EP. 25 Can Green Tech and AI Coexist in Data Centers? with Mukesh Ranjan

00:01 - Mukesh Ranjan’s Career Journey

PJ Farmer

Well, welcome to Cloud Currents, the podcast that navigates the ever evolving landscape of cloud computing and its impact on modern business. I'm your host, PJ Farmer, vice president of product management at TierPoint. And today, I'm thrilled to have Mukesh Ranjan. He's the vice president at Everest Group, and he's joining us today. Mukesh leads Everest Group's cloud and infrastructure practice, providing critical insights to industry leaders and decision makers.

With over a decade of experience in IT and cloud services, Mukesh has a unique experience and perspective on the challenges and opportunities facing enterprises in their cloud adoption journeys. In today's episode, we'll explore the current state of cloud adoption, discuss strategies for maximizing cloud investments, and delve into emerging trends like AI integration and edge computing. Mukesh will also share his thoughts on the future of cloud computing and its potential to reshape business strategies. So, Mukesh, welcome to Cloud Currents.

Mukesh Ranjan

Thank you so much. Looking forward to the session.

PJ Farmer

Me too. So let's open it up. Uh, let's just start with your personal journey. Like, can you walk us through your career journey and what led you to specialize in cloud computing and infrastructure services?

Mukesh Ranjan

Sure. So, uh, you know, it's an interesting one. Right? I did not start with, um, a background in IT or, you know, IT services as such. Um, I am an electric and electronics engineer.

I started with a company called Tata Steel. Right? And there too, in its mining division. Right? So it was core engineering, which I was dealing with.

But slowly, um, you know, I had a lot of other interest. Right? I wanted to explore other areas. I got a chance to work with the procurement team at Tata Steel, and there I was exposed to, um, you know, my first enterprise platform software, which was SAP. I think that's where the journey started.

Right? Post, uh, you know, post spending some time at Tata Steel, I did my MBA. And, uh, after that, I joined Everest Group within the cloud and infrastructure services practice. And, uh, you know, I think the journey from there has continued in in different directions. Right?

So while so when I joined, um, essentially, you were a small team, um, you know, 3 people. And now today, you know, we have grown to around 14, 15 people or even more, I would say. So, you know, when we started, essentially, um, the focus was on cloud and infrastructure. Right? And at that point in time, the market used to see cloud and infrastructure, including everything right from your, um, cloud traditional infrastructure, digital workplace, cybersecurity, everything used to be under 1 leader.

I think slowly the market has, uh, matured, and we have come to a place where all of these are very distinct entities. Even within cloud, there are distinct entities. For example, AWS, Azure, Google Cloud, all of them have, uh, you know, their own area of resource. You know, enterprises need to know about each of them in a lot of detail. And therefore, uh, the market in general, right, the cloud overall to understand cloud and to take the right decisions has become more complex.

So decision making needs, uh, you know, more more more thought through processes, and that's where a lot of enterprises also falter. So, yeah, you know, in short, that's that's how the journey has been.

PJ Farmer

So I found that's really interesting because when I think about cloud and infrastructure, I often think of it, you know, you have to be a full stack engineer. Right? You can't you know, the the silos of specialization and passing things off from the storage engineer to the compute guy, to the security guy or gal, obviously. Um, I feel like that's that is just not how businesses work quickly and seamlessly and get things done anymore. And as you were talking about that, you were talking about these I I may have misunderstood, but I think you were saying that, you know, as the clouds have matured, they've had to specialize in different areas and break them up.

Can we talk about that part a little bit more?

Mukesh Ranjan

Yeah. No. Um, so, you know, you kind of got it right. So, essentially, in terms of the complexity, right, so there are multiple moving parts, multiple components when it comes to cloud. Right?

So, you know, if you if you just think about it, um, today's if you just take any enterprise landscape today, right, so typically, most enterprises today are in a hybrid cloud of construct. So what that means is essentially, they will be workloads on, um, their on premise data centers, on their private cloud environments. Uh, there will be workflows on AWS Azure or Google Cloud. There will be edge workloads, IoT workloads. Right?

All of these having their own, um, you know, challenges or their own, um, you know, yeah, basically, their own challenges to deal with. Right? So, potentially, you need to have an in-depth understanding of each of these, what's going wrong, you know, what's, um, in the entire system, where is the what can you optimize? Right? What can you do differently?

So, basically, it's tough, right for someone to, you know, have an overall view just for so what you mentioned in terms of full stack engineer. Right now, theoretically, that does sound brilliant, right, that, you know, one individual can do everything right end to end stack, and we do see demand for those kinds of services. Right? I'm not talking that it's not there. But essentially, when it comes to the vast landscape of an of a cloud environment, typically, you need a lot of expertise in different pockets.

Right? Uh, the full stack is one component of it that there need to be few people who understand the end to end stack so that they are able to bring everything together. Mhmm. Okay. But in general, yeah, you know, basically, the point was that, um, overall, it's it's a very complex landscape, and you need very deep expertise to be able to efficiently manage it.

Right? You know, many people are managing it, but not not the way it should be right now.

PJ Farmer

Yeah. Okay. No. I get that. I appreciate you clarifying that a little bit more.

07:04 - AI Integration and Cloud Operations

PJ Farmer

Um, I can certainly see too as you were talking about, you know, your your hybrid cloud, have something on prem, you have something up with the public cloud, you may have something on the edge, all of those things have their unique challenges. Some of it's just simply understanding the networking of how all those things get together both securely and optimally and what you might need. And, uh, I'm sure AI factors into that too if you're trying to do any kind of, you know, building any large language models or doing anything along those lines. Have you had, uh, much, um, interaction with enterprises that are leveraging AI and automation to optimize their cloud operations? And if so, could you tell us a little bit about that?

Mukesh Ranjan

Yeah. Definitely. So, uh, again, you know, maybe I did not touch upon this, but as a part of my role, um, I speak to multiple enterprise customers, right, both from the demand side, supply side. So, typically, you know, I'm speaking to multiple enterprise who are actually adopting cloud, right, within their environments. So, um, AI is definitely a hot topic right now.

Right? And almost everyone wants to be there. It's not that something it's, you know, it's not that it was not happening earlier. Right? It automation, AI, potentially, all of it were, um, pleasant within enterprise environment, um, but generative AI, everything around chat, JPT, a lot of marketing, uh, lot of, um, you know, how it picked up essentially is now driving the wave.

Right? So but when it comes to an enterprise environment, uh, potentially, you know, something like you need to think much deeper than what we think of when we think of Charge, GPT, or the Google Gemini. Right? You know, those can be underlying layers, but you need to build a lot of, um, building blocks or or modularities on top of these. Now, yes, you know, almost all enterprises today are thinking about how do I integrate AI.

And the way I look at it is it's twofold in terms of how this is happening. So, um, it's cloud for AI and AI for cloud. Right? These are the 2 distinct ways in which, um, you know, AI and cloud are coming together in enterprise environments. Let me go deep into each of these.

So when you say cloud for AI, right, now what it means is that, um, most enterprises today are experimenting with AI. Right? So you mentioned LLM models. A lot of them are choosing public LLMs. A lot of them are developing private LLMs.

Now to execute all of these, basically, we all understand now that essentially any AI, um, application requires a lot of a lot of compute, a lot of data to be run. So now it's not possible for enterprises to have their own infrastructure for all of these, and that's where they are at least starting with cloud. Right? So they are using some of these hyperscalers or private cloud environments to run these, uh, run these AI applications. So that is where we observe, um, essentially, it is cloud for AI.

Right? So, you have cloud as the underlying layer, and you have AI applications being built on top of it, and you are experimenting with that. So that's one component of how enterprises, um, you know, bringing cloud and AI together. The second component is AI for cloud. Now what does AI for cloud mean is that, um, essentially, within your existing cloud operations, how do you use AI and automation to bring more efficiencies and, um, essentially, you know, how do you optimize your cloud environment, current cloud environment?

Now that will include, uh, things such as cost. Right? Um, cost innovation, resilience, agility, regulations. So all of these are different components which can be impacted if people bring in AI and automation within their cloud environment. Right?

You know, it's often a lot of a lot of enterprises mistake thinking that it could impact just my cost and they think in terms of productivity benefits just from a cost standpoint. But, again, these are the different parameters which we can think about and determine logic at least how, um, I use it is, uh, basically, I call it value from cloud, right, or cloud values. Therefore. So cloud value essentially is a combination of tangible and intangible factors, right, to tangible being cost benefits and intangible being the benefits such as innovation, resilience, uh, regulations, agility, and so on. Now when we say AI for cloud, essentially, how do you use AI to maximize this cloud value?

Okay. So not just cloud cost, not just from an operational standpoint, but the overall value that an enterprise is deriving from its cloud adoption, where can we use AI and automation to enhance and maximize that? That's the second way in which enterprises are trying to adopt AI.

PJ Farmer

Can you give me because I like this. You've got cloud for AI and AI for cloud In the AI for in cloud management, right, can you maybe tell me about some really innovative use cases that you've come across in your research?

Mukesh Ranjan

Yeah. Good question. Um, so when it comes to AI for cloud management, typically across the different steps of the life cycle, right, or if you think about it from a services standpoint across consulting, consulting design, build, and manage services, there are multiple applications. Um, for example, you know, if you think about, uh, consulting. Right?

So, basically, a lot of document processing needs to be done at the beginning. Now earlier, it used to take significant amount of time for people to go through all of the documentation and collect all of the inputs from different people through primary interview service and so on. Now a lot of this is, uh, you know, being fast tracked using AI. Right? So, basically, you're gonna tell you can deploy AI automation to gather all of these.

Um, you know, you can you can, uh, extract insights from the database directly. Um, and, you know, you can also collate all of these inputs more efficiently and so on. Right? So that is one example that we can take at the consulting stage, which is from a document processing standpoint. Now it is also being used from a monitoring perspective.

Right? So if you're talking company operations part of it so, um, or, you know, let's start from the consulting and then design build. Right? So if you talk about design and build, now, um, you know, in the entire process of, um, application rearchitecting, for example. Right?

Now, typically, uh, a coder used to go in and search for different codes in terms of how can we optimize this application and so on. Now a lot of it is is being done through AI. Now the coder is still present, but they are able to leverage AI and generative AI as an assistant. Do a lot of quick searches and identify which, uh, code can I use over here, how can it be optimized in a much quicker manner? So the entire 6 hours.

Right? So, you know, replatforming, rearchitecting, and so on at all stages, typically, some or the other component of AI is being used to optimize this entire process. Now when it comes to the run stage or managed services stage, uh, you know, we have things such as monitoring, um, you know, monitoring and and compliance related things that are all that are being, uh, plugged in. That AI is being plugged in, and it is getting automatically it is being done in an automated fashion. So across the entire, um, you know, state, right, right, from consultant design, build managed services, if you talk about productivity benefits coming from AI, typically, um, you know, depending on where an enterprise is in its cloud maturity journey.

Right? If you consider a medium maturity enterprise, uh, typically, we do absorb 30 to 40% benefits. Now these benefits can be in terms of cost. The these benefits can be in terms of reduction in time to market an application. Um, it could be reduction, uh, in in response time for a for a particular issue that has come up in an application and so on.

And, again, ties back to point when we talk about productivity benefits that we are receiving from AI, you need to consider all of these factors, right, not just cost as a single factor.

Mukesh Ranjan

Oh, for sure. Um, the productivity benefits, you know, as we're being told, and I think even starting to experience that, you know, if you don't figure out how to use AI to get that that productivity benefit to get that faster, you you're gonna get left behind. Right? So I think everybody is looking for how can this impact me, my job, my company. What can I do to go faster?

PJ Farmer

And I think it's incredible. This is gonna be an incredible time over the next couple of years. So I like how you were talking about, like, IT and skill talents too. I wanna talk a little bit more about that because I think you have a pretty unique perspective and and and broad at the same time too. And so when we we're just talking about AI.

15:29 - Evolving Skill Sets for Cloud Professionals

PJ Farmer

So I'm curious. How do you think that the integration of AI is changing the skill sets that are required for cloud professionals these days? How do you do you see that changing? How do you see them reacting to it, figuring it out? What do you think?

 

Mukesh Ranjan

Yeah. So that's that's a very interesting question and and, um, I think very important one for almost, um, you know, all of the all of the folks out there who are in this area. Right? Irrespective of where you are. Right?

You know, whether you're operating at an l one, l five level, or you are a CXO within an organization. Typically, you need to now understand how cloud and AI are coming together and what are the skill sets that you need to accordingly change. So first and foremost, what's happening, and it started a slightly higher level, is that cloud the way it was? Right? I think, um, it is changing to cloud plus AI.

Right? You know, everything that all of the conversations that we used to have within cloud, for example, you know, you're moving an application from your on premise infrastructure to AWS or your Google Cloud. We used to talk at an application layer, infrastructure and application layer. Now today, the conversation is, you know, how do we move this application to AWS? What are the elements where AI can be integrated along with it?

Right? So we are talking about infrastructure. We are talking about applications. We are talking about data. We are talking about AI.

We are talking about security. Right? So it's not even just AI. Essentially, all of these 5 things are now coming together. And what it means for, um, you know, for for, uh, anyone anyone in this particular field is really they need to to expand their, uh, skill sets.

Right? So, potentially, someone a couple of field back, sorry, um, who was a specialist in AWS. Right? Um, you know, and it they had good specializations in terms of doing migration, modernization on AWS, and they were, you know, they were only earning in in significant dollar values. They are starting to become less relevant.

Right? You know, unless and until you kind of integrate, um, and add AI skills along with what you already have, they are not getting the, um, you know, the advantages that they were having having a couple of years back. Now not only does this apply, um, to the to the actual folks who are doing the execution work, developers, coders, and so on, testers, and so on, but also the salespeople. Right? So in 2020, 2021 period, um, I remember talking to a few sales folks as a at Azure, and they were having such a great time because it was selling like hotcakes.

Right? And it was really easy for them. But now, first of all, cloud is not selling the way it was. Essentially, the nature of, um, the consumption itself has changed because most enterprises today have already adopted it for the simpler workloads. And now, um, first of all, the selling is not easy.

And now wherever it is getting sold, you need to have those, um, you know, data security, AI skill sets that you need to bring along. You need to talk about the entire picture in terms of what's going to happen. And I'll just round it off. Right? So, um, so in the entire if you talk about the entire IT skills market, right, you know, what we the way, at least, I like to think about is it it can be break and broken down into different levels of expertise.

Right? So one is your for example, it it starts at an application level. Right? You know? So that's the highest level in terms of we are talking.

But when you start to come down, so within applications, what's the l two level of skill sets that, uh, someone possesses? And then there is this l three level of skill sets that someone possess. Now there are 2 ways in which people can differentiate right now. One is that you have this broad end to end capability, and you are really having that, uh, full stack capability, what you mentioned. And second is you really specialize in a certain area.

For example, there's a lot of demand for AI and analytics on Google Cloud. Right? So if there are specialists who, um, you know, who go very deep into some of these areas, they also kind of command high premium and are are sought after in the market. Right? So, basically, these are the 2 distinct ways in which, um, you know, people need to enhance their skill sets and and go about this.

20:33 - Shifts in Cloud Adoption Trends and FinOps

PJ Farmer

Yeah. Yeah. It makes sense. Uh, absolutely makes sense. It's changing.

I like how you were talking about how it starts at the application level and goes down. Right? Because at the end of the day, you're getting the value out of the application. The focus has shifted there over time. I feel like we hit a few different things there.

I wanna dig into one of them, um, and that is sort of, like, how cloud adoption trends have evolved. You touched on, you know, what happened in 2021 in terms of Azure selling like hotcakes, which I love that I love that saying. Um, could we talk about since you joined Everest Group in 2016, what are some of the things that you've seen since then? So we can put, like, that little snippet of of trends that you were just talking about maybe inside of a decade. Right? Maybe talk about how it went.

Mukesh Ranjan

Oh, that's an that's really interesting one. Um, again, I touched a bit upon this in the beginning in terms of how things have changed. Um, but let let me try and break it down. Right? So, basically, at least when I joined 2016 to 2018 and 2019 time frame, I put that as one, um, you know, first phase.

Let's say, you know, it's phase 1 of of of cloud adoption. Um, I think that is where enterprise had just begin to understand cloud in in detail, right, and what all applications should be moved, and they were experimenting. They're slowly doing the migration. Slowly, they were doing migrations and so on, and cloud market was picking up. In fact, we talked from a growth perspective.

I remember, you know, it used to be somewhere around 18, 20%, um, CAGR when we when we talk about cloud services market. And, uh, you know, enterprises were a bit bit skeptical. Right? So example, there were concerns around security. People were not entirely confident of, um, their data being secured in in public cloud environments.

And therefore, there was a lot of focus on on prem infrastructure also. There were private cloud environments also. So within this time frame, um, essentially, everyone. Right? You know, all CIO, their agenda was quickly, you know, let's let's do this migration quickly.

You know? They did not really think in terms of what's a long term view. Everyone just wanted to be on cloud. Mhmm. And by 2022, I think a lot of people actually had moved a fair share, at least a simpler workloads on cloud.

Um, and, um, I think even within Everest Group, we saw so much of momentum for the cloud in such a practice, uh, in terms of research. So many people reaching out trying to understand one because that option was happening so rapidly. I think 2022 onwards, um, a combination of multiple factors. Right? One is definitely the entire recessionary environment that that came up.

Right? And we are living in a prolonged recession. It's it's not come out as strongly, but that's how it is. And enterprise became a lot more cautious in terms of of their spending. They also realized that we did not really think this through.

Right? You know, we are moving to cloud, but what exactly are we expecting out of cloud? And that's when they started to question their investments. Themselves, to the hyperscalers that what is the value that we are getting out of the investments that we have done. And that's where cloud value as a concept came up.

And, you know, people started to and that's where, you know, value led cloud services, essentially. And what's the value that we that is being delivered came, um, came about. And enterprises started to invest less in new cloud services and started to invest more in optimizing what they already have. That's where FinOps or cloud economics as a practice also picked up. Mhmm.

Right. So very interestingly, um, you know, at at I was at 8 of the last year, and there were at least 30 or 40 niche or very specialist FinOps players, right, just doing FinOps with and even within FinOps, some very specific focus areas within FinOps. So just goes on to show the market traction in terms of cost optimization, in terms of optimization of what people already have, and it is continuing to be that way. It continue to be that way at least till 2023 and, you know, um, early part of 2024 also. And one part of cloud still continues to be about that.

Right? You know, more about maximizing value, optimizing things, and so on. And then, you know, we had the entire generative AI. Mhmm. You know, that that kind of changed everything again very dramatically.

All of the cloud leaders started talking about AI. Right? You know, earlier, there were 10 folks who were talking about AI, but AI and cloud, I think, the both of them came together, um, essentially, and and the lines got blurred. And, essentially, now, as I mentioned earlier, basically, it's not wherever the cloud conversation was happening, it's cloud dotaiorcloudplusai conversation that happens right now. There were 1 or 2 more interesting things that happened in between that die that died off.

25:18 - Industry Cloud, Future Trends, and Green Cloud Initiatives

Um, one was industry cloud. Right? It is basically, it's not died off, but I would say that it it it got deprioritized because of the generative AI wave. But industry cloud was something that was starting to pick up in around 2021, 2022 because once enterprises had moved their simpler workloads, they wanted to, um, adopt cloud, which was more easy to do. It was more plug and play and also suitable for their environments.

Right? So banking enterprise, for example, wanted a cloud which was already, um, you know, tailored to their environments, right, which had, um, compliance related things that were built in earlier, right, which are optimized for application that are used in the banking space and so on. So that's where industry cloud as a concept was picking up very rapidly. Mhmm. But due to the generative AIV, it it, uh, took a back seat.

Now the reason I bring it up is these are the things which I do believe that over the next 4 to 3 years will start to become relevant again. Uh, basically, it'll start to pick up now itself as the generative AI wave dies off. Some of these things will start to pick off, uh, again. Right? So 2025 onwards, potentially, we'll start seeing some of it.

So one was that. Um, the second was, so industry cloud. The second one, migration of complex workloads. Right? So there was a lot of push for from hyperscalers, for example, to migrate mainframes on AWS because they were also seeing stagnation, and they wanted these complex workloads which form a good 50, 60 percent of enterprise states, right, which are which have not yet migrated to cloud.

So potentially, enabling enterprises to migrate those complex workflows such as mainframes or enterprise applications such as SAP, and so on to migrate to cloud. That is another thing that we will start to see more accelerated, um, over the next couple of years. Another important theme that picked up in 2020 and continues to slowly pick up is, um, around sovereign cloud. Right? So this was especially relevant in Europe.

Mhmm. And it continues to be so. But 2020 and 21 period was significant, um, because I don't remember the name of the committee that was formed. Um, I think it was GAIC or something. Right?

Um, where essentially a lot of it was like consortium where enterprises, service providers, government, everyone came together and, um, it was Gaia X. Right? Now I remember. Right? So it was Gaia X is the name of the consortium.

And, um, essentially, everyone came together and ensured that these standards are put in place, and at least we develop. We see it being followed in a stringent manner now. And we also believe that this concept of sovereign cloud will be adopted in North America, APAC, and other regions also. It is being adopted at some level, but not like Europe, but it will start to become more and more relevant. And I think finally, um, there was one more theme which was picking up, died off, but is picking up again, um, is green option sustainability.

Okay. Right? So it was picking up in 2020, 20 21 because, you know, there was so much of work from home. People started using application like Teams and so on, and the data center consumption went up. So there was a lot of focus on building green data centers and so on.

And, uh, slowly, you know, over the next couple of years, it was starting to die off. But now, again, with AI and generative AI workloads, the consumption of the, uh, compute consumption, the water consumption of of a of a query done on chart GPT, and the carbon emission of a of a, um, query done on chart GPT. All of these are significant in terms of numbers. And, again, there's a drive to ensure that how do we optimize all of it. Right?

So, basically, you want to focus on sustainability and green ops also, uh, in the near future. So these are few of the trends which are also we will be seeing. Right? We're already seeing some of it, but then this will start to get more relevant in the next 1 or 2 years as well.

PJ Farmer

So I think those last four things you said, um, were going backwards a little bit. You know, readoption of green technologies, kinda put a pause on that for a second. That'll come back. Industry clouds, and I think you talked about complex workloads and then a focus on cost optimization. Or is was that the 4th, or was there another 4th, I'm sorry, that as far as trends forward trends?

Mukesh Ranjan

Yes. So, uh, I think so these were the few ones. The 4th one was Sovereign Cloud.

PJ Farmer

Sovereign Cloud. I'm sorry. Yeah. So the question I had about that, um, if you would, is, you know, there was a huge focus on green and data centers and everything we're doing, um, a lot of initiatives, ESG, you know, initiatives around. But I feel like what's going on with AI right now is sort of taking a lot of people to put that in the back seat.

And then recently, I was reading about protests about data centers in small towns, uh, I thought was pretty interesting. You know? People are going anywhere they can find power. How do you see that, like, those two things come together? You know?

Because I feel like they are complete opposites of the spectrum.

Mukesh Ranjan

No. They are. They are. Right? And, um, essentially, I think that's where, um, the objective is to build more green data centers.

Right now, you can't do away with some of these. Right? But, essentially, um, the most energy intensive component of a data center is the cooling. Now we see a lot of innovative solutions when it comes to cooling, which require a lot more lot lesser, um, electricity. Uh, you know, there are there are, um, you know, there are structures which are naturally cooling, right, which don't need any amount of electricity.

And a lot of innovation is happening in this particular space, and that's where the push is, right, to adopt more and more, uh, greener data centers. So whatever new data center is getting created, at least the focus is to make it more, uh, sustainable, more, um, yeah, you know. And and it it is at a conflicting object conflicting roads. Right? Basically, we talk about AI consumption through AI and green data centers.

One interesting thing is also happening right now. Right now so when 2020, 2021, essentially, this, um, sustainability thing picked up, There were a lot of commitments by enterprises in terms of their ESG codes, carbon reduction by 2025 and 2030. So by 25, the period is approaching, and most of them are far from it. So, therefore, we are also seeing a lot of focus on outsourcing or getting partners to come in and help enterprises achieve those goals that they had listed then. Right?

So some of the goals will be pushed forward. But still, um, I think that's another reason why something like sustainability is going to pick up because all of these commitments have been made in annual reports Yeah. Of enterprises, and they they they need to to adhere to that as well. So, I mean, that's another reason why this picks up. And within sustainability, obviously, sustainability is a very broad umbrella and, you know, everything including your ESG, right, environmental social governance comes into it.

But when you talk about cloud and sustainability, I think cloud sustainability in itself is a big topic.

Right? So because, you know, you're using so much of compute capacity for each application that is being run and the world is becoming more and more about applications. So cloud sustainability in itself is a is a big topic to be talking about. Yeah.

PJ Farmer

For sure. But that makes a lot of sense, um, about, you know, their ESG initiatives and what we're gonna have to do in order to have all the power that we need to do all the things. So, um, let me I wanna touch on another one. And now, um, nowhere in that discussion did you talk about cloud repatriation. And I know, to me, you know, this is a big topic too.

You've got all these varying articles out there. You know, outside of the giant, you know, obviously, Apple, you know, repatriated years ago. Dropbox did too, several of these. Are you seeing this at the global enterprise level, at the enterprise and commercial level? And and if you are, why?

Mukesh Ranjan

That's a good question. Right? And, again, I I, you know, I understand where this comes up. And believe me, I get questions from people within Everest Group that, you know, see this article has got published. Right?

You know, what what's what's your point of view on it in terms of cloud repatriation? And, uh, we have explored it. Right? You know, honestly, we try to get get real, genuine feedback because it's it's it's a bit hard to think that it's happening at that scale. Right?

Reason because it's not that easy. Essentially, once you have moved an application to cloud and you think about bringing it back, you need a lot of skills in terms of expertise. Potentially, you know, there will be disruption in your ongoing operations and so on. And and, generally, it's also not very cost, um, effective at least in the short term. In the long term, maybe, you know, you can achieve it, but in the short term, potentially, it's not.

Right? And it also goes against the entire philosophy of, um, digital transformation that most enterprises have undertaken. You rightly pointed out that there have been few use cases where Dropbox, right, I think is a major example. Everyone talks about it because they have shown big numbers in terms of the savings that they were able to achieve through repatriation. But I think it's very use case specific.

Right? And Dropbox as an organization is doing something which is which is very, um, unidirectional, right, I would say. So, essentially, if you do talk about a lot of other enterprise, there are so many different organization or, you know, individual silos, organizational units operating. Right? You know, there are different business um, objectives that are there and so on.

So it's so we don't yes. Right. Let me come to an answer to it. Right? So, honestly, we don't see it happening at that scale.

Right? So cloud repatriation, a lot of it is noise that, um, is there in the mark. It happens. Right? So there are certain applications you move to cloud and you realize that, you know, potentially, this was running better on an on prem or in terms of cost.

You know, maybe you don't need cloud for it. Right? You don't need to it's a very stable workload. Right? You know, it'll run run well.

You don't need that kind of avail availability, reliability for an application. So you decide to move certain applications back. So even when you actually read articles about repatriation, typically, what happens is that there will be one application which has been moved back to, um, you know, private environment and a on prem environment. And the way it's often projected is that, you know, this enterprise has done repatriation, moved away from cloud, and so on. So the, um, it's it's it's blown up in proportion, um, at times.

And, um, you know, although I don't see that it has not happened, there are things wherein, but it's not that it happens at scale that you are migrating 100 applications back to on prem data centers. So that kind of repatriation, uh, does not happen.

36:17 - Conclusion and Future Outlook

PJ Farmer

Yeah. I agree. I agree. So I love this. Let's keep talking about, um, trends.

I feel like you've given me some really good, like, insights, predictions, and reasons for those predictions. You know? Just what you were talking about, taking a break from industry cloud. We're doing cloud you know, we're doing AI for a hot second here, and it just evolves into what something you talked about earlier. AI doing the monitoring and the regulations for these industry clouds, exactly what they need.

This is all very exciting. It all piecing together, um, everything that you're talking about. So let's hit a couple of other things, like blockchain. So what do you think about blockchain? What do you think about its future in cloud ecosystems?

Mukesh Ranjan

Yeah. That's, uh, you know, another good one. Right? You know, potentially, again, a technology that I would have put as something that caught a lot of attention, and then we don't hear a lot about it now. Right?

So what happened to blockchain? So I think what has happened is that, uh, there was a lot of hype when blockchain came into existence. Um, it had applications across different verticals. Right? You know, obviously, banking was the first one, and potentially, it had the maximum number of applications over there.

A lot of banks also adopted it and are running it successfully. So, um, that is definitely there in terms of retail, etcetera. Also, there there were, uh, uh, applications of blockchain, and it is being run successfully. But, um, again, it it did not really become mainstream in the sense that it's not suitable for all kinds of application. Right?

Neither do we need that level of, um, you know, coding and security, which are the key features of of blockchain. Right? Either you want to put everything on a, you know I I don't remember all of the technical details within a blockchain, but I do understand that you need a lot of people who are mining. Right? So it links very closely to Bitcoin also that, you know, you are doing some of those mining activities and so on.

But you don't don't need that level of security. Right? So blockchain, I think, became, uh, relevant because of it being very secure. Right? You know, you can track, uh, transactions also back to points of origin and so on.

So, again, what I believe is, uh, blockchain will continue to exist with very specific use cases across different industries. Right? So, um, it it it's not something that'll become mainstream and again start to replace some of the other technologies, um, but it will be complementing a lot of other technologies.

PJ Farmer

My last question for you, and I by the way, this has been one of the most fun interviews, but I love talking about trends and why and what's coming up. So, um, but I the last thing I wanna ask is what advice would you give to CIOs and IT leaders preparing their cloud strategies for the future? Like, what's some sage advice here, Mukesh?

Mukesh Ranjan

Yeah. It's it's it's a tough one, right, again, because, um, things are changing so fast. Right? We did not know that AI and generative AI would change everything completely. So potentially, if I was answering it 2 years back, the answer would not be relevant and would not hold true right now.

But, um, I think, you know, again, given what we know right now, right, and given what we can predict and think about, um, the way CIOs and IT leaders really need to think about, and it it does need a, a change in in in some of the mindset that a lot of, uh, folks today have is, um, starting to think more longer term. Right? And I have a more defined and strategic road map for cloud adoption rather than doing it in a piecemeal manner. Okay. So, you know, the entire challenge and, uh, the discussion we had around value today, all of that happened because, um, cloud adoption in the period 2020 to 2022 was done in a thoughtless manner.

Most enterprises just went about migrating applications to cloud without thinking what is the value they are going to get. Why do they want to actually do it? And the same is true today also. Right? We are observing the same things with AI now.

Right? The cloud and AI coming together that, you know, we want to implement AI in different parts of the organizations. Procurement teams talk about, you know, give me maximum productivity benefits, uh, from AI. But what is productivity? Right?

Even that is not being defined today. So, again, it it it it's not, um, really thought through in terms of how most enterprises are doing their cloud and AI adoption. The need of the hour really is for the CIOs and the IT leaders to really, um, kind of prepare a more strategic long long term road map and then break it down to shorter, you know, checkpoints. Right? So 2 years, 3 years down the line, this is how we envision our enterprise state to be our organization to be.

These are my business objectives. Right? So today, I am utilizing cloud for, um, you know, starting certain applications, doing certain kinds of optimization. But, potentially, there is a need to reinvent my business itself. Okay?

Right? You know, there's so much that's changing in in terms of all of the organic take example of retail. Quick commerce is changing everything, especially in in in a country like India. Right? It is just overturning some big giant like Amazon.

So potentially, there is a need to rethink your business model itself. Now if there's need to rethink the business model, the cloud strategy also needs to evolve according to that. Right? So, potentially, you don't keep running the applications the way you are running today on cloud and you're done with it. Right?

So it's a journey. Essentially, cloud adoption is a journey, continuous journey, and there are different components within the cloud, um, journey that also are circular in nature. Right? For example, cloud economics. So that needs to be, you know, kind of reviewed at a very periodic interval of of of 6 months, 4 months that, you know, what's happening with my entire cloud economic story?

Am I in place or not? From a value perspective. Right? You know, keep checking at very regular intervals whether I'm getting the value the way I had defined it or not and so on. So, again, you know, to cut it short, um, identify your business objectives.

Right? Um, define value that we expect out of cloud and AI investments according to that. Create alignment within your internal organization. Right? That's very important.

Right? So create alignment within all of the key stakeholders within the organization. Implement it. Right? You know, essentially, it sounds very logical, but do the implementation.

Often, the implementation takes so long that everything changes. Right? So implement it in a giant manner. Measure your success. Right?

Uh, basically, there needs to be proper monitoring measurement, uh, techniques. And then be, you know, agile and f 2, change it, or, um, you know, use certain metric, and then you have you repeat the cycle. Right? So, potentially, these are the 4 or 5 different steps that most CIOs, IT leaders can follow to ensure they have a robust strategy, um, going forward.

PJ Farmer

That's perfect. That's perfect. Thank you very much for your time today. It was great to meet you. Good conversation. I hope you have a great day.