Okay. Great. Hey. Hi, everyone. This is Monique. Good morning, good afternoon, good evening from wherever you have joined, and thanks for joining, the session for today.My today's topic is embracing the future with AI first cloud strategy. And before we get deep dive there, I would like to just quickly give a a quick introduction from my side. I'm Manik. I work as a principal consultant at Thoughtworks. I'm a principal architect there with overall nineteen plus years of experience. I majorly, drive into the business with the client on the areas like digital transformation, legacy modern modernization, cloud strategy, and with the new one as AI first cloud strategy. So, while we deep dive into this topic, I expect the people, the audience here definitely understand what is cloud and what is AI.

Today, what we are going to do is we are going to shape it shape up together how the AI first cloud strategy can be, can be planned, strategized, as well as operationalized. And that's the topic for today. Now let's first understand that why it is so important to have, both AI and cloud into our business. So in the area of digital transformation today, we are standing at a intersection point where these two transformational focus areas, which is cloud and AI, are very important for our business. With cloud, we are able to gain scaling, transformation, as well as building a strong foundation platform for our business. With AI, we are it helps us to bring the intelligence, the real time decision making, and a faster innovation for our business. So it is not a choice whether we should select cloud or AI, but at this today's moment, it is important that we bring together the strengths of both of these technologies.

And that's what is the tech that's what is the topic for today. So with cloud and AI, we are we it is possible for us to reimagine the entire digital transformation by future proofing our enterprise, driving smart decisions, and making faster innovations at a very large scale. Now let's also understand the market signals and the executive trends that we see about AI and cloud today. So while everyone was speaking a year ago that AI is going to be our feature, I will say in today's session that AI is already there. And that's what these insights actually give us those kind of statistic as well as the data here that, yeah, it's not a future, but it is already there. So there are some various surveys that have been done by the leading, companies like Gartner, McKinsey, PwC, and they say that 85% of the organizations have already led the cloud first, have already built the foundation with the strong cloud technologies, and they are now moving towards the adoption of AI at scale.

50 per 55 of them have already adopted the AI first strategy. So they are not at the start of this journey, but they have already adopted and experienced the benefits coming out of the cloud first AI. While McKinsey says that as per 92% of leaders are already thinking to boost their their spending in this area by 92% in the next three years. And while other technology leaders as for PW says that they have already fully integrated AI into their strategy, and they have they have now on the verge of designing and operationalizing their AI first cloud strategy. So all in all, it is very clear that AI is not a choice for us today, but it is a mandate for our business if we wanted to scale, grow, and, bring more profitability to our business. Now how do we reach the state of AI first cloud strategy? Of course, there are some steps that will help us to guide and move ahead.

And, again, it is not a choice whether you choose a cloud first, data first, or AI first, but it is more about it's amalgamation and it's convergence of bringing all these technologies together to enhance our business growth. Now let's understand what is the first step, what is the next step. So when it comes to the cloud, as as we understood already, that cloud brings scale agility and a strong platform foundation for for us. Data brings the re helps us to get insights of that quicker insights from the data, getting real time statistic, and which helps us to make a decision making. So while cloud and data are there, they they can act as a fuel for us. AI helps us to make it more intelligent, make it more automated, and make it more smart.

And that's where it is not a choice that whether you use cloud and data as a combination or data and AI as a combination, but it is it is imperative for today's world that we use a combination of cloud data and AI and build up our business strategy, which could be formulated using cloud first AI strategy.

Now what is AI first cloud strategy? So it is it is all about building a strong foundation using cloud data and building AI on applying or using AI services to build some innovation use cases which could enhance and help customer experience improvement and which could also delight your customer journeys for the users.

So what does it mean? It means don't plug in AI as part of your strategy, but embed it as a core foundation as part of your strategy. This means what? It means building an intelligent infrastructure, building the data foundation, which is AI enabled so that you don't need to really wait for the insights to come. But you can at the same time, while the insights are on the way for you to to get reported, you can also apply AI to draw inferences, make some decision making based on those based on those insights as a part of recommendations. And what is the AI native application? AI native application is nothing but applying the usage of AI enabled services for your user journeys, for your innovation use cases so that you could enhance the customer experience. So this is what is all about our topic of today that what is AI first cloud strategy.

It is just to reiterate, AI not is not required to be plugged in, but it is something should exist as a core foundation while you formulate the strategy. It is more about applying the AI in each and every layer of your strategy when you operationalize your framework. What are the key building blocks of AI first cloud strategy? While cloud, which provides a robust infrastructure, security, and a governance for us. This has to exist as a foundation. It is also important to have a data foundation. Data should not be treated only for a storage purpose, but data should be considered for a pay for a purpose through which where we could we could have some meaningful data so that we could derive some accurate real time insights out of it. And on top of that, apply the AI stack, AI technologies so that you could make it more intelligent, you could make it more smart, and automate it.

Now when it comes to the AI first cloud strategy, first is the cloud infrastructure. Second is the data foundation layer. On top of that, you'll have to deploy the various AI, ML services. Why they are required? They will provide you a foundation in terms of generic models, which you can customize further for your specific use cases. The ML ops and automation is another thing which will help you to actually continuously monitor as well as optimize your landscape or the the ecosystem wherein you are deploying your services. The security compliance is is very much cross cutting as same as the governance layer. It is very important that your organization, when when adopting the AI first cloud strategy, your talent is ready for it, and your operating model is aligned to the cloud first AI strategy.

Because most of the most of the time, it happens that the the leadership takes a decision that we wanted to implement AI first or we want to make our organization AI enabled. However, the people and the processes are not compliant to that. Hence, it is very important that as you formulate your AI first cloud strategy, you'll you'll have to think also around people, process, and technology. And these all are the moving parts which will go as part of your change governance and a change management while you further design your AI first cloud strategy as well as finally operationalize it. It is really as part of today's world where it's it's a free, free ground for hackers and unethical lot of unethical things, it is really important that you make your AI services as well as the use cases that you are delivering to the customers. You make them ethical as well as implement a responsible AI as a strategy. This is a small blueprint you can use as a reference.

This is, of course, this is not a foolproof, and this this can be a framework for you as a guideline when you are starting a AI first cloud strategy for your organization. And, of course, when you are formulating the strategy and a blueprint, it has to be customized based upon a a particular organization wide scenarios as well as the state of it. So while before we went to formulate a blueprint and a strategy for it, it is really important that you assess the current state of your organization and do that observation or gap analysis in terms of you know the target state. You want it to be an AI enabled organization. But where do you stand currently? And what are those gaps? Once you identify once you observe your current state and and where you want to reach, it is really important to do that gap analysis and identify an a path. How can you reach your target state?

And this blueprint could act as a small framework for for your organization, which will help you to actually start building and making those steps forward towards being an AI enabled organization. So what is it? At the foundation, we will have a cloud led infrastructure, which is not really focusing only on the compute, but it is focusing more on faster compute, providing the GPU as a service, and then providing accelerated, faster processing to your various building blocks that would be built on top of it.

Next and, of course, it is really important to have this infrastructure AI optimized so that as a part of the foundation, you also embed AI foundational services as well as models, and you make them available for your devs and and the other teams so that they can use that and start building the complex use cases that you wanted to build for your customers.

The next stands, the data to AI operational capabilities. Now this is where you'll have to identify the the services and the storage where you would like to store your data. The data lake is is one of such example where it makes sense to because as year on year, your data organ organization's data will grow, and, hence, it is really important that you collect it together, build it as a single source or a golden source of data, and making it enable through various services which could be built on top of top of the foundation governance layer so that you could easily retrieve query and, the data that you need and easily draw the inferences and out and insights out of out of it by showcasing the various dashboards.

This it is not when we talk about data. It is not only about storage, but it it is also about how can you plug in and orchestrate the various data foundations or the data blocks that are available as a part of this layer, as well as how making enable your models and and the various AI services to consume that data.

So this also includes building the MLOps pipelines data pipelines in a more automated way so that the data that you are producing or storing can be easily retrievable, consumable, as well as storable. The next is a AI native cloud execution layer. This is more for the developers and the, and your product team's point of view wherein we will provide various foundational architecture building blocks as well as solution building blocks in the form of exposing it as a service so that the developers can readily use it as well as any wrap.

If they want to enhance those services which are already plugged in or build as part of this foundations can be customized for their own journeys, user journeys, or, for their own purpose of the innovation use cases. And next is AI driven cloud uses. This is more about, this is more about showcasing your use cases, various workflows. Let's say you are doing a customer onboarding for any of the form or KYC for for a BFSI domain, client. Then it is it is about adding an intelligent to to make your entire workflow robust, secure, as well as faster in terms of the processing. So the at the top of the layer lies the various use cases that you will that you will build by leveraging the prior three layers, which are nothing but the infrastructure, the data, and AI foundation, as well as native services that will be available as part of this entire blueprint.

We will also have certain cross cutting things which are nothing but the security and compliance, the the governance of the entire platform because those are very, very important, aspects to build any strong and robust architecture and an ecosystem for the organization. So while we we understood that how the blueprint for AI first cloud strategy will look like, Often, organizations ask me, being a consultant, organizations often ask me the client often ask me that, what is the right time to get into AI? How can I make my organization AI enabled? And what is the right time to do that? I would say there is no right time to do it. It is irrespective of your journey where you stand today, whether you are just embarking the cloud journey or you are already in the middle of this journey or you are already on the cloud and you have already transformed your organization by leveraging the benefits of cloud, still, it's a good time and is the right time to enable your ecosystem, enable your organization with AI first cloud strategy.

But the important aspect to take into consideration that while you are applying AI, do not apply AI only at one layer. You will still get benefits, but those are not the all the benefits which you will otherwise get if you apply AI at all the layers of your framework. To gain more benefits, it is important that you build and bring AI as a core of your strategy and as a core of your ecosystem. And that's where you will derive more benefits when you formulate these kind of strategies. So what is the next, what is the road map looks like when you move towards the formulating the AI first cloud strategy towards the executions? How do you start? Irrespective of where you are on your cloud strategy, you could still adopt AI and make it a core aspect of your strategy.

But the first thing is you have to understand what why are you why you want why do you want to adopt AI? It should not be only for the purpose because your competitor is adopting it, but it should be purpose driven. So what are your AI omniscient ambitions around adopting it? What are the business region goals out of it, and what is the, what is that you are envisioning by adopting it? So that strategic intent is very, very important, and that's where you start. It's the beginning of your building AI first cloud strategy. The next step is assess your current state, identify what are your benchmarks, as well as what is your current state. And then compare what is that where do you want to reach.

There could be various intents behind being an AI to become an AI enabled organization, but you'll need to focus on some prioritized way of adopting the AI. And that's where there should be a focus AI investment, which could bring you a high impact as part of your business, and it should be feasible also. So you'll have to compare and analyze your organization current state from various attributes, from the feasibility perspective, from the cost perspective, from the risk and compliance perspective, and also from the customer experience and customer delightness perspective. Experience and customer delightness perspective. Once you move towards identifying those strategic use cases, it is important to start designing and making that change enable for your organization by by start designing. It could be, the the smallest thing that you could start is is by formulating a center of excellence, which could which could act as an enabler for your entire organization.

And once you start building your small, small innovation use cases and rolling out, you will start seeing those benefits. So start tracking the value. What are your learnings so that you you establish a feedback clue so that you could enhance your strategy based on your learnings and experience. And the next thing is to start scaling about it. This there's a large style, and I'm I think I'm almost on the time. So it this is a AI road map at a glance. Again, it will change based upon what problem you as an organization trying to solve, and it could be customized. But this is more of a guideline that the first thing is to formulate a strategy, identify what is the value, and what are your prioritized use case.

Once you identify those, start working towards those areas or from all the aspects, people, process, as well as technology. While you are moving and adopting to the AI, it is also really important that your engineering team is enabled with the required skills, with the required frameworks, tools, and the technologies. And and if your data is not really if your data is really fragmented and spread across various system, that's a good point. It's it's a it's a good time to bring it all together and start building a single source or a golden source of data so that you could derive some meaningful insights out of it. So this is an entire road map, looks like, when you're formulating a cloud first AI strategy. And thank you for attending my session. Please feel free to connect with me for more conversations around this topic. I'll be happy to connect.

And, anything you required as part of your organization, I'll be really happy to help you all. Thank you so much.