy at this point.

And then I think we'll spend a bunch more time on top of that, maybe in that episode or maybe it's a specific one, because you could certainly give it one, and that's just around FinOps and that whole emerging space of thought and thought leadership that's starting to take shape there that you're working on with a lot of our team.

The next trend I'd like to dig into and talk a little bit about is AI. Pretty hot topic, topical for everyone in every industry and almost in every walk of life these days. There's something that somebody's talking about or sharing some anecdotes around AI. Some of the trends we've been working with at MOBIA for probably two and a half, three years, I'd say fairly intensively around AI before it kind of got, I'll say, in vogue are very trendy right now.

And some of the things that I think that have become very topical, particularly in the last year, yearand a bit. There's some interesting consulting opportunities to go in and really help companies around this and that's around company readiness for AI because everyone's got these anecdotes or these use cases or examples or maybe they bought something and brought it in that they've gotten some benefit or some value from an AI perspective inside their organization. I'll say it was very tactical and very quick and it was a good anecdote of a good experience.

But if you look at the value of what you can do with AI–and I think the maturity and how quickly things are evolving around large language models and how industry and business problem-specific they're getting with these large language models–is companies starting to understand and how they need to build a plan to be able to look at what is the best approach to AI and how can I bring it into my organization. And a couple of,I'll say key drivers or things to look at I think are super important. It's starting to emerge as an interesting topic or a topical area of discussion, and that is company readiness.

And if you look at that, do you have any sort of a strategy ora plan that you have under consideration for how you want to look at bringing AI into the organization? There's these perceptions or roadblocks that you're going to have to deal with around the people side. And a lot of the FUD that's out there: the fear, uncertainty, and doubt around what it means to people on their jobs. And how are they going to interact or work with this tooling and these technologies and what does it mean to their job? Is their job at risk? Does it augment their job? All those types of things.

So, the company readiness elements become pretty important. And so maybe if I can just pause there and I'll give you an opportunity, cause that's–we're using AI as a use case–but that's always been a challenge when you start to implement change or new ideas or new technology into an organization.

Marc LeBlanc: There's a lot to talk through on this topic: AI. You can't hardly have any conversation in any industry that doesn't touch on AI, let alone tech in and of itself. I think as a technologist, I'm always sort of probing and seeing what's possible. And it's interesting with AI and the misconceptions or the misbeliefs of what it's intended for.

There definitely is a conversation around, "What does this mean for job stability? What does this mean for what my day-to-day is going to look like?" Just like as if you introduce any new tool or new process, people have questions, they have concerns. I think that it's going to be interesting because there's two different avenues that we're seeing emerge.

There's one on the commercial consumption of AI technologies like Copilot, like you mentioned, there's ChatGPT, there's a myriad of other ones available. That's one element, and how do we bring in those tools to make use of it from a productivity perspective, how do businesses really tap into some capabilities they weren't aware of?

And then there's the more customized, "What am I going to get AI to do for my specific business?" I think it was in December thatMcDonald's announced a partnership with Google Cloud. They're looking to bring some of the AI capabilities to all the restaurants specifically to their kiosks. So we're going to see more of that.

Companies are saying, "How do I run an AI workload on my business?"Whether that's on individual sites or if it's central to some sort of a datacenter? The readiness from a consumer perspective really is around culture and does your workforce, do they understand what you're trying to get out of it?

I'll even share one of my own very current explorations of what I can get, get AI to do. I'm taking a transcript from a presentation I gave back in February and I'd like to convert that recorded presentation into a blog. And I'm taking the transcript and I'm saying, "Hey," in this use case I'm using ChatGPT. And I'm saying,"Help me convert this to a blog." So I don't have to take time to reconvert that existing content into a different format. If that can save sometime, I think that's a great use case.

Mike Reeves: A couple of other things that maybe you can talk about is that you kind of get into the personal side and personal use case stuff, because I know you're spending a lot of time on this internally with our teams in the consulting group, and that's around starting to integrate AI into your job on a daily basis.

And if you look at app dev as an example, and you were looking at some frameworks and some different approaches over the past couple of weeks that you've been sharing with me and some other folks on the team. Maybe talk a couple minutes about you started to socialize that with a lot of our consulting and a lot of our team and that's, there's a couple different perspectives here.

One is the view of, we're trying to get our consultants to embrace it so they can enrich how they do their work. Hopefully speed up the time that they're doing their work and let them let AI do more mundane tasks for them and the lower value tasks, if you will, and let them focus on the higher value things. If you look at application development, and certainly elements of the app dev process that that our folks engage in, andI'll pause there and give you a minute to comment on that because you've been doing some neat stuff internally.

Marc LeBlanc: I think again, you're back to a bit of a culture conversation, and we touched onCopilot becoming an available product within your 365 subscription.

There's also the Copilot that plugs into your GitHub, your codebase. And we're seeing a lot of that emerge right now where there's theseAI-powered bots that can interact with your developers and the concern is:well, if I'm a junior developer, am I really learning if I'm getting that to do that?

And if I'm a senior developer, well, I probably know more than that chatbot. What we're really seeing emerge is, okay, if you're using a standard language, like maybe you're developing in Go or Python or whatever it is, you can really quickly get feedback from these bots, these AI-powered bots.

It says, give me a very standard function, and you get the very quick structure. It's much better use of your time to actually get that function to do what you need from your application as opposed to going andGoogling and referencing, finding out what that structure is. So you're saving a fair amount of time letting someone move quicker through the more meaningful pieces of that development workflow.

Getting people to embrace that, it's all true. You have to kind of normalize and talk about it, talk about what we're trying, give those use cases. Because otherwise people do have that, "Am I really developing? Am I really coding? Am I just kind of cheating here?"

Mike Reeves: Yeah, that's that's great insight.

I appreciate you sharing that and again, it's a big topic. We're not going to cover all the ground today, but AI is something we're going to spend a fair bit of time doing some deep dives on in coming episodes and there's a lot of different perspectives or views I think that we'll dig into.

One is, from our perspective as a company, it's, "How can we drive some operational efficiency inside the organization?" So the operational elements of AI and trying to get some advantage and leverage there.But also, with the consulting work that we do can we create, put some automation around that to help with driving some scale and efficiency and time and effort around the work that we're doing for customers?

And then you get into kind of the, the blending of how do you set the organization up as best as possible to build a plan to implement AI as successfully as possible as you take it through the organization. And so we'll dig into that more in some future episodes. Appreciate your insights there.

I think the last one we'll close on today and kind of trend three. We're trying to keep it to three trends today. Is going to be around cybersecurity and again, another big topic a lot of time we could spend on it, but we just want to kind of want to touch on a couple high-level things thereto kind of frame some stuff.

And if you really think about it, it ties into pretty much the other, the two themes of what we've already talked to you in pretty much a fair bit of detail. And that is around cloud and AI and security has to be woven through all those things. One of the things I wouldn't mind getting a little bit of insight on from you and from a security standpoint is posture.

People talk about posture, so maybe you could spend a minute, like give us a definition of posture. I know there's lots of depth you could put around that, but just from a high-level perspective. And we'll kind of start the discussion there if that's okay.

Marc LeBlanc: There's so much in there as well, like all these topics.

And I think, like you've said, they've all interwoven.

Probably where I start today, I don't worry so much about posture at a high level. I think you gotta go a little more practical because I think some of the traditional approaches, companies have a good handle on it today. If you think about the GRC, the threat risk assessments-type positioning, companies have been doing that for a longtime. Where it's becoming increasingly apparent is: how do you secure earlier?And really, if you hear the phrase shift left, that's what we're talking about.

We're talking about how do you take your security posture and put it way out front before a developer writes a line of code, before software shift, before you build a data center, before you go to cloud, what is your position? What are the security controls you're putting in place? Are you thinking about if a developer makes use of an open-source library?

How are you going to secure that and making sure that your supply chain is, is good? How are you going to make sure that the resources that are available to developers are secure and proper? So that whole notion of security shifting left, thinking of that earlier. That's a newer developing kind of idea. And you see practices that never existed before, like DevSecOps emerging, that never existed before. A lot of focus being put on that.

Mike Reeves: If you don't mind maybe a little bit more on shift left in terms of a definition, because you definitely hear people talking about it. Then maybe if you can dovetail that in to talk about shift right, as we talked about earlier with cloud and hybrid or multi cloud and this, this movement of back to on-prem. Because there's a duality there that you're trying to operate under.And if you could explain both of those, shift left, shift right, that would be,I think, very helpful.

Marc LeBlanc: Shift left, like I was saying is, before you even have a notion of an application, you kind of want to define what are those security controls you're going to care about.

Code shouldn't even exist, infrastructure shouldn't exist until you understand what those edges are. So, what was happening for a long time is, an application would be written, it would get shipped, it would be put into a staging environment, and it would be getting ready for production. And someone would say, "Hey, let's get security involved and find out what we need to do here to secure this thing."

We want the security conversation to be happening in the design phase. So, hear what the security concerns are upfront so that when we're coming up with an architecture or design or some sort of implementation idea, we already know what we're trying to address from a security perspective. So that's the idea of shifting left: way up front as part of the design phase.

The other half of that: it's great, we want companies to bethinking about this. We want them to be shifting that security posture left, doing all this upfront work. But, you can't ignore the fact that once it's running, there's still those traditional operational security measures you've got to put in place. So that's the idea of shifting right.

I think we were talking about earlier with cloud and the repatriation to our data centers. You're, you're seeing a lot of those traditional security vendors re-emerging. They didn't ever disappear, but you're hearing more about them. They're still relevant.

What's new today in this sort of back and forth from cloud to on-prem is, "Okay, now we have a really true hybrid environment."Even if a company says, I'm all in, I'm going back to running a workload entirely in my own data centers, you can almost guarantee that there's some element of a SaaS-based application that's integrated with their workloads, with their environment.

And so, you still can't rely on the four walls of your datacenter like we could 15, 10 years ago. So there's different concerns from a shift right perspective.

Mike Reeves: Just a couple of the quick things to talk about. And again, I know we'll go into this in much more detail, but you did mention DevSecOps. And that's very topical inside of our organization.

We're trying to do a lot to build more consulting and then develop more maturity around that in terms of building templates and models fora lot of code automation, co-creation around the integration and weaving together of DevSec and Ops. I just want to pause there for a second, maybe just again, back to let's have a definition of that.

And then maybe you can just kind of talk a little bit more about it at a high level, and I think that would really help folks in terms of starting to understand. Again, as I say, we're going to, we'll, we'll dig into this materially in a future episode.

Marc LeBlanc: I think maybe an example of what it looks like would help more than a definition.

You could ask 10 people and get 10 different definitions ofDevSecOps. So, when we think about DevOps, we're trying to marry up operations schemes with developer schemes, having them have a shared responsibility, shared set of goals. When we talk about DevSecOps, we're really introducing the idea of making sure security is part of that as well.

So, an example of that is making sure that we have security tooling that gives faster feedback to your development teams and maybe gives the operations team a notion of, "Hey, this is what's coming down the pipeline for you." There's no secret as we've kind of gone through these transformation journeys, we want less manual steps in this process of developing code to shipping and running it.

So, we're seeing tooling emerge that will automatically, as a developer ships their code, it gets into some sort of a code repository.There's some automation to do during integration testing. Security testing is part of that. And before it goes any further, that feedback is back with the developer. They can fix whatever it was that emerged and resubmit it, so that it gets out to production in a secure manner.

Mike Reeves: Just one other theme that ties into the entire discussion today, but security's probably been one of the areas where it's been felt the most and then certainly now, with AI coming so robustly forward, it's materialized there as well, and that's the skills.

The skill set and the people. There's these two different schools or there's a few different schools of thought in terms of: you've got people that are trying to grow and develop skills and they want to embrace new technologies and learn and grow from a security standpoint and as well as an AI standpoint, and then you've got this notion of there's so much need that companies have and the market and the labor force isn't there yet to be able to support that. Maybe just to pause a little bit and chat a little bit about the people-side and the skills-side.

And you can take that in a couple of different paths if you want. I'm kind of giving you a pretty wide swath. Because I know we do talk about the resourcing and the people side of this a lot, in terms of where can you get people, how do you develop people.

Marc LeBlanc: It's definitely an area that I've got a lot of opinions on.

I feel quite strongly about it. And, it's a bit of a paradox when you think about it. We see all the economic pressures happening in companies. We're seeing the some of the largest layoffs in tech that we've ever seen and yet you still hear companies talk about there's a shortage in labour. And the nuanced difference to understand there is they're talking about skilled labor, the right people with the right skills. And so, there's always a balancing act between: do you spend significant resources in finding the individuals that have these capabilities, or do you invest in these programs to build the staff up.

It's really tricky, because some of these things are emerging so fast and changing so fast. Kubernetes that seems to be everywhere today has only been around for nine years and really only being embraced or maybe the last five or six. So, you're not going to find a seasoned vet that has seen everything that's happening. It's still evolving day-to-day.

And so, companies need to be putting time into thinking: how do you bring someone in, quickly identifying and assessing this is where they are today and this is a path they're going to be successful on and building those blocks. Because, it's not immediately clear, as it may have been previously.

Mike Reeves: We'll dig into that a lot more in future episodes. So, I think we'll finish there in terms of some of the trends and things that we wanted to touch on today just to give everyone a flavour for what we're going to be spending our time on. But some stuff to think about for the future.

This is just a handful of the trends that we've seen fuel business transformation initiatives in the past few years. So much more to talk about here, lots of disruption going on with our customers and the various markets that they serve and the technology that they're trying to embrace and/or the technologies coming in and disrupting their business.

It's going to be our job over the next little while to, hopefully, elucidate people, inform people try and really hopefully, give people some valuable insights or some things to think about as they're kind of trying to figure out their journey with transformation inside of their organization.

A couple of trends that we're going to keep an eye on and maybe give some thought to, and you'll certainly hear about them from us in future episodes. There's a lot of spending as we talked about earlier in this episode. And there are other ways we can help with how people are planning and budgeting and spending money and doing more upfront work around the monetization piece and the spend piece of how you're looking at trying to bring technology into your organization.

And of course, the concept of doing less and not spending as much resource time or money, but trying to create and drive more value and in the process save money and have people spending physical time doing fewer things and having technology, hopefully, do more things. So that'll be certainly a topic we're going to spend a fair bit of time on.

And then we talked about AI readiness. Like, I think we're going to spend a fair bit of time talking about that one and it's really an area of excitement. And I think it's, it's something that we've got to spend more time really educating folks on. That upfront work in terms of how do you get the most value and plan as best as possible to introduce AI into your organization and/or trying to get some reins or some controls or some guardrails around it if you've already started down that journey and not quite happy with how that's moving.

As we just talked about security, so much to talk about there because security just, it spans everything that we talked through today and so much more. So, so keep an eye out for us to talk about some of those things and.

Accelerating the adoption of technology and fueling innovation, the past four years have been marked by seismic shifts in markets and disruptive technologies.

As the environment around them changes rapidly, organizations must remodel their businesses to remain competitive. Here are three key insights. We hope you'll walk away with about your business transformation in the age of digital acceleration.

Again, we're back to the idea of as resources become tighter, we're seeing interesting shifts in cloud adoption. And when we say that as we talked about earlier, it's the move back to on-prem and then planning to go to be in the cloud in the right way. Companies are going to be more mindful of how and what they move to the cloud and the costs associated with doing so.

A lot of key questions that we need to look at there. What applications are ready? Do you have an approach as to how you're going to look at what how you're going to move to the cloud. And how are you going to scale up and scale down and be able to get the best value of cloud when you actually embrace that in your multi-cloud strategy?

Culture is another big area. We're going to spend a fair bit of time on that as we move into future episodes, touched on it again today.

And again, there's security. So there's three core themes I think you'll see is as three legs on the stool that we're going to spend a lot of time talking about over the next little while.

And I'd just like to say, thank you for listening to solving for change. If you enjoyed this episode, leave us a rating and review on your favourite podcast service. Keep an eye out for our next episode as we explore automation with generative AI.

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