Introduction
Ryan: Welcome everyone to the AI Founders Podcast Show. Our podcast is dedicated to celebrating the remarkable accomplishments of AI innovators, entrepreneurs, and visionary founders and the captivating stories behind the movements they’ve built. I’m your host, Ryan Davies, and I have the honor of hosting today’s episode, Integrating AI in Multi-Cloud Architecture Design for Retail, Healthcare, and Financial Institutions, with our special guest, Ayisha Tabbassum, Ayisha. Thanks so much for joining us here today.
Ayisha: Thank you so much, Ryan, for hosting this event. I mean, it’s a pleasure to be here.
AI in the Modern Tech Landscape and Fundamentals of Multi-Cloud Architecture
Ryan: I’m really excited to go into this one. There is so much here that we are going to be able to cover Ayisha. You’ve got such an amazing background as an entrepreneur of One Stop For Cloud.com, a certified multi-cloud architecture enthusiast, and a public speaker. You have a wide range of knowledge across a number of different industries, with a passion for integrating AI and multi-language and machine learning capabilities in the multi-cloud complicated architecture designs. Our distinguished guest is at the forefront of revolutionizing the integration of artificial intelligence into multi-cloud architectural design, particularly, as we mentioned in retail health care and financial institutions, really is an artist of innovation with a passion for solving complex challenges and throughout her career has been an advocate for ethical AI practices and has actively contributed to discussions on responsible and inclusive technology adoption. So I think this is a wonderful guest for our audience to really dive into and kind of go where wherever you are, right? If you are a founder of a tech company, if you’re someone who’s interested in AI and what’s happening out there, particularly in that retail health care and financial area, this is fantastic for us to dive into, and I think you know, I’m going to turn over to you to start with a little bit of an overview on AI in the modern tech landscape and the fundamentals of multi-cloud architecture.
Ayisha: Yeah, thank you so much, Ryan. Coming to the AI landscape in the multi-cloud architecture designs, the opportunities are huge and the integration capabilities in terms of APIs and in also, in terms of prompting technologies are gaining a huge popularity. I happen to see this trend amongst the tech folks wherein, you know, the jobs are getting replaced. So I felt that it is my responsibility to let the audience know that it’s very important and it’s highly critical for us to adapt to these AI technologies, to upskill ourselves with the AI in different areas that we work for, and the reason for me to choose retail health care and financial institutions is because these three industries are predominantly leading in every sphere in every geography. That being said, in today’s podcast, I would like to give the audience the opportunities to reflect think, and find the resources to upskill themselves so that they become part of this AI rather than getting replaced by this AI capabilities.
AI in Retail: Case Studies and Multi-Cloud Integration
Ryan: I think that’s fantastic. It’s a great place for us to kind of dive into. I’d like to go down and talk about each of those industries to kind of go and dive into a bit more. Maybe we’ll start with retail and really where the case studies are around AI-driven retail solutions and integrating those multi-cloud systems and AI into retail environments.
Ayisha: Yeah, sure.Ryan. So it’s very interesting that AI is providing the capabilities and AI is leading retail in predominantly three areas. One is the personalization and the customer experience, wherein you know AI can analyze customer data across multiple cloud platforms to offer personalized shopping experiences, product recommendations, and targeted marketing. That’s the very first predominant area, and the second is supply chain optimization. AI algorithms can predict demand, they can optimize inventory levels, and manage logistics by integrating data from various cloud sources. The third predominant area in retail is omni-channel integration, where an AI can help in creating a seamless customer experience across online and offline channels by integrating data from multiple clouds. So, yes, these are the three predominant areas in retail, and the way we see this is that the supply chain is a very complicated process wherein when we have millions of skews from predominant retailers such as Macy’s, Bloomingdale’s, Walmart, Aldi, Amazon and Flipkart and these predominant retailers have thousands and millions of skews. When I say skews, it’s actually the product inventory. Ensuring that the product inventory is sufficient to serve the demand and is being updated on a daily basis, minute by minute and second by second, is a highly complicated process, and AI is providing these capabilities to do it at a much lesser time with higher accuracy, that’s the best part of it. Next, I would like to talk about the healthcare space wherein AI is doing great in doing patient data analysis wherein AI can process and analyze vast amounts of patient data stored in multi-cloud environments for better diagnosis treatment plans and patient care. This is the first area in healthcare, and secondly, it is about medical imaging and diagnostics where advanced AI algorithms can assist in the analysis of medical images such as X-rays and MRI across cloud platforms for quicker and more accuracy.
This is the second area where AI is providing advanced capabilities, and it’s high time for the medical resources to upgrade themselves with the tools and tech stack and also with the capabilities and software that AI is providing secondly, it’s about the remote patient monitoring. AI can enable the monitoring of patient’s health data collected from various devices that are stored in multi-cloud systems. Facilitating remote care and telemedicine, when it comes to medical, we have this sensor-based wherein you know, for example, a patient who has a cardiac arrest undergoes an ECG because the AI works through sensors and multimodal approach, it is able to analyze the data and provide the accuracy of the diagnosis and the treatment plan and the third sector that I would like to talk about Ryan is finance, wherein we have this fraud detection and security. We have risk management, and we have regulatory compliance. When we talk about fraud detection and security, AI can analyze transaction data across multi-cloud platforms to detect fraudulent activities and enhance our security measures, and coming to risk management. AI models can assess risk by analyzing data from various cloud resources, helping in investment strategies and financial planning and third, the most important aspect in financial institutions is regulatory compliance. AI can aid in ensuring compliance with various regional and global financial regulations by analyzing data stored in multi-cloud environments. So yes, these are the main areas in retail healthcare and finance.
AI in Healthcare: Implementation and Success Stories
Ryan: I think that’s perfect. With all of that, I mean, that’s a great breakdown of each of those three different pillars and kind of the key areas to cover on each of those, and we can unpack and go a little bit deeper into each of them. As you mentioned, we saw what happened with supply chain solutions when the world started to change, and we needed to be able to get people those skews in different ways and just different ways. People had to access their financial services and health care all through the pandemic. Everything that we saw there and with all of this, I think it really created an accelerator in these areas, and really, people took notice of the importance of being able to stand up and support those areas to whichever retail or healthcare, financial or other that we’re looking at. Tell us a bit about the design principles, that have to go into place for this and the key considerations that are that need to be had.
Ayisha: Sure, Ryan, so the most important strategy in multi-cloud architecture design is data. Data management and governance are the most important factors because for all the three industries that I mentioned, there are financial transactions and there is sensitive data. When it comes to retail, the sensitive data is probably the credit card, the banking information, the social security number, and the addresses for the safety and security of the consumers and when it comes to health care, it’s the patient’s data which is very confidential. Even when we visit the healthcare, we are asked to sign a confidential agreement saying we can reveal the details of the patient only to the concerned person to whom the patient has provided permission to access the data, which is like our own close family members. So that is how important data is in these three different sectors. That being said, AI integration in data management and governance is easing and is simplifying, making that possible because it can do the threat modeling, it can do the penetration testing and it can identify the bot’s attack. There are various different kinds of attacks that hackers actually perform, such as there are DDOS attacks and there are virus infections where the entire data is wiped out, and we get to see this in the news a lot, right? What AI is doing is, it is establishing robust data management practices, which is crucial for the effective use of AI that is one of the factors includes data normalization, it includes ensuring data quality, and maintaining privacy across different platforms because, as I have seen in my professional career working for a number of clients, fortune 500 clients, I have seen that every client has a different requirement. Some go with a single cloud provider, some go with a hybrid module wherein they have their own data center hosting the sensitive microservices, and they have the other front end and the back end and the data layer hosted on the cloud platforms.
This being a mix of requirements, AI is ensuring and is easing and simplifying solutions to make it happen at a much faster pace in providing the solutions at a much lesser cost, which we count as cost and resource optimization and according to the next thing we do have like cloud agnostic AI services which use these tools to ensure compatibility and seamless operation across different cloud environments. What this approach does is facilitate flexibility and scalability in AI developments. These are the first two major things, and then we have edge computing, especially in retail and healthcare. It helps in leveraging real-time AI processing, especially for time-sensitive operations such as patient monitoring and in-store customer engagement. We see this in the kiosk, right? That is one of the factors, and patient monitoring is highly critical because it is the question of life and death. Any wrong diagnosis will lead to that, and it has an impact on the family’s life, and it’s also an impact on the medical professional, so AI is facilitating that and also the next strategy is the hybrid cloud strategy, wherein we are implementing hybrid cloud approaches that can provide the optimal balance between on-premise control of sensitive data and the scalability of public clouds for AI processing and analytics and the next key factor is security and compliance. Given the sensitive nature of data in these industries, ensuring robust security protocols and compliance with industry-specific regulations is paramount, and AI can be employed to enhance cybersecurity across multi-cloud architectures in predominantly AWS to assure GCP IBM and OCI and the next factor is interoperability and API management ensuring smooth interoperability between different cloud services through effective API management is very crucial for the seamless flow of data, which is essential for AI applications and the next key factor is continuous learning and adaptation so we have these AI models like neural networks, the LLMS, the NLPS so these are actually designed to continuously learn and adapt to new data and changing conditions, which is very important in dynamic industries, like retail healthcare and finance and partnerships and collaborations. We can see this in chatbots, wherein they collaborate much more efficiently than a human user, and that is why the major tech industries are actually going through a lot of layoffs; they’re laying off the marketing individuals because chatbots are able to do it all. It is like a robot that is able to do it all, and that is why recently, even Google led off a lot of folks, and so is the reason I wanted to bring up this topic in this podcast.
Ryan: I think it’s perfect to be able to kind of talk through all of that, and you mentioned there a little bit about some of the challenges with integration and scalability. Some of the common problems that might exist in there when we’re talking about these multi-cloud architectures and maybe you can give us some examples of the successful implementations in AI and multi-cloud implementations for our audience, what that kind of looks like.
Ayisha: Sure. Absolutely. When we design multi-cloud architecture designs, it involves multiple cloud service providers. There are different service-level agreements, different service-level objectives, and service-level assessments that are performed to ensure that the SLAs are being met. What happens is when there is a multi-cloud architecture design, the assessment, validation, and performance play a key role, and AI ensures that it is providing the APIs that could be integrated between, say, N number of CSPs wherein it is aggregating data and based on the multimodal capabilities, it is able to assess that data through like image analysis, through contextual analysis and through predictive analytics to through NLPs through language translations and through video analysis and through audio analysis, so it is able to assess the data that is gathered at one point in an AI data warehouse and we are able to run the predictive analytics and also the real-time insights. We are able to get, say, for example, I have a product that is hosted on AWS insure. It’s a very large-scale employment. We have millions of users on the website and I want to know what is the use case of a particular microservice. It is a multi-tier architecture wherein we have this web tier, we have an application tier, and we have a data tier, and the web tier is hosting the UI apps. The back-end tier is a connection between the front end, back end, and the data layer, and the data layer consists of the databases.
What this AI is doing is it has one central warehouse having all the traces, having all the trace logs of these different services from different tiers and also multiple providers and I want to analyze what is the resource optimization that can be performed. How do I know it? I would be able to know it by, running a real-time predictive analysis wherein I am saying, like OK, what is the resource optimization of service that is hosted on a cloud service provider?
One, and you know, is the resource utilization above 70% or below 70%? When I say resource, it is the compute, it is the storage, it is the data, and it is also like network iops and data iops. That being said, AI will provide me with a dashboard or it will provide me the image having the charts as to like OK. So for these services, the resource utilization is less than 70%, wherein the resources are optimized and cost is also optimized and for certain sets of services, the resource utilization is less than 30% which means we have allocated more resources than what is needed and so is the reason we scale down the resources and we save cost over a period of time in terms of millions of dollars, in terms of thousands of dollars and in critical times every dollar counts and retail is a very competitive market. Healthcare is not competitive, but it’s more of accountability because it’s about the lives, right? It’s very critical, and financing, of course, there is competition, but at the same time, data governance plays a huge role because it’s very difficult to have a successful financial institution. Any breach would result in less confidence from the consumer and they will go to other competing banks or financial institutions. It is very difficult to get back the trust, no matter the number of ad campaigns and the number of apologies from the executives.
Emerging Trends and Future Predictions in AI
Ryan: Exactly. Once you break that trust, that’s nearly impossible to get back. It certainly slows any growth, and it’s a catalyst for change, sometimes in a negative way from that side of things. As we kind of wrap up here as well, I wanted to talk about maybe some of the emerging trends and the future predictions, and for businesses, how can they stay ahead in adopting these technologies and assess where they’re at now?
Ayisha: Education, Ryan. So that’s a very good question to ask, and I wanted to make sure that I cover it, especially in the retail sector. It is very important for the executives and the management in every industry to upskill their employees rather than jeopardize their jobs. It’s very important that they train the resources to adapt to these technologies and provide them with alternative employment and career growth part because even if there is a layoff, then it is very difficult to get the resources back because it’s again a chain of process, so instead retain the resources that you have, retain the people with a humanitarian touch and upskill them, provide them trainings, provide them assessments and it will not cost them much because all of the major cloud providers are providing free education path. For example, if you say AWS, it has AWS cloud communities, there are cloud user communities, and that is like one aspect, and in AWS Academy, they have the certification program for the AI services that they are offering, which is like real-time and which has very good in-depth content. They have beginner, intermediate, and advanced levels. The resources are available for free, and the free resources are good enough. That being said, AWS is providing free AI certifications along with free training, which is online and also self-paced.
We have NVIDIA, which is charging for its content and also for the certification, but NVIDIA is a very trusted company. I have gone through the content. It is very technical, and it is at an in-depth level and whatever the asking is very feasible, I think it is affordable. It’s hardly like $135 or something for certification, and they also provide the resources and materials that are needed. Then we have Azure providing AWS. I’m sorry, but Azure is providing AI services that they have created. The training and certification badges are free, but certification has some charge, which is nominal and affordable, and GCP is in a similar line. We heard this huge trumpet along Google’s Gemini, and I also conducted a workshop along with applied AI on the same, and its capabilities are huge. It has a multimodal approach which made it more popular compared to Chat-GPT and other competitors in AI text pieces. When I say multimodal, it involves text, image, video, audio, MP4, presentation and everything. It covers all of the aspects of data types. So it’s the reason it is getting huge popularity and that is like one aspect of it. These are the parts Ryan, and I would also request the teams to be more vocal about their use cases and about their needs in adapting these technologies and in getting training and resources to their management.
Key Takeaways and Closing Remarks
Ryan: I think it’s just an unbelievable summary at the end there that ties everything together in terms of how to kind of find out and get that foundation and stay ahead of the curve on this, but I want to also open it up to you here before we sign off to just address our audience as well with any key takeaways we may not have touched on and also for our audience, how they’re able to get a hold of you and get more information and connect with you as an expert in this area. There are many people listening who I’m sure, are very interested to learn more and to be able to take that advice and put that into practical use.
Ayisha: Absolutely. Ryan. I consider that as my responsibility. I am there on LinkedIn. You can just type Ayisha Tabbassum, or you can ping me on LinkedIn. I have close to 10,000 followers, just, I think, 100 less than 10,000, and they do reach out to me, I help them, they help me, and I’m also conducting a workshop and a webinar on AI operations wherein I would talk in-depth and from a technical point of view on blending AI and IT operations. That is scheduled for Jan 31st, and the tickets are available on Eventbrite it’s free, and it’s online because that is my way of giving back. It gives me great pleasure to help people around the world learn the technologies that are needed, and I do have some content on YouTube. The contact information is also present on the One Stop For Cloud website, wherein I provide it is more of a tech company wherein I’m providing content and resources on five major cloud platforms, just Azure, AWS, IBM, OCI, and GCP. It is like a one stop for these clouds, and there is a service catalog, how to use pros and cons to do the analysis before beginning a project, like what a cloud provider would fit our needs at its best.
Closing and Acknowledgments
Ryan: I think for our listeners, it doesn’t get any easier than that to be able to continue to upskill in this, get more information, get connected with you. We’ll have all of that in the show notes as well. so we make sure that you’ve got everything at your fingertips to be able to connect with Ayesha and get more of what you’re looking for in terms of this space, how to implement it, and making sure you’re on the right track and with that, thank you so much, Ayisha, for joining us here today, for sharing this knowledge and looking forward to again taking, taking in those the events that you’re putting on and hopefully having you back again where we can cover some more topics in these areas and dive a little deeper.
Ayisha: Absolutely. I’m open to it, Ryan, and thank you so much for being a wonderful host. I mean, AiFounder is doing a great job wherein all the podcast lists that I went through. It is of high quality content from a very technical resource and a very relevant resource. The quality of content is really high. I would definitely promote AiFounders.net for the same, and yes, thank you.
Ryan: Thank you so much. That’s very kind of you to say. We strive here again exactly this, you know, to have great key opinion leaders and thought leaders like yourself here to be able to provide that resource from both the technical and the practical. So, no matter where you are in the journey, you can understand and upscale and get something from every episode we do. Thank you so much for calling it. It means a lot to us here, and thank you to everybody for joining us on this enlightening journey through AI innovation. Today, we hope you’ve been inspired by the incredible stories and lessons that were shared, and remember the future is driven by pioneers like our guest Ayisha Tabbassum and the limitless possibilities of AI, so stay curious, stay innovative, and keep exploring the boundless horizons of technology. Before we sign off.Of course, a small request to our dedicated listeners. If you’ve enjoyed our podcast, we know you have today. Please take a moment. Leave a review. Subscribe to the show on your favorite platform. Tell others about it. It’s your feedback and support that helps us bring amazing guests to you like Ayisha today. Thank you again for everything. Thank you for listening, and with that, this is Ryan Davies signing off. Take care everybody.