AI and ML: The Path to Modernizing Applications and Services

Join The Data Story podcast, where two veterans of the Data & Analytics industry cut through the chase and bring you the most relevant technology trends transforming the industry. James Serra and Khalil Sheikh have helped transform several Fortune 100 enterprises into data-driven enterprises. This fortnightly podcast will equip you with the best practices, tools, and frameworks available that will help you spearhead your business insights journey. Stick around for each new topic discussion and subscribe to this channel.

Guests on this episode

James Serra

Data Architecture Lead at EY

James Serra is a Data Platform Architecture Lead at EY, and previously was a big data and data warehousing solution architect at Microsoft for seven years. He is a thought leader in the use and application of Big Data and advanced analytics, including solutions involving hybrid technologies of relational and non-relational data, Hadoop, MPP, IoT,

Khalil Sheikh

Khalil Sheikh is the Executive Vice President of Saxon. Under his leadership, Saxon is transforming from an IT Staffing and services organization into a new age digital transformation partner and a strong brand. Khalil has extensive experience in the IT services industry and in turning around businesses by promoting growth and profitability.

EPISODE TRANSCRIPT

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Khalil Sheikh: [00:00:04] Good morning. I’m Khalil Sheikh, and with me is James Sara. Today we’ll be talking about how AI and ML is leveraged by multiple industries to modernize their application and services across healthcare, financial services and others. Let us get started. So at first, I want to welcome all the attendees. Thank you for joining. I want to share some facts around AI & ML before we jump into some conversational discussion with James. So start-up ambitious AI-based companies who are building new products and development are about $19.5 billion in 2021. They expected to go another 24% year after year. For the next three years. There were some numbers out of Gartner. It’s about $37 billion by 2024. So, a lot of investment is being made by the start-ups.
The second fact is that a lot of product development companies started to integrate AI as part of their product and services. Microsoft is being a champion. IBM, Google, several others. So ultimately the job market is getting pretty hot here 2022 just on product development side, we are looking about 56 to 58,000 open positions on DevOps ML, digital prototyping, social listening, product lifecycle management and advanced analytics. This is a very conservative number, but it seems to be going pretty significantly as we go up.
Another fact is the revenue from product and services company that is using AI ML is going to be around $685,000,000,000 from 519 last year. That is 24% to 25% growth across these Aim agents. So lot of interesting data that is started to emerge here. So with that, let me ask a question to James. So James, based on your experience, how has AI ML changed the way product or services are delivering value for modern enterprises?

James Serra: [00:02:30] Yeah, sure. What I’m seeing and I’ve seen for the last number of years while I was at Microsoft with customers is a huge demand for AI and machine learning. And I think those numbers are showing it’s just going to get a lot more. I see an avalanche of AI machine learning coming in the future, and that’s because companies are seeing what they can do with data. They can collect it all, centralize it all and build historical reports off that. And many companies are still in the process of centralizing all the data from all these sources, putting it together and getting better insights into our company making better business decisions. But from a historical point of view, let’s look at the trends over time, and it’s a lot of effort to get all that data together and clean it and master it, join it and have it ready for those historical reporting. But once you do all that, then you can go another step and do analytics on top of that data. So I can build machine learning models of all that data. I can train it and use it for predictive analytics in there. And why it’s a tidal wave coming is because still many companies haven’t collected all this data.
You can have all those companies along with the other ones that have collected and haven’t yet dipped into AI and Ml. And so you’re going to just see a tremendous increase in the companies that are using AI and Ml, especially as they see the art of the possible what they can do with all this predictive analytics. And that’s where a lot of the products have changed in the past few years to make it easier for predictive analytics in there. If you look at the Microsoft Realm, all their products have automated ML in there, and we can talk further about those individually.
But it’s that the basic products like a Power Bi and then they also have machine learning services for the true data scientists. But the idea is let’s make it easier for somebody using Power Bi, for example, to create these models and not have to be a data scientist with a PhD in there. Also because of that, now you’re going to see even more implementation today because they’ve made it so much easier to build those models. The barrier to entry is greatly reduced now.

Khalil Sheikh: [00:04:47] Got it. Thanks. When you’re talking about Microsoft AI ML like Conversational AI and others. Can you talk about some of their products which are really gaining momentum going forward because they started to integrate these products within their existing dynamics and SharePoint and others. Can you talk about some of those products when it comes to Microsoft?

Speaker 2: [00:05:12] Yeah. I think one of the first ones was in Power Bi. They created automated ML in there and it is really easy to go through it. You can almost say it’s too easy because you have to be careful. I always say when you’re using automated ML because they’ll go through and they’ll look at your data and they’ll suggest models and they’ll suggest the algorithms to use and within a few minutes, literally, you can have a model trained and start using it in Power Bi for that predictive analytics in there.
But I would say it’s junk in and junk out. If you don’t really understand what you’re doing, you can make things worse for yourself by coming out with a model that’s way off in its predictions. So I think you would be wise to get some basic knowledge of understanding of the different machine learning models that they have out there. So you make sure you’re pretty accurate when you’re using that. But when you look in Power BI automated ML, it’s just a wizard to go through, and it actually looks at your data and does suggest the models to use for that.
And Power Bi has that we used to be called Microsoft Flow. It’s Power Automate now has AI builder in there and they’re all pretty similar. And you just go through these Wizards and you can quickly and easily add machine learning capabilities to those various products. If I look at I was looking at like Power apps, they have all these models that you can start out with classifications, form processing, object detention, detection prediction, and the extraction and some of them you can go through and it’ll automatically detect objects for you.
And you can use that to help build your model in there. All that stuff comes from prior products that Microsoft had built out, and now we’re adding it to all these products in there and you even have in Synapse. They have the same thing. Automated ML you can go in there and you can have a table in Spark that you’ve created and you can just right click it and say, I want to build a model from this and it will go through the wizard and have it very easy to come up with this model and then they can deploy the model and then you can start calling it and using it within your applications.
A lot of this comes from the cognitive services that Microsoft has, and they’ve embedded those API calls into these products in there and do something new too. In Synapse that was announced at Ignite is database templates, which are a different name for what they call common data models. And the cool thing about this is because I can put this data in I can map any data into this common data model, and now it makes it easier to build machine learning models off top of that common data model because you know what the fields are. So they’ve only introduced one so far, and it’s called a retail one, and it allows you to do product recommendations on there. So now we’ve made it easier. You have this common data model, you map your data into it, and because it knows the field, it has this pre built retail product recommendation that you can put right on that. You can expect that to expand to dozens of models that will make it really easy to create that machine learning model and get value at it right away.
So those are just a few of the other ones that I would say are in the end user category. Basic users can use those then more of the high end. When you look at Microsoft has machine learning services, and that is more for the data scientists. And you can use Machine Learning Studio in there and you can track your models, deploy them, version them all those other things that you can either do that within that tool, and it has its own automated ML in addition to just doing things in notebooks on there, or you can do it in your own favorite modeling tool and just put the model inside of machine learning services to version it and deploy it and run it and all those things.
So they have it models now to be able to build for the basic end user all the way to the data scientist with a PhD.

Khalil Sheikh: [00:09:27] Thank you. Where do you see what industries are early adopters of Microsoft app platform, which is a low code platform versus synapse their data management platform. What industry do you see are the early adopters we started seeing in health care and BFSI. In healthcare you have language processing a lot of patient related 360 degrees to connecting all the dots, pharmacies and your hospitals, your hospices and all. So where do you see that. As for Microsoft Power apps, on one hand or synapse on the second hand, or both.

James Serra: [00:10:09] Yeah, well, you pointed out healthcare. That’s the one I’ve seen that embrace it the most, I think because there could be such a tremendous amount of savings that can happen in predictive analytics. Dozens of use cases, one of them that I dealt with healthcare company was to try to limit the remittance on there because their biggest costs.
As somebody who leaves the hospital has to go back in, so how can we reduce this? So if we collect all this data on all these patients and predict which ones are likely to be remitted, we can do. We can be proactive and do things.
Before they leave the hospital or after they leave the hospital so they don’t have to come back. I’ve seen healthcare ones that try to predict.
Health that will deteriorate for somebody, so these companies are hired to collect all this data from employers about their employees and try to predict ones that may have, say, a heart attack or predisposed to certain conditions and then try to be proactive and do things for them. Suggest a Wellness checkup for them. Because once you go to the hospital, the costs skyrocket in there. So can we do things preventive care wise for that? And because it is, I would say per user, it could be such a tremendous savings that’s where health care is where you can see it in others, like retail.
But you’re only maybe saving a few pennies here and there, it’s just a bigger bang with the health care in there and then. I would say second day is in some of the and anything with the machine that could break down. I’ve seen a lot of companies with predictive maintenance.
It could be tell me when aeroplane parts gonna fail or elevator parts gonna fail because that can be another tremendous cost savings in there. Replace a part before it breaks.
So you don’t have this downtime and this extra cost to fix things off hours. That could be a lot more expensive, so that’s become very popular and it just goes on and on with even in retail trying to predict who’s going to buy what and that could be a lot save you generate a lot of revenue if you expand that. Now to a lot of people. If you look at Amazon the way they go and they always predict other books to buy after you buy other things, that’s a machine learning model done in real time, and so that’s the other thing that I’m seeing more of is trying to do real time decision making.
Machine learning models as opposed to kind of running a report at night and predicting who can buy what and maybe sending out.
Emails to them is can we on the fly as they’re making decisions. Come up with machine learning models that’ll that’ll make them buy more or change who we focus on for our customer service and things like that. I think we’ll start to see more of the real time machine learning come into play on there, and then you even look at hospitals when they can use machine learning in real time on the tools that they’re using in operating rooms, predict that they’re going to break and replace them before that actually happens, which is a tremendous cost saving, so I can probably spot speak for 8 hours in all the use cases I’ve seen for it, but it’s important to get those use cases out because customers, once they hear those use cases they go. Oh wow, I never really thought of that. I can think of. That’s a great use case and then their mind starts going and all these light bulbs go off and they think of all these other use cases. So it’s showing them the art of possible through use cases or actual demos of what you can do with machine learning that really gets their interest and and they can see the value in it.

Khalil Sheikh: Right, right and one of the use cases we came across fraud, waste and abuse in the healthcare space, right? You know, so when an AI&ML model was put on it for two to three weeks, it showed about $90 million worth of savings. That could happen. You know, the largest category? Was you know fraud where people are doing double Billings and things like that, right? Or you know. Making some kind of a, uh, intentional spot, right? Then there’s definitely waste and abuse as well. In addition to it claim processing, right? You know when as claims are being processed, how do you make efficient claims? And also we have seen a lot of use cases of AI&ML within the healthcare industry. Same applies to financial industry as well, right? You know. So if you design the system in some reasonable format, then you get to see a lot of value day one within two to three weeks, not beyond, right, so what do you see, yeah, go ahead, go ahead.

James Serra: [00:14:45] Yeah, I was saying to add on to that, and I should have mentioned that was a good point in claims processing.
A lot of fraud that can be caught in the healthcare industry, so a whole another area of savings that could be made in there and then the financial you talked about. We’ve all probably got alerts from our bank that says, hey, this credit card Payment or credit card purchase on your card is suspicious. Yes or no. If this is you and that’s machine learning in the background on there analysing predicting that hey, there’s a good chance that this could be fraud.
Let’s take some action in real time for that, and then a larger scale. I’ve seen banks and other financial institutions look through transactions in real time for money laundering.
And some of these could be huge amounts of transactions. 10s of millions of dollars that could be happening on there, and they’re trying in real.
Time to stop that. Before then, when it gets transferred and you can even think of In banks when they’re interfacing with customers and prevent customers from getting scanned by doing wire transfers and all that, can you predict that this wire transfer that you’re going to do is? Is something fraudulent in there and warn the customer before they actually do that transformation so that it transaction is so the endless possibilities?

Khalil Sheikh: Right, right, thanks. So how do you see, like, uh?
The platforms are available, but we you still have to evangelise the company to adopt it as a, you know, an integrated AI strategy, right? You know. So when you go to these companies who are looking for reporting, analytics or even build.
Bring product and services for themselves. I’m talking about from enterprise perspective. How do how do you see that? What are the challenges that companies see to adopt it?
Is it like lack of understanding of the platform? Is it skills that is available in house? What do you see major challenges for enterprise?
Is to adopt it as a unified coherent strategy and March towards it because it’s not a one time delivery, it’s it has to be span out in in in a time frame, right?
Give us your perspective, challenges

James Serra: [00:17:00] Yeah, that’s it’s. It’s a tough question to answer in full because there are so many challenges that I see with customers trying to implement AI and mill. And the first is. I mentioned for the art of the possible, knowing what they can do. So a lot of times that Mark said when customers came.
In I would show them the icing on the cake.
Here is the reporting that you can do that they with Power BI. They may never have seen that before and they go wow. I had no idea you can do that. We were using Excel spreadsheets. And then you show them machine learning and you give them demos and sometimes your mind are completely blown, they go.
We had no idea you can do any of this stuff with it, so it’s awareness and that’s the first step is make showing them what can be done with that and that’s through just those demos and maybe appeal. See and I think the other challenge Is you need to have a lot of data to build a machine learning model to train a model, it needs more and more data and it’s as you pointed out the continuous thing. So you train That and the problem I’ve seen sometimes is they don’t train it well or they don’t update the models in there and they get bad results. And then once they have a bad experience, a lot of times they go. All that it doesn’t work. We’re not going to do it anymore. But the problem was they didn’t have the correct amount of data to train it with in there as well as they may not have had the grade. Experience that you need for building machine learning models as easy as we’ve made it, you still want to have a group of data scientists helping you out at your company. Then make sure those end users are doing it correctly and then to take it to the next level to build more sophisticated models you need somebody who really understands all the models Available to you, and that’s where there’s a big challenge, because the data scientist is probably the hottest field out there right now, and it’s hard to find data scientists. It’s really hard to find just data people in general, especially with the great leg nation or the great reshuffle that’s going on now And finding people who will understand building data warehouses because you need that in order to build the models. On top of that. So it’s dearth of talent that’s challenging to customers in there, and the data scientists people in there, it’s a very different skill set than I’ve been a DBA and developer and architect for many years.
And you can say, well, how different can be it? It’s still data, but the modelling is just a whole other different level of thinking and it’s a skill that’s hard to acquire if unless you get a lot of training in it and such and so.
Because of that, I don’t think you’re seeing as much as implementation because they need the people to go and show the art if possible and build out these solutions on there and then the last thing. I would say is just sometimes customers take too long to show value and you always try to get a quick win in there and go in there. You can collect some, just some data in a machine learning and data warehouse. You could even use power BI as the full and solution and pull the data Clean it and then build the machine learning models off that so you can quickly. Show the value of that and implement something in production. And then get people excited and it unlocks those budgets and they hire more people to go and build out these models because they see the true value of. That because in the end you want to put some ROI on that. What is this saving us and with machine learning you can really do that.
You can say look at we implemented this model in it and it increased our sales or reduced our costs or and or prevented less people from getting reinvented, and so you can put numbers on it very quickly and see the value of that and then it expands more because they have those tried and true numbers on there. But it’s happening and I think you’re going to see more and more of this. As I said, that’s avalanche of AI and machine learning coming down, coming on down the road.

Khalil Sheikh: [00:21:01] So do you see that if you want to embed AI into the organisation you got to have #1 executive sponsorship focus a team that is focused on determining the right data set.
What is the security and storage for this data set? Would be what cloud or infrastructure that you’ll adapt to? How do you integrate AI into existing systems, right? And what kind of models or outcomes of that animal model as you’re describing, has to be thought through rather than having an intellectual conversation around it and the outcome then doesn’t does mismatch? Do you see all of this is very important day, one to think through. Of course, it’s an iterative process.

James Serra: [00:21:46] Yeah, certainly that’s where and we’ve had this conversation. We get some experts in there, get a consultant company who’s done it before, who can show you the use cases, who understands the data who knows the right data to collect and the machine learning models to build on top of that, it and that could be a great quick win. By having somebody who’s done it before come in and show you the art of the possible and build something out that you can put in production in there. So then you get spread around the company and they go, Wow, this is really great stuff. We need to do more of this because to your point it’s never ends. Once you create some great reports and machine learning models and give it to the end users and you’re making their job easier, they’re saving more money.
Their sales are going up. They’re going to go. We want more and more and more of this so it becomes collect more data to build more models on there and and. Then you get into.
Dev, OPS and or MIL OPS and you want to make sure you’re tracking all this, so you need somebody who’s got the experience of deploying models and not messing things up by incorrectly deploying to the wrong place. And that’s where you have. They need somebody who knows all the tools are available for that and then how to use all those tools on there.
So it’s and it’s very hard to create that expertise from scratch in there, so you can get a consultant company come in and then maybe they can train your staff and build something along with them.
So in the end, when that consulting company is done, you have the skill set in house now and the other thing is a lot of times customers need help Justin.
Hey, who do we hire for building a data warehouse as well as an analytics platform in there? Maybe let’s have a consulting company help us in not just with building, but the roles and responsibilities and the hiring we need to do to get those right people in there so they’re in place going forward.
And that’s another big challenge, because as hard as I mentioned, for it’s hard to find those people, but so if to have a design of save a centre of excellence for analytics and reporting.
And data warehousing is really important and have some expertise who’s done that before. Who can guide you along the way so you’re not kind of making your best guess of how you should go and hire people and build solutions?

Khalil Sheikh: [00:23:49] Got it, thanks and let’s take healthcare as an example, right? How do we identify and plan AI strategies? For example, business priorities, right? How do I synergize my data pipeline because my data pipeline across my patients and services may be very distributed right? It may be internal IT.
Maybe third party or whatever, or not. How do you address this ethical concerns, right? How do I secure my data privacy for my patients, my providers and those scale gaps because what technologies I’m going to adopt, right? Whether they are Microsoft or other like tensor flows or Pytorch or others, right? How do I make sure that there is seamless implementation of it where I can keep on checking my different level? Models with an ROI mindset right? How do I engage my customers? What do you see as the biggest challenge there. The change management? Right? Because it’s not one time. Fix all kind of.
Thing, right, uh, how do I make sure that my business priorities as well as my overall deliverance happens when it comes to identifying, planning and execution of AI within an enterprise.

James Serra: [00:25:08] It’s not easy, so you can tell just from that question. The challenge in it and just a couple of things. Like from your question is things like privacy and HIPAA concerns with healthcare and there.
If you start collecting those data, you better be aware of these requirements for securing it and for making it maybe anonymous and doing other things so you’re not violating because that could be really expensive to get fines and such from that, so you need somebody who understands. That process of collecting this data and not building a model that’s Incorrectly or making people aware of data that they shouldn’t be and such and that gets into the machine learning models as well as your data warehousing. Understanding in the privacy and the security is another big win too, especially with All these companies you read almost on a weekly basis getting hacked and people seeing data they shouldn’t, which you can imagine if somebody pulls out the medical data and it gets out there. How much damage that can cost to companies, so security has become a bigger and bigger part of it.
So with customers I always say focus. There’s a lot of things you need to do, but some of the focus should be on the security and then and then. The other thing is the cleaning of the data and mastering the data, which a lot of times companies don’t put enough time and effort or and machine learning. When you talk to data scientists, they’ll say they spend too much time cleaning the data and Gartner’s had studies that say like 70% of their time is cleaning the data, not doing the machine learning models, which is what their sweet spot is in there.
So you need to put together a lot of time and effort and maybe create a centre of excellence for data governance, which is the security. And the cleaning part of that, so you’re making it much easier for the data scientists to go and build those models and not have to clean the data, because if they start building models with data that’s not cleaned in massive properly, then you can have inefficient or completely wrong models on there. So that’s another piece of it. So we can probably talk for a few hours on all the. Those things that you have to be aware of and you just have to get experienced with that. And so as a Company start with small and build something out. Get some help.
Outside of your company to and then have over time by building those solutions and and getting help, you can have that internal expert.
Piece and showcase certain things that will unlock that budget and get more people hired and expand on that team in there.
But defining that that team of of data warehousing and machine learning data scientists and how they work together is really important too, because you need to have proper communication and understanding of. What each group is prioritising and what their roles and responsibilities are. Because they have been in this industry for.
35 years, having seen a lot of projects failed, it’s because it’s not so much the technology just because of the the lack of communication.
That’s and the lack of defining roles and responsibilities and the lack of getting experts in different areas that all work together really well and and data warehousing and machine learning. It’s hard and there’s a lot of challenges.
There’s no shortcuts, despite all the latest buzzwords in there. It takes a long time to build this stuff out, and why you can get quick wins.
You have to understand to get a full out big enterprise wide system is going to take a lot of time and expertise.
And a lot of planning on there and and be prepared for a long process of, but once you do, once you develop that the the profits, the increased revenue, the cost savings on there can be tremendous and you have to do it in the in this world with all your competitors may be doing it, and so you want to. Keep ahead of them. So you need to get on this stuff. And learn it.

Khalil Sheikh: [00:29:05] Alright, thanks so James, where do you see Microsoft stand? Because Microsoft has adopted on their product development platform like Power App, AI and cognitive learning. And IBM is also another player which is using. Uh, on the on the app side product side, but also on their data management platform. So Microsoft and IBM have adopted AI in a big way.
Uh versus Amazon and Google also are chasing that down. Where do you see Microsoft future is when it comes to? Fully automated AI platforms that they are offering today versus other players in the marketplace.

James Serra: [00:29:53] Yeah, well, I’m biased since I’ve been using Microsoft since the 80s, and have I’ve seen that these newer players come around who’ve done great jobs, and I know Google is kind of known for a lot of their Machine learning AI tools that they’ve that they’ve created on there and you have to look at some of these tools and say are they enterprise wide tools or they’ve just for smaller individuals? And if they’re enterprise wide, how do we use them and not have an exorbitant payment costs for those? So that’s again where you can. You need somebody who understands all these different approaches. In the Microsoft world, so we’ve talked about.
Half a dozen different ways of building out models or what’s the best? Way and but a lot of it depends On each company. The type of data, the speed of the data and where their ultimate goals. What’s the end result? Now we say that kind of work backwards with customers is come up with some of these use cases where you can say we can really save a lot of money if we have a machine learning model. Do this and then say, OK, let’s go and build the infrastructure to get to that.
Point on there, so we have that end goal and you get the users involved, so they’re excited about it. And in the Microsoft realm.
I think you have to understand there’s those automated mill that could give you the quick wins. And so there’s a place for those you wouldn’t want to go and use those for everything, because when you want enterprise level solutions and more flexibility, more features you want to go to the high end stuff that demo services and and and there. And so it’s just understanding all those products. And then how can you?

James Serra: [00:31:23] Create models, say in in one of the automated machine learning, but transfer that to enterprise model and you could do that with Microsoft Tools.

James Serra: [26:09] You can save those models and implement them in other products and that are Enterprise Products in there. So you need to understand all of.

James Serra: [00:31:39] So you’re not losing work that’s created by these end users on there and then. Also, there’s a lot of it has to be training on there, so you need customers to know how, especially machine learning to know how to use these tools. So you give them the training and.00:31:54 Speaker 2 Make them be aware of the limitations and where should they at what point do they say oh this is getting above my understanding of machine learning models? We need to go and move this to the data scientist team to build something out because it’s this is too much for what I’m trying to do in there.
So it so it’s that understanding and. I would, I would say finally, it cost is another big thing in there. The machine learning models themselves. There’s a cost of training them, which can require a lot of compute. And then there’s the cost of running them, which is just really a function call which sounds like it could be cheap, unless you’re making millions of these calls, so a lot of times with customers you can say look, this is the best solution for you. It’s going to cost us much and they may go. Oh, that’s too much. Can we want another solution that’s not as costly?
I’m willing to sacrifice maybe features or speed on there, so that’s where you need to understand all the capabilities of products, but also understand the pricing of that because you don’t want to be surprised and get a bill that you made millions of machine learning calls and you have these this hundreds of $1000. Bill that you aren’t expecting or use all these compute to train the models on there and that whole area can be.
I mean. A lot to understand with the compute options and training these models and getting away from. Hey, let’s try to do everything on my laptop. On my desktop.
You’re very limited on the compute power you get to the cloud, and there’s unlimited compute, and they even have some.
Of machines that have virtual machines that have hundreds of cores that you can use, and maybe that’s worth it because it’ll create your model in a much quicker amount of time. And if you want to pay more. But being aware of those and then do a cost evaluation on there so you’re not surprised by that Azure bill at the end of the month.

Khalil Sheikh: [00:33:45] Thank you and one of the things when we talk to our customers right? As you also pointed out, there are lot of AI activities that they do fails. They don’t produce the outcome that they seek, or at least there’s a disconnect between the business priorities and the technical deliver.
So what can be avoided? What needs to be done in order to make sure that your business priorities and what your outcomes are matched by and understood by the technical teams?
Uhm, there’s lot of exercises that we have seen people have done. The technical team has done that doesn’t generate the business outcome, whether it’s from a data analytics perspective or even business process automation perspective, right? You know. So there’s always this catch up game. How do you resolve?

James Serra: [00:34:38] Yeah, that’s where again, I touch on it is you try to have what is the end goals think and so we’ll have brain times warming stations sometimes with customers.
What are some things you can do with AI and MIL after we help them understand what that is and with the capabilities of that?
What are some of the things you can do? They and their health care may go. Yeah, we want to lower it. Remittance Rates in there and then you start posting all these notes. In there We want to do predictive maintenance on machines. We want to find fraud and you start putting all these on a board and then you start digging in deeper, well, OK, let’s try to figure out what would be the return of investment of this. How much money can you save? Can you put some numbers on in ballpark and then you put all that and then you say OK?
Now let’s look at how hard it would be to implement each of one of these. Do we already have the data, or do we have to create the new data? So now you’re having what you’re trying to accomplish and the cost of it as far as to build and then their ROI. And once you get all those you can start prioritising them and try to find something.
Ideally that you can build quickly and has a high ROI and that gets the highest priority, so you have all that and this is done with the end users. The business users on there because they understand better than it. T the value they can get out of these machine learning models that you build and then once you have that then you go to it because it could always.
You gotta be careful because they can always see anything else. It’s just an extra burden that they have to do. And the way I always try to have it is they work with the business users and business users are part of the process, so they’re they feel like they’re involved and are getting excited about it. And they also are camping you because they want to be successful and you want them to be successful. And then you work closely. They can validate things you’re dealing to make sure they’re right. Because usually there’s a separate team from the business users. And so you have to have that communication and work together really well. And then you have some of those quick wins that come out of that. And then they can showcase that to other users to say, look what we’ve done. Look at how much money we saved and they go.
Wow, that’s incredible. I want to do something similar and then you get the snowball effect of everybody wanting to do it all, but just the challenge is to get those.
Business users involved early on and focus on them for the ROI and not any other group or it. It’s those business users in there, so a lot of the initial conversations I had with customers are the business users and doing those brainstorming sessions and showing them the art of hospital and then we get it involved later.

Khalil Sheikh: [00:37:14] But thank you, we are end of a session. So first I want to thank everyone who has attended this session.
Appreciate the time. Secondly, our section global is our data management and analytics company and we embed AI into your data platforms. In addition, we also do a lot of power product development or product engineering and embedding AI into your product engineering as well.
So please reach out to us if you have any questions or any concerns within your AI model and you want to have a safe harbour conversation to explore. What are your choices? We are cloud agnostic. We are technology agnostic. Uh, what it really means is like we work with multiple email platforms. So if you already have chosen a platform we can help you support you there as well.
Versus if you’re at an early stage and drafting a strategy, we can provide you some advisory services in a safe harbour mode to give you choice.
He says again, thank you so much. Appreciate the time and look forward to the next podcast. Take care teams appreciate it.

Khalil Sheikh: [00:38:22] Thanks everyone, thank you James.

James Serra: [00:38:25] Bye, thank you.