Let me tell you the first condition of AI in India so I have a again I captured some of the discussion around it so so if you are on my slide via the scope of AI deployment have been limited so you can just I'm going to answer what is the condition at the today's time in India and in general as well so you know means we have got the confidence that okay churning the data is going to bring the next level efficiency in the business however you know the giving the quantitative view that what is the additional benefit that you have gained by the application of the AI is still a kind of a matter of debate where you know data scientist or the kind of a data analytics as a function and the business functions they debate around it because of many times the functions which are already existing yes.

So still like the leaders they have the question to themselves that what AI can do for them where to obtain the AI-powered application so still because now when the market has changed everybody is claiming that you know we got a machine learning and artificial intelligence software.

So this is creating a lot of confusion in the system that whom which platform and software to adapt to build a transformation and if you since in your business so this is another clutter that I can say that many of the players and competitors are talking about it then again if you have brought certain applications which is quite efficient in building your data churning and the predictive models but integrating with your existing systems like your customer service and the transaction system in the sales-marketing service and even in the procurement or the supply chain they are going to find a challenge and another challenge what we feel is also upgrading the existing challenge.

Where you know people have already spent like you some of you said that I have 12 years of experience in mainframe but think about a person of with 12 your experience and supply chain and you are going to say that an I know how you have done the placement of the various orders let me tell you how the predictive model is going to tell you to imagine that you go to the wine tester you say that hey let me let my equation tells you what can be you know the quantity of the wine of certain windage so still the adoption has to happen and especially in the case of the Indian organization they are still the service that I have shared with you still into the kind of a pilot phase they have not done full hog.

I think it is a transition time mostly we are experimenting with the kind of tools and technology and we are trying to get into a cross-functional discussion that whether it is helping them to make the right decision quickly and efficiently or not so it is going to take time to transform people however if I talked about the leadership willingness to bring the kind of adoption of AI and the machine learning and analytics is quite high everybody wants to transform their function by the application of analytics and the multiple tools and platform however because of the investment may be like you know you know tools and technologies are ready but is my organization or are the organization's are ready to those tools and technologies so this is the current landscape.

I will I would rate in comparison to you know the developed companies or the kind of technology-driven companies the traditional companies are yet to find their way they have to still test and validate and verify the concept that whether they want to take up this AI journey at this point or they are still waiting that what other people are fetching from this entire activity if my competitor the news is coming that okay they have done the adoption of a certain better way of targeting their customer then they are getting into a scenario that okay why not I can listen so some are followers some are early adopters but still we are into transition and especially for the Indian organization still they are required to work on what data they are looking for what kind of information they should store.

Still, it has to transform scientific majorly the information is getting captured just for the sake that you know we have captured the information so we have to have the clarity of objective that why we are trying to capture this information are they looking for self to ask such 40 questions to the customers or am I done if I ask just 15 to 20 relevant questions to the customers so this is again it has to reach on a scientific conclusion and I think it is going to take its another you know at least four or five years where we are going to be more logical on the creation of our data sources we are going to improve the accuracy and the kind of effectiveness of the data capturing system in today's time we may have the data capturing platform but you know means the majority of the data can be junk it can have errors so you need to have to improve on the quality of the data as well.

So that is the current scenario I can say that and for again the companies the examples where I just take you on a slide and I hope there is no lag in the screen and you can see the screen you know the companies were able to make the advantage maximum companies like Tesla and Google car then the law of you know your n mile transportation startups like Ola who were left they are capturing your day-to-day activity very well they can monetize the data they can now look into the better way of the customers the way the traditional businesses are still looking on so it now starts with a legacy of the business as well the businesses which are very old in nature which is existing.

Let's say pre your 90s they have to really take transformation into their systems they still have to work on multiple initiatives where they can smooth in either you know make the better data acquisition strategy the new edge companies which I can say that the startups of let's say year onwards 2000 or 2005 they have been very systematic that how they are going to monetize the data rather I can say that they are doing the business basis data as they're set pillar so they are very clear on that what they want to capture and how they want to monetize it is just a matter that they have to bring the right shape and scale of processing that data and bringing in the usage so Samir I wish I have answered your questions and how you the current adoption AI also from one of the gentlemen now how to move in to be an as an MBA.

So Alvin Lukas has passed the question so I'll just touch upon it I will go back again to the slide where I talk more on the kind of skill sets and what is going to make you today's leader in the analytics so some of you can confirm me if you can see my slide the title says that what makes you the today's analytics leader and this is again I just did some research and I got a very 11 content at the gutter and gutter has divided the skills that you need to develop areas one is your statistics so statistics that you should have a certain understanding on how you can prepare the data how you can apply those machine learning techniques a little bit knowledge on the operation research which is more on the optimization linguistics.

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